SAVITRIBAI PHULE PUNE UNIVESITY. University recommendation system(urs)

Transcription

1 SAVITRIBAI PHULE PUNE UNIVESITY A PROJECT REPORT ON University recommendation system(urs) SUBMITTED TOWARDS THE PARTIAL FULFILLMENT OF THE REQUIREMENTS OF BACHELOR OF ENGINEERING(Computer Engineering) BY Ankita Dhanraj Marathe (B ) Harshada Karande (B ) Kalpana Bandal (B ) Nikita Dhanraj Marathe (B ) UNDER THE GUIDANCE OF Prof. L.B.Bhagawat Department Of Computer Engineering MAEERs MAHARASHTRA INSTITUTE OF TECHNOLOGY Kothrud, Pune

2 MAHARASHTRA INSTITUTE OF TECHNOLOGY PUNE DEPARTMENT OF COMPUTER ENGINEERING C E R T I F I C A T E This is to certify that (URS) Submitted by Ankita Dhanraj Marathe (B ) Harshada Karande (B ) Kalpana Bandal (B ) Nikita Dhanraj Marathe (B ) is a bonafide work carried out by Students under the supervision of Prof.L.B.Bhagwat and it is submitted towards the partial fulfillment of the requirement of Bachelor of Engineering (Computer Engineering). Prof.L.B.Bhagwat Dept. of Computer Engg. Dr V.Y. Kulkarni Head of Comp. Dept.

3 Abstract Recommendation System is a subclass of information filtering system which takes input from users and provides User with the most suitable output to fulfill his requirements. This system is used to present a new college admission system using data mining techniques for tackling college admission prediction problems. This System uses content-based filtering to provide aspiring students (Master of Science) with the most appropriate choices of colleges based on different parameters. The system analyses the student academics, merit, background, student records and the college admission criteria. Then it predicts the likelihood of colleges the student may enter. In addition to the high prediction accuracy is an advantage, as the system can predict suitable colleges that match the students profiles and the suitable track channels through which the students are advised to enter. Keywords:Recommendation System, Recommender, Content Filtering, MinHash Algorithm

4 ACKNOWLEDGEMENT It gives us great pleasure in presenting the preliminary project report on University recommendation System.We would like to take this opportunity to thank our internal guide Prof.L.B.Bhagwat for giving us all the help and guidance we needed. We are really grateful for her kind support. Her valuable suggestions were very helpful. We are also grateful to Dr. V. Y. Kulkarni, Head of Computer Engineering Department, Maharashtra Institute of Technology, Pune for her indispensable support, suggestions.we are grateful to him for giving his valuable time and knowledge. Ankita Dhanraj Marathe Harshada Karande Kalpana Bandal Nikita Dhanraj Marathe (B.E. Computer Engineering) i

5 Contents 1 Synopsis Group Id Project Title Project Option Internal Guide Technical Keywords (As per ACM Keywords) Problem Statement Abstract Goals and Objectives Relevant mathematics associated with the Project System Description: Names of Conferences / Journals where papers can be published Review of Conference/Journal Papers supporting Project idea Plan of Project Execution TECHNICAL KEYWORDS AREA OF PROJECT TECHNICAL KEYWORDS INTRODUCTION PROJECT IDEA MOTIVATION OF THE PROJECT ii

6 3.3 LITERATURE SURVEY PROBLEM DEFINITION AND SCOPE PROBLEM STATEMENT Goals and Objectives Statement of Scope MAJOR CONSTRAINTS METHODOLOGIES OF PROBLEM SOLVING AND EFFICIENCY ISSUES OUTCOME HARDWARE RESOURCES REQUIRED SOFTWARE RESOURCES REQUIRED PROJECT PLAN PROJECT ESTIMATES Reconciled Estimates Project Resources RISK MANAGEMENT W.R.T. NP HARD ANALYSIS Risk Identification Risk Analysis Overview of Risk Mitigation, Monitoring, Management PROJECT SCHEDULE Project task set Task network Timeline Chart Team Organization Team structure Management reporting and communication iii

7 6 SOFTWARE REQUIREMENT SPECIFICATION INTRODUCTION User profiles Use cases Use case view FUNCTIONAL MODEL AND DESCRIPTION Activity Diagram Sequence Diagram Non Functional Requirements Design Constraints Software Interface Description DETAILED DESIGN DOCUMENT USING APPENDIX A AND B INTRODUCTION ARCHITECTURAL DESIGN System Architecture The Recommendation Engine SOP Analyze Engine Wishlist Engine The package diagram The deployment diagram Data Design Internal software data structure Database description COMPONENT DESIGN Class Diagram iv

8 8 PROJECT IMPLEMENTATION INTRODUCTION TOOLS AND TECHNOLOGIES USED Tools used are as follows: Technologies used are as follows: METHODOLOGIES AND ALGORITHMS DETAILS Algorithm For Recommendation Engine Algorithm For SOP Engine Algorithm For WishList Engine VERIFICATION AND VALIDATION FOR ACCEPTANCE SOFTWARE TESTING TYPES OF TESTING USED TEST CASES AND TEST RESULTS RESULTS SCREEN SHOTS DEPLOYMENT AND MAINTENANCE INSTALLATION AND UN-INSTALLATION Installation USER HELP FUTURE ENHANCEMENT AND CONCLUSION FUTURE SCOPE CONCLUSION REFERENCES 76 BIBLIOGRAPHY 77 v

9 Annexure A LABORATORY ASSIGNMENTS ON PROJECT ANAL- YSIS OF ALGORITHM DESIGN 79 Annexure B LABORATORY ASSIGNMENTS ON PROJECT QUAL- ITY AND RELIABILITY TESTING OF PROJECT DESIGN 83 Annexure C PROJECT PLANNER 89 Annexure D TERM-II PROJECT LABORATORY ASSIGNMENTS 91 Annexure E INFORMATION OF PROJECT GROUP MEMBERS 96 vi

10 List of Figures 5.1 Waterfall model Task network Use Case diagram Activity diagram-urs Application Sequence diagram-urs Sequence diagram-urs system architecture Recommendation Systems package diagram deployment diagram Class Diagram B.1 Divide and Conquer Strategy C.1 Plan of Project vii

11 List of Tables 5.1 Risk Table Use Cases A.1 IDEA Matrix viii

12 Chapter 1 Synopsis 1

13 1.1 Group Id Group ID Project Title 1.3 Project Option Inhouse Project 1.4 Internal Guide Prof. L.B. Bhagwat 1.5 Technical Keywords (As per ACM Keywords) Recommendation System Recommender Content Filtering MinHash Algorithm 1.6 Problem Statement Develop a web based application which can be installed on the system and performs following operations: Recommend Universities for MS. The application will recommend universities based on registered students profiles. Perform SOP Analysis MIT, Pune. 2 Dept. of Computer Engg.

14 The application will also perform sentiment analysis on Statement of Purpose which is required for admissions in universities. Analyze Wishlist The application will allow user to create his wish list of universities wherein he can analyze on factors that are required for getting admitted to a particular university. 1.7 Abstract Recommendation System is a subclass of information filtering system which takes input from users and provides User with the most suitable output to fulfill his requirements. This system is used to present a new college admission system using data mining techniques for tackling college admission prediction problems. This System uses content-based filtering to provide aspiring students (Master of Science) with the most appropriate choices of colleges based on different parameters. The system analyses the student academics, merit, background, student records and the college admission criteria. Then it predicts the likelihood of colleges the student may enter. In addition to the high prediction accuracy is an advantage, as the system can predict suitable colleges that match the student s profiles and the suitable track channels through which the students are advised to enter. 1.8 Goals and Objectives To increase accessibility and stimulate people to pursue an education (choose for education). To inform and help them to choose the study and environment that fits them best (choose education). MIT, Pune. 3 Dept. of Computer Engg.

15 1.9 Relevant mathematics associated with the Project System Description: Input I=set of inputs I = Login, Student Details Login =Username, Password Username =Username1, Username2,,Username Password = Password1, Password2,passwordn Student Details =Score, Financial capability, Branch Score = Toe score, GRE score Output Recommended Universities Divide And Conquer Strategies The algorithmic strategy of LSH algorithm is Divide and Conquer.To achieve recommendation, the algorithm divides data into two sets, performs procedure and give jaccard coefficient as output. This process is continued for all sets. So we divide problem into multiple sets and achieve recommendation. Classes: 1. Admin() 2. Student() 3. Recoomendation() 4. Database() MIT, Pune. 4 Dept. of Computer Engg.

16 5. University() Success Conditions: Candidate gets universities recommended successfully. Failure Conditions: 1. Candidate doesnt get recommended universities. 2. Wrong input entered. 3. Desired output is not displayed. Mathematical formulation: 1) Jaccard similarity coefficient The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets: 1.10 Names of Conferences / Journals where papers can be published IEEE/ACM Conference/Journal 1 Conferences/workshops in IITs Central Universities or SPPU Conferences IEEE/ACM Conference/Journal 2 MIT, Pune. 5 Dept. of Computer Engg.

17 1.11 Review of Conference/Journal Papers supporting Project idea Dharmendra Pathak, Sandeep Matharia and C. N. S. Murthy, NOVA: Hybrid Book Recommendation Engine, IEEE, 2012 Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of the-art and possible extensions, IEEE. G. Linden, B. Smith, and J. York, Amazon recommendations: Itemto- item collaborative filtering, IEEE, Feb Michael Hashler, Recommender Lab: A Framework for Developing and Testing Recommendation Algorithms Nov R. Bell, Y. Koren, and C. Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems KDD 07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, ACM, 2007 O. Celma and P. Herrera, A new approach to evaluating novel recommendations, RecSys 08: Proceedings of the 2008 ACM conference on Recommender systems, New York, NY, USA, ACM, C. N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, Improving recommendation lists through topic diversification: Proceedings of the 14th international conference on World Wide Web, New York, USA, ACM, Robin Burke, Hybrid Recommender Systems: Survey and Experiments, California State University, Fullerton Department of Information Systems and Decision Sciences. MIT, Pune. 6 Dept. of Computer Engg.

18 Ahu Sieg, Bamshad Mobasher, Robin Burke, Ontology-Based Collaborative Recommendation Plan of Project Execution MIT, Pune. 7 Dept. of Computer Engg.

19 Chapter 2 TECHNICAL KEYWORDS 8

20 2.1 AREA OF PROJECT The area of our project is Data Mining( Information Retrieval ). 2.2 TECHNICAL KEYWORDS A.Hardware Router or LAN for Internet Connection PC with at least 2GB RAM, Dual Core processor and WindowOS B.Architecture Client Server Architecture. Database Connectivity. Technical Keywords (As per ACM Keywords) Recommendation System Recommender Content Filtering MinHash Algorithm MIT, Pune. 9 Dept. of Computer Engg.

21 Chapter 3 INTRODUCTION 10

22 3.1 PROJECT IDEA Proposed a recommendation system which gathers information from the user and uses this information to suggest most appropriate and beneficial choices to the user. Every year large no of students have to face the task of selecting the best university for pursuing their Master Of Science (MS) degree. This system helps to recommend to the student, university which is best for them. 3.2 MOTIVATION OF THE PROJECT There are mainly three reasons which motivated us to complete this project and they are as follows: Many factors are sometimes not even considered by students in selecting their respective universities as they are sometimes not available on the university website and students end up making wrong decisions. Few of such factors are mentioned below. Geographical Location. Cost of course per semester. Weather of the place. Rank of the universities. University Staff. Information available on the University website is difficult to understand as they have complex structure. Searching for so many Universities can be time consuming as they are not available on single platform. MIT, Pune. 11 Dept. of Computer Engg.

23 3.3 LITERATURE SURVEY Recommendation system Recommendation systems were evolved as intelligent algorithms, which can generate results in the form of recommendations to users. They reduce the overhead associated with making best choices among the plenty. Now, Recommender systems can be implemented in any domain from E-commerce to network security in the form of personalized services. They provide benefit to both the consumer and the manufacturer, by suggesting items to consumers, which can t be demanded until the recommendations. Every recommender system comprises of two entities, one is user and other is item. A user can be any customer or consumer of any product or items, who get the suggestions. Input to recommendation algorithm can be a database of user and items and output obliviously will be the recommendations. As in our case, inputs consist of database of students and database of universities and the output consists of recommendation of universities. Earlier System In earlier days, there were no systems or websites that recommends the things using user s data or their behavior. But now a days there are a few systems which recommend the user or their customers their favorite products or things. But in these systems the recommendation works on only one algorithm or it can recommend user by using only one type of the data they are accessing or using their ratings to the respective products. So if system doesn t get any information from user if user is new or he/she cannot rate any product or buy any product then that time system fails to generate recommendation of products. MIT, Pune. 12 Dept. of Computer Engg.

24 Referred Papers Following papers have been referred to develop. The system Fast Locality-Sensitive Hashing by Anirban Dasgupta, Ravi Kumar,Tams Sarls They proposed a new and simple method to speed up the widely-used Euclidean realization of LSH. In that paper they were proposed two new algorithms to speed up LSH for the Euclidean distance. Their algorithms exploit the property of being able to compute Hadamard transforms fast and consequently are able to reduce the hash index construction time to O(d log d + kl). Interesting future directions include using this algorithms for applications such as deduplication, clustering, similarity joins, all-pair similarity search, etc. We have referred this paper for developing our Recommendation Engine for finding similarities between student and university attributes. NOVA: Hybrid Book Recommendation Engine Dharmendra Pathak, Sandeep Matharia and C. N. S. Murthy Chameli This paper described NOVA, which is a book recommendation engine, based on a unique Hybrid recommendation algorithm, satisfies a user by providing best and efficient books recommendations. It also presents a comparative case study of conventional recommendation algorithms to NOVAs Hybrid books recommendation algorithm. Input to the Hybrid book recommendation algorithm can be classified into two parts: a) Book Database b) User Database. We have referred the above paper for content based filtering of user and university database. Similarly input to our Recommendation Engine can be classified into two parts: a) Student Database b) University Database. Web Product Ranking Using Opinion Mining by Yin-Fu Huang and Heng Lin MIT, Pune. 13 Dept. of Computer Engg.

25 In this paper, a product ranking system using opinion mining techniques to find favorable products for users is presented. Opinion mining was a hot research topic in recent years. Opinion mining is very suitable for the applications many users to discuss a single topic when a large number of sentences and reviews are on the criticism of a single topic or when exactly identifying the correct rate of a bias direction is required to improve accuracy. Here, product ranking system considers three issues while calculating product scores: 1) product reviews, 2) product popularity, and 3) product release month. Eventually, this system would provide users to specify product features in a query, and send back the ranking results of all matched products. We have the above paper for developing our SOP analysis engine which provides user s analysis on their provided SOP. Inspiration The factors which inspired us to develop this system are listed as below:- Many factors are sometimes not even considered by students in selecting their respective universities as they are sometimes not available on the university website and students end up making wrong decisions. Few of such factors are mentioned below. Geographical Location. Cost of course per semester. Weather of the place. Rank of the universities. University Staff. Information available on the University website is difficult to understand as they have complex structure. MIT, Pune. 14 Dept. of Computer Engg.

26 Searching for so many Universities can be time consuming as they are not available on single platform. MIT, Pune. 15 Dept. of Computer Engg.

27 Chapter 4 PROBLEM DEFINITION AND SCOPE 16

28 4.1 PROBLEM STATEMENT Proposed a recommendation system which gathers information from the user and uses this information to suggest most appropriate and beneficial choices to the user. Every year large no of students have to face the task of selecting the best university for pursuing their Master Of Science (MS) degree. This system helps to recommend to the student, university which is best for them. The system works in three stages: 1.The registration system. 2.The information system. 3.The recommender system Goals and Objectives To increase accessibility and stimulate people to pursue an education (choose for education). To inform and help them to choose the study and environment that fits them best (choose education) Statement of Scope The project basically focuses on the requirements of students appearing for GRE exam and suggests the most appropriate choices of universities. It uses collaborative filtering algorithm to suggest the list of choices. This approach tries to overcome the shortcomings of other approaches. The project takes care of the database so that it gets updated every year in order to make sure that the students get the proper results. 4.2 MAJOR CONSTRAINTS 1. The live currency converter will not worked unless provided Internet access. MIT, Pune. 17 Dept. of Computer Engg.

29 2. For to work efficiently both university and student details should be provided accurately and completely. 4.3 METHODOLOGIES OF PROBLEM SOLVING AND EFFICIENCY ISSUES A problem can be solved by many different solutions. The methodologies of problem solving considers the performance parameters for each approach. Thus considering the efficiency issues. Recommendation Engine We had to gather required universities data and store it in SQLyog for future use. We modified algorithm for Jaccard s coefficient score. We had algorithm which previously was used for finding similar documents from the set of documents. We modified it for finding similarities between student and university attributes. Based on the Jaccard s Coefficient score, the user is provided with the appropriate list of universities. SOP Analysis We had to download WordNet dictionary. Then we had to import positive, negative and negation word lists in our database (SQLyog). SentiWordNet from WordNet dictionary had to be imported in our web application in Eclipse Mars, for the method extract() to get the score of the word when called by SOP Analysis. Extract() method takes help of SentiWordNet dictionary to calculate the polarity of the word. It takes words as input and returns a score as output. The negation words are calculated and then score of the remaining words in the statement is calculated by calling the extract() method.the Final score is calculated as follows:- Final Score = Negation Score + Remaining words Score. WishList MIT, Pune. 18 Dept. of Computer Engg.

30 Firstly, user s details are fetched from database. Then University details are fetched which user has added in the WishList to be compared and analyzed. One by one similar attributes of both university and user profiles are compared and the result is displayed below on the screen. 4.4 OUTCOME System proposed web application which recommend the list of universities according to user profile for their higher education. 4.5 HARDWARE RESOURCES REQUIRED 1. PC with at least 2GB RAM, Dual Core processor and WindowOS 2. Router or LAN for Internet Connection 4.6 SOFTWARE RESOURCES REQUIRED Platform: Eclipse IDE Operating System: windows 7/8 Programming Language: Java, Jsp. MIT, Pune. 19 Dept. of Computer Engg.

31 Chapter 5 PROJECT PLAN 20

32 5.1 PROJECT ESTIMATES In The Waterfall approach, the whole process of software development is divided into separate phases. In Waterfall model, typically, the outcome of one phase acts as the input for the next phase sequentially. Figure 5.1: Waterfall model The sequential phases in Waterfall model are: Requirement Gathering and analysis: All possible requirements of the system to be developed are captured in this phase and documented in a requirement specification document. Hardware Requirements: * PC with at least 2GB RAM, Dual Core processor and WindowOS. * Router or LAN for Internet Connection MIT, Pune. 21 Dept. of Computer Engg.

33 Software Requirements: * Platform: Eclipse IDE * Operating System: windows 7/8 * Programming Language: Java, Jsp. System Design: The requirement specifications from first phase are studied in this phase and system design is prepared. System Design helps in specifying hardware and system requirements and also helps in defining overall system architecture. The App will be developed using Eclipse platform. The app is created using Java. Implementation: With inputs from system design, the system is first developed in small programs called units, which are integrated in the next phase. Each unit is developed and tested for its functionality which is referred to as Unit Testing. The following units were serially developed: 1. Login module. 2. Registration module. 3. Recommendation Manager. 4. Sop Analysis. 5. Wishlist analysis. Integration and Testing: All the units developed in the implementation phase are integrated into a system after testing of each unit. Post integration the entire system is tested for any faults and failures. MIT, Pune. 22 Dept. of Computer Engg.

34 Deployment of system: Once the functional and non functional testing is done, the product is deployed in the customer environment or released into the market. User can use this web application for their problem solution Maintenance: There are some issues which come up in the client environment. To fix those issues patches are released. Also to enhance the product some better versions are released. Waterfall Model Application Every software developed is different and requires a suitable SDLC approach to be followed based on the internal and external factors. Some situations where the use of Waterfall model is most appropriate are: Requirements are very well documented, clear and fixed. Product definition is stable. Technology is understood and is not dynamic. There are no ambiguous requirements. Ample resources with required expertise are available to support the product. The project is short Reconciled Estimates Cost Estimate The cost of creating a Website would be broken down as :- A basic must in creating a website is buying your own domain name. The prices for domains vary from registrar to registrar, and are around USD 10 or so per year. MIT, Pune. 23 Dept. of Computer Engg.

35 This is a monthly fee that you have to pay to the web host. Prices vary from web host to web host but are usually around USD 10 per month if your website is new and doesn t have much traffic or data. Time Estimate Time Estimates: 8 months Project Resources Hardware Resources: PC with at least 2GB RAM, Dual Core processor and WindowOS. Router or LAN for Internet Connection. Software Resources: Platform: Eclipse IDE (Eclipse Mars ) and SQLyog. Operating System: windows 7/8 Programming Language: Java, Jsp. Human Resources: The team includes four members who are involved in making the project. Our internal guide has helped us to solve our doubts and remove the errors in the project. 5.2 RISK MANAGEMENT W.R.T. NP HARD ANALYSIS Risk management is concerned with identifying risks and drawing up plans to minimize their effects on a project. A risk is a probability that some adverse circumstance will occur. MIT, Pune. 24 Dept. of Computer Engg.

36 5.2.1 Risk Identification Risk identification is the process of determining risks that could potentially prevent the program, enterprise, or investment from achieving its objectives. It includes documenting and communicating the concern. The major types of risks are:- Project risks affect schedule or resources Performance risk affects the quality or performance of the software being developed. Technology risk affects the technical aspects of the projects. For e.g. Database, function re-usability. The main areas of the project that involves significant risks are: Performance Risks It is vital that a project database should be updated regularly on the basis of new added universities and their true database. It is important to collect new student database to enhance project performance. Project Risks Project involves a branch of computer science (machine learning) that has captivated computer scientists for decades. Making a machine enough powerful to learn from a behavior and behave intelligently has been a challenge for scientists since decades. Our major concern in the project is to provide extremely relevant and filtered university results. It is a difficult task to collect a university database. Branches such as Machine learning are in their infancy. Irrelevant results of universities to users would not only lead to improper network usage but also waste user s time and money. MIT, Pune. 25 Dept. of Computer Engg.

37 5.2.2 Risk Analysis The risks for the Project can be analyzed within the constraints of time and quality ID Risk Description Probability Impact Quality Overall 1 Tampering or stealing the student profile Low High High 2 Internet Connectivity Low Low Low 3 University database updation High High High 4 Handling of database by different Admin Low High High Table 5.1: Risk Table Overview of Risk Mitigation, Monitoring, Management Following are the details for each risk. Risk ID 1 Risk Description Tampering or stealing the student profile will lead to identity theft and system hacking. Category Database Environment. Source Platform: SQLyog Probability Low Impact High Response Aggravate Strategy We have protected the student password in the database by using hash key generation. Risk Status Not occurred Risk ID 2 Risk Description Internet dis-connectivity can lead to user not able to access the website. Category Requirements Source Router or LAN cable Probability Low Impact Low Response Aggravate Strategy Use of good quality of Router or LAN cable. Risk Status Identified MIT, Pune. 26 Dept. of Computer Engg.

38 Risk ID 3 Risk Description University database updation. Category Requirements Source This was identified during early development. Probability High Impact High Response Accept Strategy We have made our System Live. Risk Status Identified Risk ID 4 Risk Description Handling of database by different Admin leading to data inconsistence. Category Development Source This was identified during early development. Probability Low Impact High Response Accept Strategy We have only one Admin to our system so as to maintain consistence in the database. Risk Status Identified 5.3 PROJECT SCHEDULE Project scheduling is crucial and important to complete project successfully. Implementing an effective schedule keeps projects on track by setting time frame and budget parameters, and helps ensure qualities of the finished project. Understanding the importance of project scheduling and its role in developing projects can help to complete tasks more efficiently Project task set Major Tasks in the Project stages are: Task 1: Selecting the project domain & the project. Task 2: Project Planning. Task 3: Study of tools and technology. Task 4: Designing of module 1 (Recommendation Engine). MIT, Pune. 27 Dept. of Computer Engg.

39 Task 5: Designing of module 2 (SOP Analysis). Task 6: Designing of module 3 (WishList). Task 7: Implementation and Coding. Task 8: Testing. Task 9: Deployment Task network Figure 5.2: Task network MIT, Pune. 28 Dept. of Computer Engg.

40 5.3.3 Timeline Chart See Table below MIT, Pune. 29 Dept. of Computer Engg.

41 5.4 Team Organization Team structure We have completed the journal, report and partial implementation of our project. The project was equally divided and distributed among the team members Management reporting and communication We had loads of project meetings to complete all of our tasks. We also meet our project guide frequently to discuss about our problems and take her guidance to solve problems. s, phone calls and messages were also used to communicate within the team. MIT, Pune. 30 Dept. of Computer Engg.

42 Chapter 6 SOFTWARE REQUIREMENT SPECIFICATION 31

43 6.1 INTRODUCTION People make decisions every day. There are too many choices and a little time to explore them all. Recommendation systems help people make decisions in these complex information spaces. Recommendation systems are a type of information filtering that presents lists of items (films, songs, books, videos, images, products, web pages...) which are likely of user interest. Amazon, Last.fm, Ulike, ilike, Netflix, Pandora are the most popular recommender systems all over the world. Simply they compare user interest acquired from his/her profile with some reference characteristics and predict the rating that the user would give. Those characteristics maybe from the item information(content-based approach) or the user s social profile (collaborative filtering approach). Definition Of Recommendation Systems Recommendation systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user. The suggestions relate to various decision-making processes, such as what items to buy, what music to listen to or what online news to read. Item is a general term used to denote what the system recommends to users. Item can be a song, a book, a product to buy or as in our case a university to a student. Project Scope The project basically focuses on the requirements of students appearing for GRE exam and suggests the most appropriate choices of universities. It uses collaborative filtering algorithm to suggest the list of choices. This approach tries to overcome the shortcomings of other approaches. The project takes care of the database so that it gets updated every year in order to make sure that the students get the proper results. The requirements and rational for project scope are as followed. Functional Requirements MIT, Pune. 32 Dept. of Computer Engg.

44 1. Database connectivity for linking the front end to the back end. 2. Usage of Java Servlet Pages to display the user interface. Scenario: 1. Go to the application. 2. Connect to database. 3. log into system if already registered. 4. Whenever a login into system the it will shows the recommendation of university based on their profile User profiles Developer : They create the application and analyze user profile. They should be knowledgeable about end users. User : User should have the They should be knowledgeable about the application Use cases MIT, Pune. 33 Dept. of Computer Engg.

45 Sr. Use Cases Description Actors Assumptions No. 1 Use case 1 candidate registers itself, then creates profile and updates the profile candidate 2 Use Case 2 DB manager saves DB manager the database and retrieves the database 3 Use Case 3 Server recommends the universities, it analysis the wishlist and SOP 4 Use Case 3 The admin adds and updates the university detailes in the database if the candidate is already registered then it can login directly Database is filled by the user Server Database is accurately managed by the DB manager Admin Table 6.1: Use Cases University database is already available Use case view Use case diagram shows the overall functionality of the system. In our system there are three use case diagrams. MIT, Pune. 34 Dept. of Computer Engg.

46 Figure 6.1: Use Case diagram 6.2 FUNCTIONAL MODEL AND DESCRIPTION 1. Recommendation engine For proposing the recommendation the users information in the database is compared with the available dataset of universities. Based on the information he has provided, the system will scale his different attributes on multidimensional graph and the universities which have parameters closest to the parameters of the particular student will be suggested. 2. SOP Analysis Engine This part of the system is used for sentiment analysis of the user SOP. Many sub tasks will be performed in this engine. The tasks will be as follows:- a) Tokens formation. b) Removal of stop-words. MIT, Pune. 35 Dept. of Computer Engg.

47 Sometimes, some extremely common words which would appear to be of little value in helping select documents matching a user need are excluded from the vocabulary entirely. These words are called stop words. The general strategy for determining a stop list is to sort the terms by collection frequency (the total number of times each term appears in the document collection), and then to take the most frequent terms, often hand-filtered for their semantic content relative to the domain of the documents being indexed, as a stop list, the members of which are then discarded during indexing. c) Removal of stem-words. Then all the remaining words will be placed in the array. d) These words in the array will be compared with the list of positive and negative words contained in our database. And a score will be computed. Calculating the Opinion Strength: The polarity strength of opinion words could be calculated as follows. 3. Wishlist Engine The engine will allow user to create his wish list of universities wherein he can analyze on factors that are required for getting admitted to a particular university. MIT, Pune. 36 Dept. of Computer Engg.

48 6.2.1 Activity Diagram In activity diagram the overall flow of the system is shown. It contains the following sequence of action. Figure 6.2: Activity diagram-urs Application MIT, Pune. 37 Dept. of Computer Engg.

49 6.2.2 Sequence Diagram A Sequence diagram is an interaction diagram that shows how processes operate with one another and in what order. It contains the following sequence of action. Figure 6.3: Sequence diagram-urs Figure 6.4: Sequence diagram-urs MIT, Pune. 38 Dept. of Computer Engg.

50 6.2.3 Non Functional Requirements Interface Requirements: Your app s user interface is everything that the user can see and interact with. Android provides a variety of pre-built UI components such as structured layout objects and UI controls that allow you to build the graphical user interface for your app. Android also provides other UI modules for special interfaces such as dialogs, notifications, and menus. Performance Requirements: Choosing the right algorithms and data structures should always be the priority. do not allocate memory if you can avoid it To ensure your app performs well across a wide variety of devices, ensure your code is efficient at all levels and aggressively optimize your performance. Avoid Creating Unnecessary Objects Object creation is never free. A generational garbage collector with perthread allocation pools for temporary objects can make allocation cheaper, but allocating memory is always more expensive than not allocating memory. Prefer Static Over Virtual If you do not need to access an object s fields, make your method static. Invocations will be about 15%-20% faster. It s also good practice, because you can tell from the method signature that calling the method can t alter the object s state. MIT, Pune. 39 Dept. of Computer Engg.

51 Consider Package Instead of Private Access with Private Inner Classes Software quality attributes: Availability * It is the proportion of time a system is in a functioning condition. * Availability of a system is typically measured as a factor of its reliability as reliability increases, so does availability. * Reliability needs to be evaluated and improved related to both availability and the cost of ownership (due to cost of spare parts, maintenance man-hours, transport costs, storage cost, part obsolete risks etc.). * Fault tree analysis and related software are developed to calculate (analytic or by simulation) availability of a system or a functional failure condition within a system. * In our case, our web application is available at all times except at the time of currency converter is being used. * Portability Portability in high-level computer programming is the usability of the same software in different environments. The prerequisites for portability is the generalized abstraction between the application logic and system interfaces. Our application is only supported on Web application. * Reusability In computer science and software engineering, reusability is the use of existing assets in some form within the software product MIT, Pune. 40 Dept. of Computer Engg.

52 development process. Assets are products and by-products of the software development life cycle and include code, software components, test suites, designs and documentation. Subroutines or functions are the simplest form of reuse. A chunk of code is regularly organized using modules or namespaces into layers. The recommendation engine and wishlist module are reusable for other applications. * Scalability An algorithm, design, networking protocol, program, or other system is said to scale if it is suitably efficient and practical when applied to large situations (e.g. a large input data set, a large number of outputs or users, or a large number of participating nodes in the case of a distributed system). We can scale our application to mobile platform and the database ca also be deployed on cloud to scale the storage. Testability * Software testability is the degree to which a software artifact (i.e. a software system, software module, requirements- or design document) supports testing in a given test context. * It is an extrinsic property which results from interdependency of the software to be tested and the test goals, test methods used, and test resources (i.e., the test context). * Our app is being tested for the following things: Sop module has been tested for different sop. Wishlist module has been for different universities. MIT, Pune. 41 Dept. of Computer Engg.

53 The project GUI is tested. Usability * In Software engineering, usability is the degree to which a software can be used by specified consumers to achieve quantified objectives with effectiveness, efficiency, and satisfaction in a quantified context of use. * It includes methods of measuring usability, such as needs analysis and the study of the principles behind an object s perceived efficiency or elegance. * The primary notion of usability is: 1. More efficient to use, takes less time to accomplish a particular task. 2. Easier to operate application, no user manual is required to use the application. 3. The application is user friendly Design Constraints 1. The apache tomcat server version should be grater than The currency converter requires Internet connection at all times. 3. If the database of the university is not constantly updated then the user might not get the desired results. 4. The project does not counsel the students about the universities, it is only a recommender Software Interface Description System provides web interface to the user. MIT, Pune. 42 Dept. of Computer Engg.

54 The software interface(web page) provides various options and buttons such as connection, selection of appropriate framework such as mysql connectivity. MIT, Pune. 43 Dept. of Computer Engg.

55 Chapter 7 DETAILED DESIGN DOCUMENT USING APPENDIX A AND B 44

56 7.1 INTRODUCTION Detailed design document is a written description of a software product, that a software designer writes in order to give a software development team overall guidance to the architecture of the software project. This chapter would contain the following contents The architecture design uses information flowing characteristics, and maps them into the program structure. The transformation mapping method is applied to exhibit distinct boundaries between incoming and outgoing data. The data flow diagrams allocate control input, processing and output along three separate modules. The data design describes structures that reside within the software. Attributes and relationships between data objects dictate the choice of data structures. The component design describes each component of the diagrams.the diagrams would contain class diagram, interaction diagram.it would also contain the module processing i.e. the pseudo-code algorithm. 7.2 ARCHITECTURAL DESIGN System Architecture A system architecture or systems architecture is the conceptual model that defines the structure, behavior, and more views of a system. An architecture description is a formal description and representation of a system, organized in a way that supports reasoning about the structures and behaviors of the system The system is user-centric. It focuses on providing user (in our case the student appearing for the exam) the most appropriate choice of universities. The recommendation engine acts as the brain of the recommendation system along with the MIT, Pune. 45 Dept. of Computer Engg.

57 Figure 7.1: system architecture algorithm used to support the system. The main components of the system are: The Recommendation Engine This engine acts as the brain of the recommendation system. It maps the relevant attributes from the university database to the student database. Using the MinHash algorithm it calculates the Jaccards coefficient and based on this result it recommends the universities to the students. Input Set I : Fetched data Output Set O : recommendation Function F : recommend () Axioms : best possible result. MIT, Pune. 46 Dept. of Computer Engg.

58 Figure 7.2: Recommendation Systems SOP Analyze Engine This part of the system is used for sentiment analysis of the user SOP. This part of the system is used for sentiment analysis of the user SOP using dictionary based approach. For SOP Analysis WordNet dictionary is being used. Knowledge base contains WordNet dictionary for domain independent polarity classification for positive, negative and neutral opinion. Sentiment words are generally categorized into negative and negative categories. For this purpose we extract the semantic score of each opinion word using SentiWordNet dictionary containing the semantic score of the words Wishlist Engine The engine will allow user to create his wish list of universities wherein he can analyze on factors that are required for getting admitted to a particular university. MIT, Pune. 47 Dept. of Computer Engg.

59 7.2.5 The package diagram Figure 7.3: package diagram MIT, Pune. 48 Dept. of Computer Engg.

60 7.2.6 The deployment diagram Figure 7.4: deployment diagram 7.3 Data Design Internal software data structure Internal software data structure are the data structures passed that are passed amongst the components of the software In our project the database tables are the internal software that are passed to the software components are as follows:- The student database table is passed as the student profile to the recommendation module for the recommendation process the university database table is passed as the university profile to the recommendation module for the recommendation process to calculate the Jacquard s coefficient for the recommendation of the universities according to the student profile MIT, Pune. 49 Dept. of Computer Engg.

61 7.3.2 Database description UNIVERSITY DATABASE STUDENT DATABASE MIT, Pune. 50 Dept. of Computer Engg.

62 7.4 COMPONENT DESIGN Component-level design occurs after the first iteration of the architectural design It strives to create a design model from the analysis and architectural models The translation can open the door to subtle errors that are difficult to find and correct later Effective programmers should not waste their time debugging they should not introduce bugs to start with. Edsgar Dijkstra A component-level design can be represented using some intermediate representation (e.g. graphical, tabular, or text-based) that can be translated into source code The design of data structures, interfaces, and algorithms should conform to well-established guidelines to help us avoid the introduction of errors Class Diagram Class diagram are the most common diagram found in modeling object oriented systems. A class diagram shows set of classes interface and collaboration and their relationships. MIT, Pune. 51 Dept. of Computer Engg.

63 Figure 7.5: Class Diagram MIT, Pune. 52 Dept. of Computer Engg.

64 Chapter 8 PROJECT IMPLEMENTATION 53

65 8.1 INTRODUCTION The system is user-centric. It focuses on providing user the most appropriate choice of universities. The recommendation engine acts as the brain of the recommendation system along with the algorithm used to support the system. The main components of the system are: The extraction engine The recommendation engine The database The client The Extraction Engine This engine can be considered as the first module of the project. There are two ways to extract the required data from a particular website. One way is to extract the data manually and the other way is to use a crawler. The extraction of data manually can prove very tedious. So, we are using a certain extraction process. The Recommendation Engine This engine acts as the brain of the recommendation system. It maps the relevant attributes from the university database to the student database. Using the LSH algorithm it calculates the Jaccards coefficient and based on this result it recommends the universities to the students. The Database This part of the system holds significance because it stores all the information that is required for processing when the recommendation engine plays its part. This data which is stored in the database is collected by the extraction engine and is provided to the recommendation engine. Two databases are maintained. One is student database and the other is the university database. MIT, Pune. 54 Dept. of Computer Engg.

66 The Client The client side consists of the user interface, the browser and the user. The user interface helps the user to properly communicate with the system. 8.2 TOOLS AND TECHNOLOGIES USED Tools used are as follows: 1. Eclipse Mars SQLyog Tool. Eclipse Mars 4.5 Eclipse is an integrated development environment (IDE) used in computer programming. It contains a base workspace and an extensible plug-in system for customizing the environment. Eclipse is written mostly in Java and its primary use is for developing Java applications, but it may also be used to develop applications in other programming languages through the use of plugins. SQLyog SQLyog is a GUI tool for the RDBMS MySQL. It is developed by Webyog, Inc. based out of Bangalore, India and Santa Clara, California. SQLyog is being used by more than 30,000 customers worldwide. SQLyog is available in free as well as paid versions. The free version is known as Community Edition and is available as an open source project at Google Code. Paid versions are sold as Professional, Enterprise and Ultimate Editions. Uses MySQL C API to communicate with MySQL servers. No dependencies on database abstraction layers (like ODBC/JDBC). SQLyog works on the Windows platform starting from Windows XP/Windows 2003 to Windows Technologies used are as follows: Apache Tomcat MIT, Pune. 55 Dept. of Computer Engg.

67 Java Develpment Kit 7(JDK) JAVA. Apache Tomcat Apache Tomcat, often referred to as Tomcat, is an open-source web server developed by the Apache Software Foundation (ASF). Tomcat implements several Java EE specifications including Java Servlet, JavaServer Pages (JSP), Java EL, and WebSocket, and provides a pure Java HTTP web server environment in which Java code can run. Tomcat is developed and maintained by an open community of developers under the auspices of the Apache Software Foundation, released under the Apache License 2.0 license, and is open-source software. Java Development Kit 7 (JDK) Java Development Kit (JDK) is an implementation of either one of the Java SE, Java EE or Java ME platforms released by Oracle Corporation in the form of a binary product aimed at Java developers on Solaris, Linux, Mac OS X or Windows. The JDK includes a private JVM and a few other resources to finish the development of a Java Application.Since the introduction of the Java platform, it has been by far the most widely used Software Development Kit (SDK). JAVA Java is a general-purpose computer programming language that is concurrent, classbased, object-oriented,and specifically designed to have as few implementation dependencies as possible. It is intended to let application developers write once, run anywhere (WORA), meaning that compiled Java code can run on all platforms that support Java without the need for recompilation. MIT, Pune. 56 Dept. of Computer Engg.

68 8.3 METHODOLOGIES AND ALGORITHMS DE- TAILS Algorithm For Recommendation Engine. We will be using MinHash Algorithm. In computer science, MinHash (or the minwise independent permutations locality sensitive hashing scheme) is a technique for quickly estimating how similar two sets are. The Algorithm for Recommendation Engine is:- 1. The Recommendation is done on the basis of the mapping between the attributes of user profile and the attributes of university profile stored in the database to compute the Jaccard s Coefficient score. 2. Based on the Jaccard s Coefficient score, the user is provided with the appropriate list of universities. 3. First we start by placing user s attributes in set A and set B consisting of university attribute. 4. Then we need to merge set A and set B into one single list. 5. Next step is generate a hash array using self defined hash function. The hash array will have size equal to the size of merge list. 6. A matrix is generated whose size of row is equal to number of sets and the size of column is equal to the size of merge list. Initial the values in the matrix will be a MAX integer. 7. Now filling of first row of the matrix is done by comparing the first element of merge list with the elements of the set A. We are performing content based comparison. If the first element of merge list matches with any element of set A then the hash value corresponding to the first element of merge list is placed MIT, Pune. 57 Dept. of Computer Engg.

69 in the place of first MAX value of first row. Then for filling second row of the matrix, again the merge list elements and set B elements are compared and corresponding hash values are placed if a match is found. 8. The next step is to compare column-wise hash values if two same hash values are found then the match counter is incremented. 9. Finally Jaccard s coefficient score is calculated as :- JC = the value of match counter / by the size of the column in matrix Algorithm For SOP Engine. This part of the system is used for sentiment analysis of user s SOP using dictionary based approach. For SOP analysis WordNet dictionary is being used. The Algorithm for SOP Analysis is:- 1. User is required to enter his/her SOP in the text box provided. 2. Identify Negation words from each statement of given SOP. 3. After Identifying negation word, score is calculated. The scores are assigned for the following three cases:- If word placed in the right side of identified negation word is negative like NOT Bad then the score is positive. Here, Not is identified negation word and bad is word placed in the right side of the negation word. A method called extract() is called to get the score of the word. It takes help of SentiWordNet dictionary to calculate the polarity of the word. The extract() method takes words as input and returns a score as output. If word placed in right side of the identified negation word is positive then the score is negative. Like Not Good, the score is positive. Again the extract() will be called to get the score of the word. MIT, Pune. 58 Dept. of Computer Engg.

70 If both the above cases fail then by default score is taken as negative. 4. The negation words are removed from the statement. 5. The score of the remaining words in the statement is calculated by calling the extract() method. 6. The Final score is calculated as follows:- Final Score = Negation Score + Remaining words Score Algorithm For WishList Engine. This part of the system is used for analyzing on factors that the user s profile is lacking, or stopping him/her from getting an admit from desired university. This system will help user improve his/her profile. The Algorithm for SOP Analysis is:- 1. Firstly, user s details are fetched from database. 2. Then University details are fetched which user has added in the WishList to be compared and analyzed. 3. One by one similar attributes of both university and user profiles are compared and the result is displayed below. 8.4 VERIFICATION AND VALIDATION FOR AC- CEPTANCE In software project management, software testing, and software engineering, verification and validation (V&V) is the process of checking that a software system meets specifications and that it fulfills its intended purpose. It may also be referred to as software quality control. It is normally the responsibility of software testers as part of the software development life cycle. MIT, Pune. 59 Dept. of Computer Engg.

71 Validation checks that the product design satisfies or fits the intended use (highlevel checking), i.e. the software meets the user requirements. This is done through dynamic testing and other forms of review. Verification and validation are not the same thing, although they are often confused. Boehmsuccinctly expressed the difference between Verification: Are we building the product right? Validation: Are we building the right product? According to the Capability Maturity Model Software Verification: The process of evaluating software to determine whether the products of a given development phase satisfy the conditions imposed at the start of that phase. Software Validation: The process of evaluating software during or at the end of the development process to determine whether it satisfies specified requirements. We have validate our system as it meets all the user requirements and dynamic testings have been performed. We have also done software verification as our software satisfies all the conditions imposed at the start of phases. MIT, Pune. 60 Dept. of Computer Engg.

72 Chapter 9 SOFTWARE TESTING 61

73 9.1 TYPES OF TESTING USED Since proposed application, we have done manual testing.there are different stages for manual testing such as unit testing, integration testing,system testing, and user acceptance testing. 1. Unit testing In computer programming, unit testing is a software testing method by which individual units of source code, sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures, are tested to determine whether they are fit for use. In our program, we have units such as application divide into multiple modules or units such as Student login and registration,sop unit,recommendation unit which are tested by individual unit testing strategy. 2. Integration testing Integration testing (sometimes called integration and testing, abbreviated I&T) is the phase in software testing in which individual software modules are combined and tested as a group. It occurs after unit testing and before validation testing. In proposed system we have integrated the login and registration module to be tested as integration testing. Also system integrated the modules which the functionality of each parameter of recommendation module,sop,and wishlist module. 3. System testing System testing of software or hardware is testing conducted on a complete, integrated system to evaluate the system s compliance with its specified requirements. System testing falls within the scope of black-box testing, and as such, should require no knowledge of the inner design of the code or logic. In proposed application successfully performed the system testing in which MIT, Pune. 62 Dept. of Computer Engg.

74 the system was tested as a whole. 4. User Acceptance testing User acceptance testing (UAT) is the last phase of the software testing process. During UAT, actual software users test the software to make sure it can handle required tasks in real-world scenarios, according to specifications. We have successfully completed this testing. 9.2 TEST CASES AND TEST RESULTS Test Test module Test input Expected Result Actual Result Status Case id 1 Launch the Ap- URL for applica- The application The app starts pass plication. 2 Login or Registration. 3 Create university database. 4 Recommendation. tion Fill profile information university database parameter such as name,state,fees etc. student profile and university database database should be Successfully System should Shows recommended universities. starts when run. Filled up information such as name,marks, gender, etc when run. all information created properly and form submitted successfully. stored database Successfully at back end. list of university and their information. pass pass pass The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub-assemblies, assemblies and/or a finished product. It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. Test Cases :- Set of inputs, execution conditions and expected results carried out for some objec- MIT, Pune. 63 Dept. of Computer Engg.

75 tives: Login Testing GUI Testing :- GUI means graphical user interface to test the friendliness of that system. Here we testing for the object properties and if the screens are user under stable or not. MIT, Pune. 64 Dept. of Computer Engg.

76 MIT, Pune. 65 Dept. of Computer Engg.

77 Chapter 10 RESULTS 66

78 PROJECT 10.1 SCREEN SHOTS MIT, Pune. 67 Dept. of Computer Engg.

79 MIT, Pune. 68 Dept. of Computer Engg.

80 MIT, Pune. 69 Dept. of Computer Engg.

81 MIT, Pune. 70 Dept. of Computer Engg.

82 MIT, Pune. 71 Dept. of Computer Engg.

83 Chapter 11 DEPLOYMENT AND MAINTENANCE 72

84 11.1 INSTALLATION AND UN-INSTALLATION Installation 1. Install Java JDK Install Eclipse JAVA EE IDE. 3. Install Sqlyog. 4. Import the Existing Project. Goto file New Import. Select the project form workspace. It Display the Project workspace on Navigator. 5. Add Server. Right click new server. Select Tomcat v7.0 sever Finish. 6. Finally click on run button. MIT, Pune. 73 Dept. of Computer Engg.

85 11.2 USER HELP Our system is quite simple.we have made it very user-friendly.the user can easily navigate through the pages of the website. MIT, Pune. 74 Dept. of Computer Engg.

86 Chapter 12 FUTURE ENHANCEMENT AND CONCLUSION 75

87 12.1 FUTURE SCOPE Similar type of system can be implemented for fields like management, marketing, finance etc. Mobile application can be developed in the future for the same system. Cloud based approach for storing data can be used to improve the overall storage and processing of data. Students can be suggested to publish papers in various research areas in order to help them improve their profile CONCLUSION We have implemented a recommendation system based on MinHash algorithm. This system is user-centric. It provides the students with the proper choices of universities. Based on the calculation of Jaccards coefficient the system generates the results. MIT, Pune. 76 Dept. of Computer Engg.

88 Chapter 13 REFERENCES 77

89 Bibliography [1] Dharmendra Pathak, Sandeep Matharia and C. N. S. Murthy, NOVA: Hybrid Book Recommendation Engine, IEEE, 2012 [2] G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of the-art and possible extensions, IEEE. [3] G. Linden, B. Smith, and J. York, Amazon recommendations: Itemto- item collaborative filtering, IEEE, Feb [4] Michael Hashler, Recommender Lab: A Framework for Developing and Testing Recommendation Algorithms Nov [5] R. Bell, Y. Koren, and C. Volinsky, Modeling relationships at multiple scales to improve accuracy of large recommender systems KDD 07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA, ACM, 2007 [6] O. Celma and P. Herrera, A new approach to evaluating novel recommendations, RecSys 08: Proceedings of the 2008 ACM conference on Recommender systems, New York, NY, USA, ACM, [7] C. N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, Improving recommendation lists through topic diversification: Proceedings of the 14th international conference on World Wide Web, New York, USA, ACM,

90 [8] Robin Burke, Hybrid Recommender Systems: Survey and Experiments, California State University, Fullerton Department of Information Systems and Decision Sciences. [9] Ahu Sieg, Bamshad Mobasher, Robin Burke, Ontology-Based Collaborative Recommendation. MIT, Pune. 79 Dept. of Computer Engg.

91 Annexure A LABORATORY ASSIGNMENTS ON PROJECT ANALYSIS OF ALGORITHM DESIGN 80

92 To develop the problem under consideration and justify feasibilty using concepts of knowledge canvas and IDEA Matrix. IDEA Matrix is represented in the following form. Knowledge canvas represents about identification of opportunity for product. Feasibility is represented w.r.t. business perspective. I D E A Increase Drive Educate Accelerate Improve Deliver Evaluate Associate Ignore Decrease Eliminate Avoid Table A.1: IDEA Matrix Project problem statement feasibility assessment using NP-Hard, NP- Complete or satisfy ability issues using modern algebra and/or relevant mathematical models. Polynomial(P) and Non-Polynomial(NP) Polynomial Time: The class of polynomial- time solvable problems P includes all the sets in which membership may be decided by an algorithm whose running time is bounded by a polynomial. Example: * Linear Time-O(n) * Quadratic Time-O(n2) * Exponential Time-O(2n) * Logarithmic Time-O(logn) Non-Polynomial Time: The class non-deterministic polynomially acceptable problems, NP contains all sets in which membership can be verified in polynomial time. All problems from P is also NP. Examples: MIT, Pune. 81 Dept. of Computer Engg.

93 * Decision problem version of the integer factorisation problem * Graph isomorphism problem * A decision version of travelling salesman problem * The Boolean satisfiability problem Types of NP type problems: * NP-hard: In computational complexity theory, is a class of problems that are, informally, at least as hard as the hardest problems in NP. More precisely, a problem H is NP-hard when every problem L in NP can be reduced in polynomial time to H.As a consequence, finding a polynomial algorithm to solve any NP-hard problem would give polynomial algorithms for all the problems in NP, which is unlikely as many of them are considered hard. * NP-complete: In computational complexity theory, a decision problem is NP-complete when it is both in NP and NP-hard. The set of NP-complete problems is often denoted by NP-C or NPC. The abbreviation NP refers to nondeterministic polynomial time. class. Thus from the above definition our application of is P (Polynomial) Input x,output y, y=f(x) I:Set of Inputs={I1,I2,I3,I4} where, * I1: Login. MIT, Pune. 82 Dept. of Computer Engg.

94 * I2: User details. * I3: Universities provided by the user in the wishlist module. * I4: User provides its SOP to analysis in the SOP module. O: Set of Outputs={O1,O2,O3,O4} where, * O1: List of recommended universities. * O2: The analysis made on the SOP provided by the user. * O3: The analysis on the wishlist universities provided by the user. * O4: The currency converter provides the user with the desired conversion. F:Set of Functions={F1, F2, F3, F4, F5} where, * F1: The user logs in the web application. * F2: The application recommends the user with the universities. * F3: The user can add the universities it desires to the wishlist and the application will then analysis the wishlist. * F4: The user can get its SOP analyzed by the SOP analysis module. * F5: The user can get the any currency converted into any currency with the help of currency converter. MIT, Pune. 83 Dept. of Computer Engg.

95 Annexure B LABORATORY ASSIGNMENTS ON PROJECT QUALITY AND RELIABILITY TESTING OF PROJECT DESIGN 84

96 Use of divide and conquer strategies to exploit distributed/parallel/concurrent processing of the above to identify object, morphisms, overloading in functions (if any), and functional relations and any other dependencies (as per requirements). It can include Venn diagram, state diagram, function relations, i/o relations; use this to derive objects, morphism, overloading With this strategy, a problem is solved by splitting it into sub-problems, solving them independently, and merging their solutions into a solution for the whole problem. The sub-problems can be solved directly, or they can in turn be solved using the same divide-and-conquer strategy, leading to an overall recursive program structure. The potential concurrency in this strategy is not hard to see: Since the sub-problems are solved independently, their solutions can be computed concurrently, leading to a parallel program that is very similar to its sequential counterpart. The following figure illustrates the strategy and the potential concurrency. Identify Morphism Figure B.1: Divide and Conquer Strategy Much of the terminology of morphisms, as well as the intuition underlying them, comes from concrete categories, where the objects are simply sets with some additional structure, and morphisms are structure-preserving functions. A MIT, Pune. 85 Dept. of Computer Engg.

97 category C consists of two classes, one of objects and the other of morphisms. There are two objects that are associated to everymorphism, the source and the target. For many common categories, objects are sets (usually with more structure) and morphisms are functions from an object to another object. Therefore the source and the target of a morphism are often called respectively domain and codomain. A morphism f with source X and target Y is written f : X Y. Thus a morphism is represented by an arrow from its source to its target. In the category of smooth manifolds, morphisms are smooth functions and isomorphisms are called diffeomorphisms. Functional Dependency Function is named unit of group of statement this unit can be invoked from other part of program as and when required. Or it is relationship between an attribute Y and a determinant (1 or more attribute) X such that for a given value of a determinant the value of attributes uniquely defined. An FD XY indicates a relationship between sets of attributes X and Y, such that any two entities that share a value for X must also share a value for Y or they have some relations. FDs generalize the option of a key in relational databases, and as such, provide important semantic clues about data consistency. X is determinant X determines y Y is functionally dependent on x X- y RS has the following functional dependencies:- There is a dependency of the background page (database on server) on the content script until the content script passes the characters well as numeric MIT, Pune. 86 Dept. of Computer Engg.

98 from the web page to the browser, the background page does not give the alerts or any message to user. The background page can access the database of the user only when there exists a profile specific to the user. Students will be able to log in only after he has signed up earlier. The logins will be updated only after proper creation of the tables specific to each student during sign up. Testing of project problem statement using generated test data (using mathematical models, GUI, Function testing principles, if any) selection and appropriate use of testing tools, testing of UML diagram s reliability. Write also test cases [Black box testing] for each identified functions. You can use Mathematica or equivalent open source tool for generating test data. Software testing can be stated as process of validating and verifying that a computer program/application/product: Meets the requirements that guided its design and development. Works as expected. Can be implemented with the same characteristics. Satisfies the needs of stake holders. Software testing, depending on the testing methods employed, can be implemented at any time in the development process. Traditionally, most of the test efforts occur after the requirements have been defined and the coding process has been completed but in the agile approaches most of the test effort is ongoing. As such the methodology of test is governed by a chosen software development methodology. Types of testing: MIT, Pune. 87 Dept. of Computer Engg.

99 Unit testing: Unit testing is a software testing method by which individual units of source code, sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures, are tested to determine whether they are fit for use. Intuitively, one can view a unit as the smallest testable part of an application. In procedural programming, a unit could be an entire module, but it is more commonly an individual function or procedure. In object-oriented programming, a unit is often an entire interface, such as a class, but could be an individual method. Unit tests are short code fragments created by programmers or occasionally by white box testers during the development process. It forms the basis for component testing. Integration testing: Integration testing is the phase in software testing in which individual software modules are combined and tested as a group. It occurs after unit testing and before validation testing. Integration testing takes as its input modules that have been unit tested, groups them in larger aggregates, applies tests defined in an integration test plan to those aggregates, and delivers as its output the integrated system ready for system testing. Acceptance testing: Acceptance testing is a test conducted to determine if the requirements of a specification or contract are met. It may involve chemical tests, physical tests, or performance tests. In systems engineering it may involve blackbox testing performed on a system (for example: a piece of software, lots of manufactured mechanical parts, or batches of chemical products) prior to its delivery. MIT, Pune. 88 Dept. of Computer Engg.

100 Test Cases 1. To test the authentication of user. 2. The error messages are shown upon validation errors. 3. Is the user data stored upon exist. 4. Is the SOP analysis working for numerous types of SOP. 5. Are the universities getting adding in the wishlist correctly. GUI testing GUI test is for testing if the GUI we have made for the application is working in the correct manner as desired. In our application we are going to have many buttons and lists so we will be checking if they are working properly. MIT, Pune. 89 Dept. of Computer Engg.

101 Annexure C PROJECT PLANNER 90

102 Figure C.1: Plan of Project MIT, Pune. 91 Dept. of Computer Engg.

103 Annexure D TERM-II PROJECT LABORATORY ASSIGNMENTS 92

104 1. Review of design and necessary corrective actions taking into consideration the feedback report of Term I assessment, and other competitions/conferences participated like IIT, Central Universities, University Conferences or equivalent centers of excellence etc. Based on the review of the software design we have made changes in the GUI of the project and made the GUI more attractive. We have changed the template of the web application to a more attractive template.we have added pictures to each universities and made the application more attractive. We have made added more modules to the software codes since the first term assessment.we have added more modules such SOP analysis, the currency converter and wishlist analysis. We have more data to our universities database i.e. we have added more universities to the universities table of the database to improve our accuracy of the web application. By adding universities with variety of profiles the accuracy of the application can be improved. The application is made more secure by adding more security measures to the students database.we have added the hash-key generation technique to make the passwords of the students in encrypted form. Hence we taken more security measures to make the application more secure. As per the feedback provided we have changed the admin login method. We have made the admin login and the user login through the same tab. 2. Project workstation selection, installations along with setup and installation report preparations. Hardware Requirements MIT, Pune. 93 Dept. of Computer Engg.

105 Dual Core Processor. 1 GB RAM. Internal Graphics Card. Internet access. Software Requirements Platform : Windows Run Time Environment : Java JDK1.7 Platform for Development :Eclipse Java EE IDE 3. Programming of the project functions, interfaces and GUI (if any) as per 1 st Term term-work submission using corrective actions recommended in Term-I assessment of Term-work. Following are Project Execution Steps: login into system Registration for new user. list of recommended universities. Sop analysis wish list MIT, Pune. 94 Dept. of Computer Engg.

106 4. Test Tool selection and testing of various test cases. Testing The purpose of testing is to discover errors. Testing is the process of trying to discover every conceivable fault or weakness in a work product. It provides a way to check the functionality of components, sub-assemblies, assemblies and/or a finished product. It is the process of exercising software with the intent of ensuring that the Software system meets its requirements and user expectations and does not fail in an unacceptable manner. Test Cases :- Set of inputs, execution conditions and expected results carried out for some objectives: Login Testing MIT, Pune. 95 Dept. of Computer Engg.

107 GUI Testing :- GUI means graphical user interface to test the friendliness of that system. Here we testing for the object properties and if the screens are user under stable or not. MIT, Pune. 96 Dept. of Computer Engg.

108 Annexure E INFORMATION OF PROJECT GROUP MEMBERS 97

109 1. Name : Ankita Dhanraj Marathe 2. Date of Birth :09/06/ Gender : Female 4. Permanent Address: Flat no.201, H2 Bldg Vanraji Heights, MIT Road Kothrud, Pune ankitamarathe94@gmail.com 6. Mobile/Contact No. : Paper Published : Yes MIT, Pune. 98 Dept. of Computer Engg.

110 1. Name : Nikita Dhanraj Marathe 2. Date of Birth :09/06/ Gender : Female 4. Permanent Address: Flat no.201, H2 Bldg Vanraji Heights, MIT Road Kothrud, Pune nmarathe1994@gmail.com 6. Mobile/Contact No. : Paper Published : Yes MIT, Pune. 99 Dept. of Computer Engg.

111 1. Name : Kalpana 2. Date of Birth :16/05/ Gender : Female 4. Permanent Address:RH-2,Sai kripa Soci,Opp Kunal Icon,Pimple Saudagar, Pune ideeagkg53@gmail.com 6. Mobile/Contact No. : Paper Published : Yes MIT, Pune. 100 Dept. of Computer Engg.

112 1. Name : Harshada Karande 2. Date of Birth :25/09/ Gender : Female 4. Permanent Address: Goghavari bangalo Sainarar Jalochi Baramati harshadakarande2509@gmail.com 6. Mobile/Contact No. : Paper Published : Yes MIT, Pune. 101 Dept. of Computer Engg.