Research/Review Paper: Web Personalization Using Usage Based Clustering Author: Madhavi M.Mali,Sonal S.Jogdand, Deepali P. Shinde Paper ID: V1-I3-002

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1 Journal) Volume1, Issue3, Nov-Dec, 2014.ISSN: (Online) International Journal of Advanced Research in Technology, Engineering and Science (A Bimonthly Open Access Online. Research/Review Paper: Web Personalization Using Usage Based Clustering Author: Madhavi M.Mali,Sonal S.Jogdand, Deepali P. Shinde Paper ID: V1-I3-002 Visit: Copyright to IJARTES

2 Journal) Volume1, Issue3, Nov-Dec, 2014.ISSN: (Online) Web Personalization Using Usage Based Clustering Madhavi M.Mali 1, Sonal S.Jogdand 2, Deepali P. Shinde 3 ABSTRACT In today s internet environment it is more difficult to access the relevant information from the web. Because www is a vast data warehouse of web pages and links.on internet huge amount of information is available which are approximately 1 millions of pages are added day to day. To get the right information from such warehouse to the user and to avoid website exploration web personalization get needed. It is the life blood of web usages mining and e-learning process to improve the system and its design as per the user s interest. It acts as a tool to avoid the content over loading on websites for effective web navigation. Here we present web personalization which introduces web mining that is application of a data mining. Keywords: Data mining, web mining, web usage mining, personalization. 1. INTRODUCTION Today s world is Information Age due to information globalization and technology, www is a powerful tool to store, retrieve and propagate the information as per the user s need. To improve on click search on a particular website, it is necessary for web developer to use web personalization. In [1] Web Personalization is simply defined as the task of making Web-based information systems adaptive to the needs and interests of individual users. Typically a personalized websites fulfills the user s aspectives by collecting information about their preferences and adopts its services to increase ratio of human computer interaction. We describe the process of personalization in terms of an application of a data mining to the collected web data. Web personalization is the science of improving navigation result of visitor s requirement by extracting statistical information and discovering interesting usage patterns of visitors using clustering algorithms. First Author Name: Madhavi M.Mali, Pimpri Chinchwad Polytechnic, Second Author Name: Sonal S.Jogdand, Pimpri Chinchwad Polytechnic, Third Author Name: Deepali P. Shinde, Pimpri Chinchwad Polytechnic, To discover usage patterns web personalization uses a tool called web mining. Web mining is advanced research area in web personalization. Web mining examines the use of data mining on the World Wide Web. Web mining is the application data mining techniques on the web to discover interesting patterns. The website contents are customized as per user preferences and frequent access. The pages which are accessed frequently by the user are monitored to provide customized view of the website to the user. DATA MINING The application of data mining techniques depends on data types: Web content mining, web structure mining, and web usage mining. 2. WEB MINING Web mining is a data mining technique of exploring the information from the web as per user usage. Web mining is classified into following types: 2.1 Web content mining Web content mining is a process of analyzing the content of web pages. It is used to identifying the most frequently accessed information. It allows scanning of entire web to retrieve needed information from clustered pages and provide the same to search engines. Web content mining also helps to give high quality results to the uses when required to search engines. Due to this it increases the productivity because of direct use of content mining of text and visuals. 2.2 Web structure mining Web structure mining deals with linking of different web pages which might be static or dynamic [12]. The linking is through XML tags and hyperlinks. 2.3 Web usage Mining WUM is a technique of identifying user preferences within a particular site. Depending on user access patterns i.e. which information the user access or search frequently, the user choices are identified. This is done through page references and session information of the user. Information is also collected from web server and application server tags. The patterns collected through WUM helps to understand the visitor s preferences. It also helps to organize the site efficiently and create a personalized view of the page or site to the user. Typical data sources for web usage mining are web structure data, web content data, user profile and weblog. All Rights Reserved 2014 IJARTES Visit: Page 7

3 Fig. for WUM 3. WEB USAGE MINING AND PERSONILIZATION For realizing the more personalized, user friendly and business web services the essential tool used is Web usage mining. As we discuss above to avoid surplus of information on website web personalization is used. By using web usage mining based on web personalization we are able to identify needs and preferences of each user about web nevigation.wum is the process of discovering and interpreting patterns of user access to web systems by digging the data collected from the user and machine interactions. Typically WUM system based on 4 layers: 1. Data Collection/Tracking - In which user interactions are captured and acquired. In data collection phase, the data is collected from the web servers and from the information sent by the client. Packet which is sent across the network is also monitored. This data collection is used for personalization. 2. Data Preprocessing- In this phase, we find out from where the data is received. This information is collected from the session information. Techniques are used to filter the data and use it in the next stage. 3. Pattern Discovery- The discovered patterns are usually represented as collections of pages, objects, or resources that are frequently accessed by groups of users with common interests. To determine the effective marketing strategies and optimizing the logical structure of the website analyzing of the users, how website is accessed is critical. According to the patterns required for web personalization which corresponds to the interests of the user. At this stage by applying the learning methods we insist the construction of user models. 4. Knowledge Post Processing: This is the last phase where extracted data is evaluated and represent in the human understandable forms such as reports and visual techniques. 4. PERSONILIZATION Personalization who stores, collects, combines the information from transaction of sites, scrutinize the information and according to the result it produces the information for people who visit the website. Any action that adapts information or services provided by a website to the needs of user by taking advantage of the knowledge gained from the user s navigational behavior is web personalization. Web personalization s techniques are used by websites to send customize advertisements to the customers and recommendation of different products. It is used largely in marketing tactics to increase the e- commerce business. Web personalization can be done in the following methods: 1. Implicit:-Implicit personalization will be performed by system or web page based on the user behavior on the web. 2. Explicit:-User will be able to modify the system using the feature provided by the system itself. 3. Hybrid:-It is a combination of both implicit and explicit. A Web personalization system can offer a variety of functions. The personalization functions are: memorization, guidance, customization and task performance support [13]. Each of these is examined in more detail below. Memorization: This is the simplest form of personalization method where the system records and stores information about the user in its memory. For example name and browsing history. The past history of the user is displayed without the further processing whenever the user returns to the site. Memorization is offered as complete personalization solution rather than a standalone function. Guidance: - It refers to make an effort to assist the user in getting the information the user is in search of and also provide the user with alternative browsing options. Customization:-It refers to modification of web page in terms of content, structure and layout in order to understand user s knowledge, preferences and interests. The main purpose is the management of information load for easy interaction of the user with the site. Task Performance Support:- Task performance support is a client side personalization system which acts on behalf of the user. It is very similar All Rights Reserved 2014 IJARTES Visit: Page 8

4 to the adaptive learning systems used in educational models. 5. APPROACHES TO WEB PERSONALIZATION During the evolution of the web, personalization has been recognized as a remedy to the information overload problem and as a means of increasing visitor loyalty to a Web site. Considering the importance of web personalization for customizes services following are the approaches in brief: (a)manual decision rule systems- In this approach the designers design the web contents through different user model. Two examples from a wide range of products that adopt this approach are Yahoo! s personalization engine and web sphere Personalization (IBM). (b) Content-based filtering systems- In this approach the users browsing patterns are analyzed and accordingly different filtering systems are used. The personal preferences of the user are taken into consideration when the page is shown to him next time. These models can be used to filter news items according to each user s requirements. (c) Social or collaborative filtering systems- In this approach, a particular service of the website is personalized taken into consideration the ratings and the statistics information obtained from customer browsing. Most commonly used in amazon.com electronic shop. The Recommendation Engine (Net Perceptions) and web sphere personalization (IBM) are examples of products that use also this method. Fig 1: Web usage mining process 6. CONCLUSION AND FUTURE DIRECTIONS Analysis of business goals, development of business requirements, use cases and metrics are required by personalization. If this is done properly using personalization will not be a difficult task for organizations to increase their business throughput. 7. ACKNOWLEDGMENTS We express our gratitude towards the experts who have contributed in the development of subject herein. REFERENCES [1] Web Usage Mining and Personalization Bamshad Mobasher, DePaul University [2] Analysis of Web Usage Mining Hui Yu, Zhongmin Lu School of Management, South Central University for Nationalities, Wuhan, P.R.China [3] Intelligent Techniques for Web Personalization Sarabjot Singh Anand and Bamshad Mobasher [4] Web Mining Accomplishments & Future Directions Jaideep Srivastava, Prasanna Desikan, Vipin Kumar [5] Web Mining by Juan C. Dürsteler [6] Efficient and Anonymous Web-Usage Mining for Web Personalization Cyrus Shahabi, Farnoush Banaei-Kashani [7] Web site personalization IBM developer works All Rights Reserved 2014 IJARTES Visit: Page 9

5 [8] Web Usage Mining & Personalization Dr. Guandong Xu, [9] Intelligent Web & Information Systems (IWIS) Department of Computer Science, Aalborg University [10] A Conceptual Model for Website Personalization and Web Personalization Kavita Das and O.P. Vyas [11] Intelligent Techniques for Web Personalization Sarabjot Singh Anand and Bamshad Mobasher [12] How to Win Online: Advanced Personalization in E-Commerce An Oracle White Paper March 2011 [13] A Near Real-Time Personalization for ecommerce Platform Amit Rustagi [14] Web Usage Mining as a Tool for Personalization: A Survey Dimitrios Pierrakos, Georgios paliouras, Christos Papatheodorou and Constantine D. Spyropoulos [15] R. Cooley, B. Mobasher, and J. Srivastava. Data Preparation for Mining World Wide Web Browsing Patterns. Journal of Knowledge and Information Systems, 1(1):5 32, [16] J. Srivastava, R. Cooley, M. Deshpande, and P. Tan. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, 1(2):12 23, 2000 [17] Web usage mining: Discovery and applications of usage patterns from web data. Jaideep Srivastava, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan SIGKDD Explorations, 1(2):12 23, [18] WebMining, All Rights Reserved 2014 IJARTES Visit: Page 10