A Conceptual Model for Website Personalization and Web Personalization

Transcription

1 International Journal of Research and Reviews in Information Sciences (IJRRIS) Vol. 1, No. 4, December 2011, ISSN: Science Academy Publisher, United Kingdom A Conceptual Model for Website Personalization and Web Personalization Kavita Das 1 and O.P. Vyas 2 1 SoS in Computer Science and IT, Pt. Ravishankar Shukla University, Raipur, C.G., India 2 IIIT, Allahabad, U.P., India Abstract Web Personalization is the need of the time due to huge and ever growing volume of web content that is open for access to any user. It will be make the web useful in all walks of life. Web personalization involves prediction of the web content required by a surfer at any time and deliver them simultaneously. Web Personalization is being worked upon using data available at server as well as client side. This work presents an architectural model for personalization system using web usage mining for a website with personalization features distributed to server and browser. It is suggested that good web personalization can be achieved by implementing personalization activities at various levels in the web. This paper also discusses how web structure mining and web content mining can contribute to complete web personalization. As any big task starts from a small tasks, complete web personalization could be apprehended by starting personalization of each website using bottom up approach. Keywords Website personalization, web mining, web personalization, Browser Intelligence, Server Intelligence 1. Introduction The web has pervaded in all walks of life. The majority of users of web are non_expert users. They spend a lot of time in learning the intricacies of the websites and the browser and experience a tiring and overwhelming surfing in finding required information. Thus, web personalization emerges as a natural need for having its usability towards all users. Web personalization is the adaptability of information systems to the needs of their users. This can be achieved by understanding the surfer s requirements and their difficulties in exploration and by eliminating the shortcomings of the web. Much have been contributed in this direction by content based filtering, collaborative filtering [9] and few log analysis using statistical methods. Currently Web mining is emerging as a promising approach for web personalization. Web mining approaches provide insight for achieving web personalization. The works contributed towards personalization of a website are based on user profiles and web usage patterns. Web usage patterns could be deciphered by applying data mining techniques on web usage records of the surfers. These techniques include Association Rule Mining, Clustering, Classification and Sequential Patterns Mining. Clustering of the user profiles may be used to find groups of users sharing similar features and interests, but its success depends on the valid user feedbacks. This approach may be useful for websites, such as online banking, insurance companies, corporate and business houses, that maintain authentic users and sometimes provide with relevant returns to the users based on their categories. Such websites could be. Association Rule Mining on the popular patterns of web page visits, of many users in a website, would be useful to find the next probable request of page during a session. In long term, this information could be used to restructure a website and provide promotional functions and advertisements to the surfers. Another aspect is to understand the mood and task of a user in order to understand the depth and vastness of information needed by a user. Thus, classification of the mood and task of user based on their browsing behavior, may help to determine the required set of web pages. This feature will be specially useful in information rich websites such as education portals, online encyclopedias, online newspapers. This capability will make the browser intelligent. Search engines may also find this capability very useful. Again, finding the sequence of elements or information accessed over multiple sessions by a user could be found by Sequential Pattern Mining on websites providing collections such as online music, online book stores and any kind of sales. These capabilities will make the website server intelligent. All these techniques when used on web resources, are included in Web Usage Mining. It could provide website personalization. Mining the link structures over multiple related websites could give insight into popular links in demand. This approach is called Web Structure Mining. As different websites may use different vocabulary for same subject in their respective web pages, finding semantically similar web pages will require mining the contents of the web pages in multiple websites. This approach is called Web Content Mining. These approaches could provide personalization over the web. This paper presents an approach for developing a complete web personalization system according to software engineering principles. It also presents a model for website personalization. It includes mainly three modules the browser intelligence, the server intelligence and their respective databases. The intelligence modules contain rich processes for predictive tasks and their updation. The intelligence are developed using Web usage Mining. The databases contain the rules and records of surfings. An

2 K. Das and O.P. Vyas / IJRRIS, Vol. 1, No. 4, pp , December intelligent browser is capable to understand the specific intentions of a surfer. An intelligent server is capable to understand the general patterns of its visitors. The discovered rules and patterns can then be used for improving the system s performance or for making modifications to the web site. The relation of Web Mining to automated personalization tools is straightforward. The work on Web Mining can be a source of ideas and solutions towards realizing Web personalization. Personalization may be achieved first, by implementing personalization features in each website autonomously, then have personalization over a group of websites having related features or information. The web may appear as islands of personalized websites and lead to a complete web personalization. In the rest of this paper, Section 2 describes the research works contributed for developing personalization features, Section 3 gives activities of web mining for web personalization. Section 4 gives general model for web personalization. Section 5 describes the detailed model for web personalization. Section 6 discusses how complete web personalization could be achieved from personalized websites. In the following sections, this paper is concluded with an outlook into future works. 2. Related Works Many research works are being contributed towards achieving the capability for prediction of next web pages of any surfer in order to develop web personalization. Most of them are contributed by user profiling using explicit or implicit feedbacks, server log analysis and browsing activities and proposed few recommender systems. The research span time in this target is yet very short. In the paper [11] 2000, it worked on clustering user profiles based on their navigation patterns, without any explicit feedbacks from web users. Same approach is adopted in [12] in 2010 using sliding window of time on active session. [14] 2011 presented a very efficient fuzzy algorithm for clustering. In papers [2, 3, 9] in 2003 Web personalization have been proposed to be achieved by majorly user profiling obtained from content based filtering, collaborative filtering and rule based filtering. But success of these methods depend on explicit user feedbacks. Web usage mining on server logs was also included for finding web page navigation patterns of surfers. The works [4, 5, 6] in has done pioneering research in understanding the tasks and browsing activities of a surfer at the web browser contributed significantly in improving design of the browser. The tasks that were considered significant are Information Gathering, Browsing, Fact Finding and Transaction, and provided their general characteristics visible on the browser. In [8] 2009, the browsing log data on an online newspaper had been collected at an extended browser by few users who have been assigned some predefined tasks related to Information Gathering, Just Browsing, Fact Finding. The classification technique C4.5 and association rule mining Apriori had been applied on it. Some classification rules were found with accuracy above 80%. PageViewDuration, TimeSpentOnStartPage, PageviewsPerMinute, were found significant in the rules. Association relationship among some attributes was also found. [7] 2010 proposed Web Intelligent model using genetic algorithms on web navigation pageviews in transactions and achieved high efficiency in finding web navigation patterns in comparison to other previous methods. In the paper [13] in 2011, it proposed a system to perform web usage mining on user activities on a web page i.e. event tracking. It worked for feature vector extraction on a browser using session clustering.. It also describes that features are highly application dependent that can be used for predicting the web pages on a website. The work [15] 2011, is done on search engines. It found user preferences from click through behavior of users and semantic relation between documents to adapt the search engine s ranking function. It proposed the combination of Web usage mining and Web content mining for client side analysis to have effective information retrieval in web personalization. 3. Web Mining For Web Personaization Web Personalization can be perceived as a huge activity with complex personalization subsystems. Different aspects of personalization needs can be appropriately said as proper candidates of data mining on the web. Major steps towards web personalization is modeled here as web mining activities and their applications in developing the proposed intelligence in the web as shown in figure 1. Figure 1. Activity diagram for Web Personalization.

3 K. Das and O.P. Vyas / IJRRIS, Vol. 1, No. 4, pp , December In the above activity diagram, step 3.1 corresponds to Web Structure Mining (WSM), step 3.2 corresponds to Web Content Mining (WCM), step 3.3 corresponds to Web Usage Mining (WUM). Steps in 4 correspond to algorithms of personalization activities. Step 5 & 6 correspond to website & web intelligence in delivering personalization features and Step 7 corresponds to reorientation of web structure to enhance intelligence of the web personalization system. 4. A General Model For Website Personalization 4.1. General Model Web Personalization may be such that Browser becomes the salesperson of the showroom Website. The browser may keep a record of profiles of the surfers using it and understand the keywords, type of information and its scope required by the surfer automatically based on usage behavior. Based on the observations sent by the browser, the server will deliver appropriate web pages to the browser. Web personalization system should be developed such that while surfers are using a browser and visiting a web site, the system should learn to predict the interested items in a short time period. If the prediction is correct, the surfer uses the delivered items on the browser. Otherwise a new prediction starts for the next time period. The system process will include three phases- 1. The Learning Phase a) Collection of the surfer profiles and their browsing behaviors of one lac or more sessions of surfers visiting the site from various terminals into a file of Browsing set. Collection of their web page sequences also visited during each session into a file of Sequence set. b) Application of Clustering technique of data mining on profiles, Browsing set and Sequence set to find the existing categories in them. c) Application of Classification technique of data mining on Browsing set to find the classification rules of browsing behavior for various tasks or intentions into Rules set. d) Application of Sequential Pattern mining technique of data mining on Sequence set to find the popular sequences of web items required by the surfers over single multiple sessions into final Sequence sets. 2. The Working Phase- While a surfer is navigating a website, the browser will be monitoring its browsing behavior and its web pages sequence. Within a short time period, the browser will predict its task according to the Rules set found in the learning phase and stored in the databases. The browser also provides opportunity for enquiry and feedbacks from the surfer meanwhile. The predicted task and web pages sequence, along with the keywords used by the surfer, are sent to the website server as a request. Based on the task and sequence received as request, The server will select suitable web pages and web contents according to keywords and Sequence sets. The selected web pages and contents are sent as reply to the browser. 3. The Maintenance Phase- At the browser, the surfer may use the delivered web pages that marks a hit. If the surfer switches to other pages, it will show a miss. Based on the hit or miss, the weights of the used rules will be increased or decreased. INPUT USER INTERFACE OUTPUT BROWSER WEB MINING SYSTEM FUNCTION & CONTROL WEBSTE PERSONALIZA- TION DATABASES BROWSER MAINTENANCE & SELF TEST Figure 2. Context diagram for Website Personalization. The context diagram for Website Personalization is shown in figure 2. Hatley-Pirbhai modeling technique has been used here. It establishes the information-boundary between the system being implemented and the environment in which the system is to operate. The Web Personalization system is presented with 5 processing regions- User Interface, Input Process, System Function and Control, Output Process and Maintenance and Self Test. 5. Detailed Conceptual Model For Website Personalization The detailed model, the next level of the context diagram, is presented in figure 3. It shows that the personalization system consists of modules- Browser Intelligence, Server Intelligence, Filter and databases in browser and server that contain decision making rules and also new collected usage data and feedbacks. In the diagram, CRM=Classification rule Mining, SPM=Sequential Pattern Mining. The browser is aimed to act as an expert salesman for the shop owner- the website server. The browser, other than fulfilling its general routine of presenting web pages and input processing, will have an intelligence module. The intelligence module will have the job of learning the specific preferences and requirements of information of a surfer at any time from the observation of surfer s browsing behavior in order to predict the task and dimensions of the information. So that recommendation of keywords and details can be made to the server. In order to support itself in the job, the browser may take some feedback quickly. The server, other than fulfilling its general routine of processing requests and sending back web pages to the browser, will also have an intelligence module to understand the general requirements of any surfer. Its job will be to understand the sequence of web pages popularly visited in the website. This will help the server guess the popular information and links expected by any surfer. The main job of Server Intelligence will be to have rich methods for filteration of suitable web pages based on general information itself and browser s recommendations.

4 K. Das and O.P. Vyas / IJRRIS, Vol. 1, No. 4, pp , December Figure 3. Detailed model for Web Personalization. The databases will be for keeping record of surfers profiles and activities in the browser and the server and the filtration rules that are to be used by their intelligence modules respectively The Browser Intelligence In this vast web, browser is in the forefront that lead a surfer into it. In this fast growing space of web, it is desirable that browser grows in experience/expertise in recognizing the preferences of a surfer based on his body language and verbal feedbacks i.e. his browsing language [10] and feedbacks. The main objective is to learn the interests of a surfer in a website so that he can be assisted to reach the needed thing easily and quickly. Before this module starts working, its intelligence must be initialized. It must have collection of rules relating to the Browsing Language. Browsing Language could be built with Web Usage Mining. First, at least user profiles and browsing behavior of few thousand website visitors must be recorded. User profiles are transferred to the server by the browser. Second, Clustering technique be applied on the recorded browsing behaviors to find the various categories of behaviors that indicate the various tasks done by surfers while visiting the website. Then, Classification rule mining on the browsing behavior will produce their relationship to the tasks. These rules are vital and collected as rules of Browsing Language in website server. After the initial profiles set, tasks set and browsing language are ready, Intelligence module can start functioning. The Browser intelligence activates as soon as a surfer connects to the website. The browser starts sending the browsing pattern to the intelligence module. The intelligence searches and matches it to the browsing language. In a short time period, it returns the task of the surfer. In order to confirm about the keywords, the browser may take few feedbacks from the surfer quickly. Then the browser sends his profile, keywords, task to the server as request for next web pages. Also, information of the sequence of web pages visited by him are transferred to the server. The special feature of an intelligent browser will be the capability to learn to understand the vastness and depth of need of the information on any keywords provided by the surfer. Otherwise the surfing will be a costly task over unknown and lengthy paths. Figure 4. Model for Intelligence in Browser for a Website. The Development of Browsing Language is currently a significant and complicated area of research being done and evolving widely. It is still in its preliminary stage and has various issues [10] to deal with. The following are proposed to be significant requirements for an intelligent browser a) Designing a more user friendly and efficient browser. b) Derive the various types of tasks being done by surfers while visiting the web. Some of the tasks are Information Gathering, Just Browsing, Fact finding, Transaction, etc. c) Developing Browsing Language [10]- learn the rules of browsing activities and behavior to guess the task of a surfer [8]. d) To find a suitable data structure to keep the new learned rules and optimize them. e) Periodical updation in the rules. Modeling the browsing behavior of surfer is an indicative of possible intentions of user. Thus information requirement of a user may be predicted and supported by other means of web personalization The Server Intelligence The server intelligence contains the knowledge of general patterns of browsing of webpages and high rated webpages. Before this module starts working, its intelligence must also be initialized. It must have collection of rules relating to the general browsing patterns of web pages in the website and also of high rated pages. The ratings of pages and patterns may change periodically. Popular patterns could be built with Web Usage Mining and Sequential pattern Mining. First, at least user profiles and browsing behavior of few thousand sessions must be recorded. User profiles should be clustered into groups of similar profiles. Second, Sequential Pattern Mining be applied on the recorded pages visits to find general patterns among visitors. After the initial categories of profiles set, sequential patterns set and browsing language are ready, the most important requirement is efficient searching and matching methodology for suitable web pages based on keywords, tasks, profile and sequential pattern provided by a surfer. Then the Intelligence module can start functioning. The server intelligence activates as soon as a surfer connects to the website. The browser sends the profile of the surfer to the server. The server sends the Browsing Language set to the browser as initialization. Then the browser also

5 K. Das and O.P. Vyas / IJRRIS, Vol. 1, No. 4, pp , December starts sending surfer s page pattern to the server. When the server also receives the task and keywords of the surfer from the browser, the intelligence searches and matches it to the initialized intellectual databases. In a very short time period, it returns the set of relevant pages to the surfer. As the result of the automatic request generated by the browser, it sends back a set of recommended web pages to the browser for presenting them to the surfer. If the surfer accesses those web pages, browser informs a hit otherwise a miss to the server. The server updates its ratings of web pages, Browsing Language rules, and matched sequence pattern. This process is repeated continuously. INPUT USER INTERFACE OUTPUT WUM SYSTEM FUNCTION & CONTROL Selection & Filteration of Web Pages BROWSER SEQUENTIAL USER MAINTENANCE PATTERNS DB PROFILES & SELF TEST Figure 5. Model for Intelligence in Website Server. Much efforts have been put in understanding the browsing and navigation patterns of surfers to guess dynamically the future requests of a particular surfer. 6. Web Personalization From Websites Personalizations Consider a group of similar websites, all are based on a common field like education, banking, etc., each having adopted to personalization. They are capable of predicting the next page requests of any surfer upto a satisfactory level in the website. Then a search engine could perform more efficiently on the subject. Moreover, as each website would maintain degree of popularity of webpages and of popular links, the group will support better web structure mining and web content mining. Hence the popular link structures, navigation paths and set of similar content webpages could be easily deciphered over the web space. Though web usage mining has almost the complete role in website personalization, web structure mining and web content mining could produce realistic and very useful results over a group of websites for achieving the desired personalization over multiple websites. Bottom-up approach of design of web personalization is suitable since personalization attributes are very website specific. New personalization features could be further mined out from browsing patterns in the group. First developing individual website level personalizations and then proceeding towards developing personalization within a group of similar websites. This practice could be further extended over related and multiple groups of websites. This will provide a more systematic approach towards complete personalization. Hence, general level and specific levels of personalization and optimizations may develop semi-automatic and automatic personalization systems over the web also. Figure 6. Development Hierarchy of web personalization. As shown in figure 6, Web mining can produce the personalization features in the websites and the web. The websites and the web structure can be modified accordingly. This may further simplify the personalization processes and provide a comprehensive website and web personalization. 7. Conclusion Web Intelligence Web Structure Mining Web Personalization Orientation of Web and Websites Web Content Mining This paper presents a model for web personalization approach using web mining. Web mining activities are distributed at the server and browser sides for finding the personalization features. This work has also discusses proposes bottom-up approach for achieving web personalization from personalized websites. Bottom-up modular approach of implementation, distributed appropriately to servers and browsers, will be appropriate for achieving personalization at website and web levels. This paper explores the various studies made towards developing automatic tools for predicting the preferred pages or information to a user at a time, in order to provide more relevant pages to a surfer. This capability may be used to delegate relevant web pages before any explicit request comes from the surfer. This work introduces the conceptual model of website personalization and presents a general model and detailed model for it. 8. Outlook and Future Work Web Usage Mining a) Web Personalization system may be extended for promotion of newly added contents of existing websites to the surfers and new websites to a group. b) Furthur, the degree of automatic personalization may be customizable by the surfer. A completely automatic system is never desirable to all surfers. Further its high degree may be desirable to physically challenged people. c) An intelligent browser could make a complete profile of users including surfing history and personal details. But a user may have variety in need of information at any time. The relationship between a profile and its information needs may be a rare item set in many cases.

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