There are many kinds of intelligent technologies for helping web page navigation. Among them, association rules are one of popular technologies to discover interesting and frequent user access patterns in web sites. But association rules have not been very useful in practice because excessive rules are generated. In this paper, we suggest a new approach to discover a small number of more accurate rules from web site access records. By analyzing ten thousands of web page navigation logs from a shopping mall site, we have noticed that people have a special pattern in web page navigation when their interests change. When their interest is changed, they drop by one of frequently visited web pages such as a list page of items or the front page. For example, if a person was visiting web pages on MP3 players and now he wants to move to mobile phones, he usually drop by the front page or a list page of items and then choose a web page on mobile phones rather than directly goes to mobile phones from MP3 players. The proposed method separates a session record into several sub-sessions with such frequently visited pages, and finds user access patterns in the separated ones. The separated sub-session records may have more cohesive access patterns because those are related to the same item. With this idea, we construct a method of web page request prediction. We evaluate our system with huge data set. With those results, we confirm that our proposed method is effective on web page prediction.
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[Show abstract][Hide abstract] ABSTRACT: To engage visitors to a Web site at a very early stage (i.e., before registration or authentication), personalization tools must rely primarily on clickstreamdata captured in Web server logs. The lack of explicit user ratings as well as the sparse nature and the large volume of data in such a setting poses serious challenges to standard collaborative filtering techniques in terms of scalability and performance. Web usage mining techniques such as clustering that rely on offline pattern discovery from user transactions can be used to improve the scalability of collaborative filtering, however, this is often at the cost of redfied recommendation accuracy. In this paper we propose effective and scalable techniques for Web personalization based on association rule d scovery from usage data. Through detailed experimental evaluation on real usage data, we show that the proposed methodology can achieve better recommend tion effectiveness, while maintaining a computational advantage over direct approaches to collaborative filtering such as the k-nearest-neighbor strategy.
[Show abstract][Hide abstract] ABSTRACT: The Web has become a huge repository of information and keeps growing exponentially
under no editorial control, while the human capability to find, read and understand
content remains constant. Providing people with access to information is not the
problem; the problem is that people with varying needs and preferences navigate
through large Web structures, missing the goal of their inquiry. Web personalization
is one of the most promising approaches for alleviating this information overload,
providing tailored Web experiences. This chapter explores the different faces of
personalization, traces back its roots and follows its progress. It describes the modules
typically comprising a personalization process, demonstrates its close relation to Web
mining, depicts the technical issues that arise, recommends solutions when possible,
and discusses the effectiveness of personalization and the related concerns. Moreover,
the chapter illustrates current trends in the field suggesting directions that may lead
to new scientific results.
Web Mininig: Applications and Techniques, Edited by Web Mining: Applications and Techniques, 07/2005: chapter Mining for Web Personalization: pages 27-49; Idea Group Inc (IGI).
[Show abstract][Hide abstract] ABSTRACT: The proliferation of information on the world wide web has made the personalization of this information space a necessity. One possible approach to web personalization is to mine typical user profiles from the vast amount of historical data stored in access logs. In the absence of any a priori knowledge, unsupervised classification or clustering methods seem to be ideally suited to analyze the semi-structured log data of user accesses. In this paper, we define the notion of a "user session", as well as a dissimilarity measure between two web sessions that captures the organization of a web site. To extract a user access profile, we cluster the user sessions based on the pair-wise dissimilarities using a robust fuzzy clustering algorithm that we have developed. We report the results of experiments with our algorithm and show that this leads to extraction of interesting user profiles. We also show that it outperforms association rule based approaches for this task. 1 Introducti...
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