Effects of Session Representation Models on the Performance of Web Recommender Systems

Usage pattern discovery is a crucial part of Web recommendation systems and is strongly related with the performance of the recommender. User sessions which are constructed from Web access logs give information about a user's navigational behavior. These session records are one of the main resources in the pattern discovery stage. The representation of the sessions can be in different forms such as feature vectors based on page durations or existence/nonexistence of pages. This work analyzes the effects of the different recommendation models which consider different characteristics of user sessions. For this purpose, we used three different recommender models. The first one considers only the existence of the visited pages in a session and the view time of each page. The second recommender model considers only the order of the visited pages in each session. The third model is based on the co-occurrence of the visited pages among user sessions. Our experimental results show that using the ordering information improves the prediction accuracy of the next request.

[1]  George Karypis,et al.  Selective Markov models for predicting Web page accesses , 2004, TOIT.

[2]  Ramesh R. Sarukkai,et al.  Link prediction and path analysis using Markov chains , 2000, Comput. Networks.

[3]  Jaideep Srivastava,et al.  Web usage mining: discovery and applications of usage patterns from Web data , 2000, SKDD.

[4]  D. Szafron,et al.  Sequence Alignment using FastLSA , 2000 .

[5]  Tao Luo,et al.  Effective personalization based on association rule discovery from web usage data , 2001, WIDM '01.

[6]  M. Tamer Özsu,et al.  A Web page prediction model based on click-stream tree representation of user behavior , 2003, KDD '03.

[7]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[8]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[9]  Jaideep Srivastava,et al.  Data Preparation for Mining World Wide Web Browsing Patterns , 1999, Knowledge and Information Systems.

[10]  M. Tamer Özsu,et al.  A Poisson Model for User Accesses to Web Pages , 2003, ISCIS.

[11]  Bamshad Mobasher,et al.  Discovery of Aggregate Usage Profiles for Web Personalization , 2000 .

[12]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[13]  Jaideep Srivastava,et al.  Grouping Web page references into transactions for mining World Wide Web browsing patterns , 1997, Proceedings 1997 IEEE Knowledge and Data Engineering Exchange Workshop.

[14]  Yannis Manolopoulos,et al.  . EFFECTIVE PREDICTION OF WEB-USER ACCESSES: A DATA MINING APPROACH , 2001 .

[15]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[16]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .