Web Page Personalization Based on Weighted Association Rules

Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the user’s navigational behavior. Personalized recommendation by predicting user-browsing behavior using association-mining technology has gained much attention in web personalization research area. However, the resulting association patterns did not perform well in prediction of future browsing patterns due to the low matching rate of the resulting rules and users’ browsing behavior. In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule. We assign a significant weight to each page based on the time spent by user on each page and visiting frequency of each page, taking in to account the degree of interest instead of binary weighting. We present new personalized recommendation method base on the proposed weighted association-mining technique. We show, through experimentation on real data set that this approach results in more objective and representative predictions and shows a significant improvement in the recommendation effectiveness in comparison to the traditional association rule approaches.

[1]  Abdul Manan Ahmad,et al.  Web Page Recommendation Model for Web Personalization , 2004, KES.

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

[3]  Maurice Mulvenna,et al.  Personalization on the Net using Web Mining , 2000 .

[4]  Maurice D. Mulvenna,et al.  Personalization on the Net using Web mining: introduction , 2000, CACM.

[5]  Ayhan Demiriz,et al.  Enhancing Product Recommender Systems on Sparse Binary Data , 2004, Data Mining and Knowledge Discovery.

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

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

[8]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[9]  Susan T. Dumais,et al.  SIGIR 2003 workshop report: implicit measures of user interests and preferences , 2003, SIGF.

[10]  Ada Wai-Chee Fu,et al.  Mining association rules with weighted items , 1998, Proceedings. IDEAS'98. International Database Engineering and Applications Symposium (Cat. No.98EX156).

[11]  Liang Yan,et al.  Incorporating Pageview Weight into an Association-Rule-Based Web Recommendation System , 2006, Australian Conference on Artificial Intelligence.

[12]  Mathias Géry,et al.  Evaluation of web usage mining approaches for user's next request prediction , 2003, WIDM '03.

[13]  Philip K. Chan,et al.  A Non-Invasive Learning Approach to Building Web User Profiles , 1999 .

[14]  Jaideep Srivastava,et al.  Web mining: information and pattern discovery on the World Wide Web , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[15]  George Karypis,et al.  Item-based top-N recommendation algorithms , 2004, TOIS.

[16]  Cyrus Shahabi,et al.  Knowledge discovery from users Web-page navigation , 1997, Proceedings Seventh International Workshop on Research Issues in Data Engineering. High Performance Database Management for Large-Scale Applications.

[17]  John Riedl,et al.  An algorithmic framework for performing collaborative filtering , 1999, SIGIR '99.

[18]  Jaideep Srivastava,et al.  Automatic personalization based on Web usage mining , 2000, CACM.

[19]  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.

[20]  MAGDALINI EIRINAKI,et al.  Web mining for web personalization , 2003, TOIT.

[21]  Kristian J. Hammond,et al.  Mining navigation history for recommendation , 2000, IUI '00.

[22]  John Riedl,et al.  Analysis of recommendation algorithms for e-commerce , 2000, EC '00.

[23]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[24]  Yoichi Shinoda,et al.  Information filtering based on user behavior analysis and best match text retrieval , 1994, SIGIR '94.

[25]  Bamshad Mobasher,et al.  A Hybrid Web Personalization Model Based on Site Connectivity , 2003 .

[26]  Fionn Murtagh,et al.  Weighted Association Rule Mining using weighted support and significance framework , 2003, KDD '03.