Mission-Based Navigational Behaviour Modeling for Web Recommender Systems

Web recommender systems anticipate the information needs of on-line users and provide them with recommendations to facilitate and personalize their navigation. There are many approaches to building such systems. Among them, using web access logs to generate users’ navigational models capable of building a web recommender system is a popular approach, given its non-intrusiveness. However, using only one information channel, namely the web access history, is often insufficient for accurate recommendation prediction. We therefore advocate the use of additional available information channels, such as the content of visited pages and the connectivity between web resources, to better model user navigational behavior. This helps in better modeling users’ concurrent information needs. In this chapter, we investigate a novel hybrid web recommender system, which combines access history and the content of visited pages, as well as the connectivity between web resources in a web site, to model users’ concurrent information needs and generate navigational patterns. Our experiments show that the combination of the three channels used in our system significantly improves the quality of web site recommendation and, further, that each additional channel used contributes to this improvement. In addition, we discuss cases on how to reach a compromise when not all channels are available.

[1]  Henry Lieberman,et al.  Autonomous interface agents , 1997, CHI.

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

[3]  Krishna Bharat,et al.  Improved algorithms for topic distillation in a hyperlinked environment , 1998, SIGIR '98.

[4]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[5]  Robin D. Burke,et al.  Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

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

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

[8]  Philip S. Yu,et al.  Efficient Data Mining for Path Traversal Patterns , 1998, IEEE Trans. Knowl. Data Eng..

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

[10]  Arbee L. P. Chen,et al.  Enabling personalized recommendation on the Web based on user interests and behaviors , 2001, Proceedings Eleventh International Workshop on Research Issues in Data Engineering. Document Management for Data Intensive Business and Scientific Applications. RIDE 2001.

[11]  Sergio A. Alvarez,et al.  Collaborative Recommendation via Adaptive Association Rule Mining , 2000 .

[12]  Tao Luo,et al.  Integrating Web Usage and Content Mining for More Effective Personalization , 2000, EC-Web.

[13]  Anupam Joshi,et al.  Extracting Web User Profiles Using Relational Competitive Fuzzy Clustering , 2000, Int. J. Artif. Intell. Tools.

[14]  Ada Wai-Chee Fu,et al.  Incremental Document Clustering for Web Page Classification , 2002 .

[15]  Osmar R. Zaïane,et al.  Combining Usage, Content, and Structure Data to Improve Web Site Recommendation , 2004, EC-Web.

[16]  Jia Li,et al.  Using Distinctive Information Channels for a Mission-based Web Recommender System , 2004 .

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

[18]  Jeffrey Heer,et al.  LumberJack: Intelligent Discovery and Analysis of Web User Traffic Composition , 2002, WEBKDD.

[19]  Jon M. Kleinberg,et al.  Automatic Resource Compilation by Analyzing Hyperlink Structure and Associated Text , 1998, Comput. Networks.

[20]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[21]  Patrick Pantel,et al.  Document clustering with committees , 2002, SIGIR '02.

[22]  Oren Etzioni,et al.  Adaptive Web Sites: Automatically Synthesizing Web Pages , 1998, AAAI/IAAI.

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