Improving collaborative filtering with multimedia indexing techniques to create user-adapting Web sites

The Internet is evolving from a static collection of hypertext, to a rich assortment of dynamic services and products targeted at millions of Internet users. For most sites it is a crucial matter to keep a close tie between the users and the site. More and more Web sites build close relationships with their users by adapting to their needs and therefore providing a personal experience. One aspect of personalization is the recommendation and presentation of information and products so that users can access the site more efficiently. However, powerful filtering technology is required in order to identify relevant items for each user. In this paper we describe how collaborative filtering and content-based filtering can be combined to provide better performance for filtering information. Filtering techniques of various nature are integrated in a weighed mix to achieve more robust results and to profit from automatic multimedia indexing technologies. The combined approach is evaluated in a prototype user-adapting Web site, the Active WebMuseum.

[1]  Bradley N. Miller,et al.  Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system , 1998, CSCW '98.

[2]  Nils J. Nilsson,et al.  Artificial Intelligence , 1974, IFIP Congress.

[3]  Joshua Alspector,et al.  Comparing feature-based and clique-based user models for movie selection , 1998, DL '98.

[4]  Arnd Kohrs,et al.  Clustering for collaborative filtering applications , 1999 .

[5]  Yoav Shoham,et al.  Fab: content-based, collaborative recommendation , 1997, CACM.

[6]  Naohiro Ishii,et al.  Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents , 1998, CIA.

[7]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[8]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[9]  Bradley N. Miller,et al.  GroupLens: applying collaborative filtering to Usenet news , 1997, CACM.

[10]  Shih-Fu Chang,et al.  Local color and texture extraction and spatial query , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[11]  Arnd Kohrs Using color and texture indexing to improve collaborative filtering of art paintings , 1999 .

[12]  Shih-Fu Chang,et al.  Tools and techniques for color image retrieval , 1996, Electronic Imaging.

[13]  Upendra Shardanand Social information filtering for music recommendation , 1994 .

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