GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Comparing feature-based and clique-based user models for movie selection
Proceedings of the third ACM conference on Digital libraries
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Feature-based and Clique-based User Models for Movie Selection: A Comparative Study
User Modeling and User-Adapted Interaction
CIA '98 Proceedings of the Second International Workshop on Cooperative Information Agents II, Learning, Mobility and Electronic Commerce for Information Discovery on the Internet
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An image retrieval method based on analysis of feedback sequence log
Pattern Recognition Letters
Proceedings of the 1st ACM international workshop on Connected multimedia
Content-Based image filtering for recommendation
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Automatic classification for grouping designs in fashion design recommendation agent system
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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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.