Unified filtering by combining collaborative filtering and content-based filtering via mixture model and exponential model

Authors:
Luo Si;Rong Jin
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Michigan State University, East Lansing, MI
Venue:
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Year:
2004

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Abstract

Collaborative filtering and content-based filtering are two types of information filtering techniques. Combining these two techniques can improve the recommendation effectiveness. The main problem with previous research is that the content information and the rating information are not combined in an integrated way. This paper presents a unified probabilistic framework that allows the mutual interaction between these two types of information. Experiments have shown that the new unified filtering algorithm outperforms a pure collaborative filtering approach, a pure content-based filtering approach and another unified filtering algorithm.