Clustering techniques can be used to improve collaborative filtering for venue recommendation. The document discusses using user clustering based on co-authorship networks and item clustering based on citation similarity between venues. Evaluation on DBLP and CiteSeerX datasets shows that clustering approaches provide better recommendation accuracy than standard collaborative filtering. Future work includes analyzing the impact of overlapping vs non-overlapping communities on recommendation quality.

1. Clustering Techniques for Collaborative Filtering and the Application to Venue Recommendation Manh Cuong Pham , Yiwei Cao, Ralf Klamma Information Systems and Database Technology RWTH Aachen, Germany Graz , Austria, September 01, 2010 I-KNOW 2010

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