Using a trust network to improve top-N recommendation

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended for topN recommendation, but this approach does not work accurately for cold start users that have rated only a very small number of items. In this paper we propose novel methods exploiting a trust network to improve the quality of top-N recommendation. The first method performs a random walk on the trust network, considering the similarity of users in its termination condition. The second method combines the collaborative filtering and trust-based approach. Our experimental evaluation on the Epinions dataset demonstrates that approaches using a trust network clearly outperform the collaborative filtering approach in terms of recall, in particular for cold start users. Categories and Subject Descriptors H.2.8.d [Information Technolog...

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Mohsen Jamali, Martin Ester
• Keywords: Cold Start Users, Control Systems, RECSYS 2009, Top-N Item Recommendation, Trust Network