2009 TrustWalkeraRandomWalkModelforC

Subject Headings: Cold-Start User.

Notes

Categories and Subject Descriptors: H.2.8.d Information Technology and Systems: Database Applications - Data Mining.
General Terms: Algorithms, Design, Experimentation, Measurement

Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations for so-called cold start users that have rated only a very small number of items. In addition, these methods do not know how confident they are in their recommendations. Trust-based recommendation methods assume the additional knowledge of a trust network among users and can better deal with cold start users, since users only need to be simply connected to the trust network. On the other hand, the sparsity of the user item ratings forces the trust-based approach to consider ratings of indirect neighbors that are only weakly trusted, which may decrease its precision. In order to find a good trade-off, we propose a random walk model combining the trust-based and the collaborative filtering approach for recommendation. The random walk model allows us to define and to measure the confidence of a recommendation. We performed an evaluation on the Epinions dataset and compared our model with existing trust-based and collaborative filtering methods.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 TrustWalkeraRandomWalkModelforC	Martin Ester Mohsen Jamali			TrustWalker : A Random Walk Model for Combining Trust-based and Item-based Recommendation		KDD-2009 Proceedings		10.1145/1557019.1557067		2009