Using a trust network to improve top-N recommendation

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Using a trust network to improve top-N recommendation

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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents

New York, New York, USA

SESSION: Trust and evaluation table of contents

Pages 181-188

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Mohsen Jamali	Simon Fraser University, Burnaby, BC, Canada
Martin Ester	Simon Fraser University, Burnaby, BC, Canada

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 34, Downloads (12 Months): 34, Citation Count: 0

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ABSTRACT

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 top-N 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.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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