Trust-aware Recommender Systems
Paolo Massa, Paolo Avesani
Proceeding of ACM Recommender Systems Conference, Minneapolis, Minnesota, USA
2007

Trust-aware Recommender Systems

Recommender Systems based on Collaborative Filtering suggest to users items they might like. However due to data sparsity of the input ratings matrix, the step of finding similar users often fails. We propose to replace this step with the use of a trust metric, an algorithm able to propagate trust over the trust network and to estimate a trust weight that can be used in place of the similarity weight. An empirical evaluation on Epinions.com dataset shows that Recommender Systems that make use of trust information are the most effective in term of accuracy while preserving a good coverage. This is especially evident on users who provided few ratings.

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  1. Pingback: » Recommender Systems conference in Minneapolis - Paolo blog: Ramblings on Trust, Reputation, Recommender Systems, Social Software, Free Software, ICT4D and much more

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