Paolo Massa
Paolo Avesani
ABSTRACT
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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Index Terms
- Trust-aware recommender systems
Recommendations
Trust in recommender systems
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Modelling trust networks using resistive circuits for trust-aware recommender systems
Recommender systems have been widely used for predicting unknown ratings. Collaborative filtering as a recommendation technique uses known ratings for predicting user preferences in the item selection. However, current collaborative filtering methods ...
Item Reputation-Aware Recommender Systems
iiWAS '14: Proceedings of the 16th International Conference on Information Integration and Web-based Applications & ServicesRecommender systems provide personalized advice for online customers based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems employ users' ratings to generate their output. In ...
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