ComputingData ScienceData MiningInformation RetrievalRecommender SystemsCollaborative FilteringOnline RecommendationRecommendation EvaluationRecommendation SystemsTrust In RecommendationTrust-aware RecommendationData SparsityEmpirical EvaluationSparsity ConstraintsTrust In Recommender SystemsTrust Network AnalysisTrust PropagationTrust Propagation In Recommender SystemsTrust-aware Collaborative Filtering
Collaborative filtering recommender systems suggest items based on user similarity, but data sparsity often hampers similarity estimation. The study proposes replacing similarity estimation with a trust metric that propagates trust across a network to compute trust weights. The authors employ a trust‑metric algorithm that propagates trust over the trust network to estimate trust weights for recommendation. Empirical evaluation on Epinions.com demonstrates that trust‑based recommenders achieve higher accuracy and maintain good coverage, particularly for users with few ratings.
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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