Abstract
Collaborative filtering based recommender system (CF-RS) provides personalized recommendations to users utilizing the experiences and opinions of their nearest neighbours. Although, collaborative filtering (CF) is the most successful and widely implemented filtering, data sparsity is still a major concern. In this work, we have proposed a fuzzy trust propagation scheme to alleviate the sparsity problem. Since trust is often a gradual trend, so trust to a person can be expressed more naturally using linguistic expressions. In this work, fuzzy trust is represented by linguistic terms rather than numerical values. We discuss the basic trust concepts such as fuzzy trust modeling, propagation and aggregation operators. An empirical evaluation of the proposed scheme on well known Movie-Lens dataset shows that fuzzy trust propagation allows reducing the sparsity problem of RSs while preserving the quality of recommendations.
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Kant, V., Bharadwaj, K.K. (2011). Incorporating Fuzzy Trust in Collaborative Filtering Based Recommender Systems. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_53
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DOI: https://doi.org/10.1007/978-3-642-27172-4_53
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