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Stochastic trust network enriched by similarity relations to enhance trust-aware recommendations

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Abstract

Collaborative filtering (CF) is the most popular recommendation approach that has been extensively employed in recommender systems. However, it suffers from some weaknesses, including problems with cold start users, data sparsity and difficulty in detecting malicious users. Trust-based recommender systems can overcome these weaknesses by using the ratings of trusted users. However, since users often provide few trust statements, trust networks are typically sparse and therefore the cold start and sparsity problems still remain. In this paper, we use the positive correlation between trust and interest similarity to enrich trust network by similarity relations and propose a stochastic trust propagation-based method, called LTRS, which utilizes the enriched trust network to provide enhanced recommendations. In comparison with existing recommender systems combining trust and similarity information, the proposed system (1) incorporates both trust and similarity relations in the trust propagation process and, in this way, increases the coverage and accuracy of predictions; and (2) addresses the dynamic nature of both trust and similarity by modelling the enriched network as a stochastic graph, and continuously captures their variations during the recommendation process and not at fixed intervals. The experimental results indicate that the proposed method can significantly improve the recommendation accuracy and coverage.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. Department of Computer Engineering and IT, Amirkabir University of Technology, Tehran, Iran

    Mina Ghavipour & Mohammad Reza Meybodi

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  1. Mina Ghavipour
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Correspondence to Mina Ghavipour.

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Ghavipour, M., Meybodi, M.R. Stochastic trust network enriched by similarity relations to enhance trust-aware recommendations. Appl Intell 49, 435–448 (2019). https://doi.org/10.1007/s10489-018-1289-9

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