Abstract
Collaborative filtering is one of the most prominent techniques in Recommender System (RS) to retrieve useful information by using most similar items or users. However, traditional collaborative filtering approaches face many limitations like data sparsity, semantic similarity assumption, fake user profiles and they often do not care about user’s evolving interests; such flaws lead to user’s dissatisfaction and low performance of the system. To cope with these limitations, we propose a new dynamic trust-based similarity approach. We compute trust score of the users by means of implicit trust information between them. The experimental results demonstrate that the proposed approach performs better than the existing trust-based recommendation algorithms in terms of accuracy by dealing with the aforementioned limitations.
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Roy, F., Sarwar, S.M., Hasan, M. (2015). User Similarity Computation for Collaborative Filtering Using Dynamic Implicit Trust. In: Khachay, M., Konstantinova, N., Panchenko, A., Ignatov, D., Labunets, V. (eds) Analysis of Images, Social Networks and Texts. AIST 2015. Communications in Computer and Information Science, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-26123-2_22
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DOI: https://doi.org/10.1007/978-3-319-26123-2_22
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