Explicit and implicit trust modeling compatibility and explicit trust link prediction in trust-based recommendation systems

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2022-1-31

Author

Demirci, Mehmet Utku

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In social networks, trust is a fundamental notion affecting the nature and the strength of ties between individuals. It is also a piece of useful auxiliary information for improving the performance of recommendation systems. The number of ratings given by a user is minimal compared to all items in popular, widely-used e-commerce sites. Therefore, the user-item matrix that is used in collaborative filtering suffers from data sparsity, resulting in poor recommendation quality. Another issue is the cold start problem, which occurs for the inclusion of new users and new items to the system. Trust notion helps alleviate the effect of these problems by providing additional relationships between the users and pointing out strong relationships. Information as to the trust between users can be explicitly available. However, such information is not widely available, and hence implicit trust models have been employed. This work analyzes two sub-problems under trust modeling for recommendation: (1) What is the relationship between explicit and implicit trust scores, are they replaceable? (2) Can the explicit trust in a trust network be modeled? An implicit trust model is presented for the first problem, and the compatibility of implicit and explicit trust scores is analyzed. For the second problem, two different explicit trust models are proposed: Explicit trust modeling through users' rating behavior and explicit trust modeling as a link prediction problem. The performances of the generated prediction models are analyzed on a set of benchmark data sets.

Subject Keywords

Trust modeling, Implicit trust, Explicit trust, Recommendation, Recommender systems, Supervised learning

URI

https://hdl.handle.net/11511/96338

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

M. U. Demirci, “Explicit and implicit trust modeling compatibility and explicit trust link prediction in trust-based recommendation systems,” M.S. - Master of Science, Middle East Technical University, 2022.