Trust Modeling in Recommendation

Date

2021-12-09

Author

Demirci, Mehmet Utku
Karagöz, Pınar

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In social networks, trust is a fundamental notion aecting the na-ture and the strength of ties between individuals. It is also a pieceof useful auxiliary information for improving the performance ofrecommendation systems. The number of ratings given by a user isminimal compared to all items in popular, widely-used e-commercesites. Therefore, the user-item matrix that is used in collaborativeltering suers from data sparsity, resulting in poor recommenda-tion quality. Another issue is the cold start problem, which occursfor the inclusion of new users and new items to the system. Trustnotion is helpful for alleviating the eect of these problems byproviding additional relationships between the users and pointingout strong relationships. Information as to the trust between userscan be explicitly available. However, such information is not widelyavailable, and hence implicit trust models have been employed.This work analyzes two sub-problems under trust modeling forrecommendation: (1) What is the relationship between explicit andimplicit trust scores, are they replaceable? (2) Can we model ex-plicit trust in a trust network? For the rst problem, we present animplicit trust model and analyze the compatibility of implicit andexplicit trust scores. For the second problem, we model explicit trustmodeling as a link prediction problem and analyze the performanceof the prediction models we generate on a set of benchmark datasets

Subject Keywords

Trust modeling, Implicit trust, Explicit trust, Recommendation, Rec-ommender systems, Supervised learning

URI

http://dx.doi.org/10.1145/3444757.3485105
https://hdl.handle.net/11511/95429

DOI

https://doi.org/10.1145/3444757.3485105

Conference Name

MEDES '21: Proceedings of the 13th International Conference on Management of Digital EcoSystem

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. U. Demirci and P. Karagöz, “Trust Modeling in Recommendation,” presented at the MEDES ’21: Proceedings of the 13th International Conference on Management of Digital EcoSystem, Tunus, 2021, Accessed: 00, 2022. [Online]. Available: http://dx.doi.org/10.1145/3444757.3485105.