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Applying multi-factor Beta distribution-based trust for improving accuracy of recommender systems

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Abstract

Calculation and applying trust among users have become popular in designing recommender systems in recent years. Considering multiple factors for estimating the value of trust can improve the accuracy of trust-based recommender systems. In this paper, a multi-factor approach for estimating trust among users of recommender systems is introduced. In the proposed scheme, first, users of the system are clustered based on their similarities in demographic information and history of ratings. To predict the rating of the active user into a specific item, the value of trust between him and the other users in his cluster is calculated considering three factors i.e., time, location, and context of their ratings. To this end, we propose an algorithm based on the beta distribution. A novel tree-based measure for computing the semantic similarity between the contexts is utilized. Finally, the rating of the active user is predicted using weighted averaging where trust values are considered as weights. The proposed scheme was performed on three datasets, and the obtained results indicated that it outperforms existing methods in terms of accuracy and other efficiency metrics.

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Data availability

The data supporting the findings of this study are available either within the article or on request from the corresponding author, Hassan Shakeri.

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Authors and Affiliations

  1. Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

    Samaneh Sheibani, Hassan Shakeri & Reza Sheibani

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  1. Samaneh Sheibani
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  2. Hassan Shakeri
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  3. Reza Sheibani
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Correspondence to Hassan Shakeri.

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Sheibani, S., Shakeri, H. & Sheibani, R. Applying multi-factor Beta distribution-based trust for improving accuracy of recommender systems. Multimed Tools Appl 83, 41327–41347 (2024). https://doi.org/10.1007/s11042-023-17265-x

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