A First Principles Approach to Trust-Based Recommendation Systems in Social Networks

2024/06/17 by Stefanopoulos, Paras, Chatterjee, Sourin, Zehmakan, Ahad N.
#FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · doi:10.48550/arxiv.2407.00062

Abstract

This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than other information types in a collaborative filtering approach. The trust graph-based approaches were found to be more robust to network adversarial attacks due to hard-to-manipulate trust structures. Intra-item information, although sub-optimal in isolation, enhances the consistency of predictions and lower-end performance when fused with other information forms. Additionally, the Weighted Average framework is introduced, enabling the construction of recommendation systems around any user-to-user similarity metric. All the codes are publicly available on GitHub.

Related

• A Distributed Method for Trust-Aware Recommendation in Social Networks
• Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence
• Exploring Trust-Aware Neighbourhood in Trust-based Recommendation
• Social Trust as a solution to address sparsity-inherent problems of Recommender systems
• Matrix Factorization with Explicit Trust and Distrust Relationships