Social Trust as a solution to address sparsity-inherent problems of Recommender systems

TL;DR

This paper proposes a social trust-based scheme to mitigate sparsity and cold start issues in recommender systems by leveraging user ratings to estimate trust, demonstrating improved performance over traditional methods.

Contribution

It introduces a novel trust calculation scheme based on user ratings to enhance recommender systems and addresses sparsity and cold start problems effectively.

Findings

Trust-based scheme outperforms non-trust systems

Alleviates cold start issues for new users

Improves recommendation accuracy over time

Abstract

Trust has been explored by many researchers in the past as a successful solution for assisting recommender systems. Even though the approach of using a web-of-trust scheme for assisting the recommendation production is well adopted, issues like the sparsity problem have not been explored adequately so far with regard to this. In this work we are proposing and testing a scheme that uses the existing ratings of users to calculate the hypothetical trust that might exist between them. The purpose is to demonstrate how some basic social networking when applied to an existing system can help in alleviating problems of traditional recommender system schemes. Interestingly, such schemes are also alleviating the cold start problem from which mainly new users are suffering. In order to show how good the system is in that respect, we measure the performance at various times as the system evolves and we also contrast the solution with existing approaches. Finally, we present the results which justify that such schemes undoubtedly work better than a system that makes no use of trust at all.