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

TL;DR

This paper presents a trust-based recommendation framework for social networks, emphasizing the importance of item ratings, trust graphs, and intra-item data, with a flexible Weighted Average approach for various similarity metrics.

Contribution

It introduces a first principles approach that combines multiple information sources, particularly trust graphs, to improve robustness and flexibility in social network recommender systems.

Findings

Item ratings are most influential in collaborative filtering.

Trust graph-based methods are more robust against adversarial attacks.

Intra-item information improves prediction consistency when fused with other data.

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.