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

arXiv: https://arxiv.org/abs/2407.00062 · HTML: https://mlsanity.com/paper/2407.00062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [GGBond: Growing Graph-Based AI-Agent Society for Socially-Aware Recommender Simulation](https://mlsanity.com/paper/2505.21154/) (2025)

## Abstract (arXiv)

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.