A First Principles Approach to Trust-Based Recommendation Systems
<|reference_start|>A First Principles Approach to Trust-Based Recommendation Systems: 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.<|reference_end|>
@article{stefanopoulos2024a,
    title={A First Principles Approach to Trust-Based Recommendation Systems},
    author={Paras Stefanopoulos, Ahad N. Zehmakan, Sourin Chatterjee},
    journal={arXiv preprint arXiv:2407.00062},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00062},
    primaryClass={cs.IR}
}