A First Principles Approach to Trust-Based Recommendation Systems in Social Networks
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916625183145984 |
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| author | Stefanopoulos, Paras Chatterjee, Sourin Zehmakan, Ahad N. |
| author_facet | Stefanopoulos, Paras Chatterjee, Sourin Zehmakan, Ahad N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A First Principles Approach to Trust-Based Recommendation Systems in Social Networks Stefanopoulos, Paras Chatterjee, Sourin Zehmakan, Ahad N. Information Retrieval 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. |
| title | A First Principles Approach to Trust-Based Recommendation Systems in Social Networks |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2407.00062 |