Local Collaborative Filtering: A Collaborative Filtering Method that Utilizes Local Similarities among Users
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911269906284544 |
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| author | Shen, Zhaoxin Wu, Dan |
| author_facet | Shen, Zhaoxin Wu, Dan |
| contents | To leverage user behavior data from the Internet more effectively in recommender systems, this paper proposes a novel collaborative filtering (CF) method called Local Collaborative Filtering (LCF). LCF utilizes local similarities among users and integrates their data using the law of large numbers (LLN), thereby improving the utilization of user behavior data. Experiments are conducted on the Steam game dataset, and the results of LCF align with real-world needs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13166 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local Collaborative Filtering: A Collaborative Filtering Method that Utilizes Local Similarities among Users Shen, Zhaoxin Wu, Dan Information Retrieval Artificial Intelligence Human-Computer Interaction 68T09 (Primary) 68M11, 68W27 (Secondary) H.3.3 To leverage user behavior data from the Internet more effectively in recommender systems, this paper proposes a novel collaborative filtering (CF) method called Local Collaborative Filtering (LCF). LCF utilizes local similarities among users and integrates their data using the law of large numbers (LLN), thereby improving the utilization of user behavior data. Experiments are conducted on the Steam game dataset, and the results of LCF align with real-world needs. |
| title | Local Collaborative Filtering: A Collaborative Filtering Method that Utilizes Local Similarities among Users |
| topic | Information Retrieval Artificial Intelligence Human-Computer Interaction 68T09 (Primary) 68M11, 68W27 (Secondary) H.3.3 |
| url | https://arxiv.org/abs/2511.13166 |