通过用户局部相似性提升推荐系统数据利用率

Local Collaborative Filtering: A Collaborative Filtering Method that Utilizes Local Similarities among Users

• 基于用户间局部相似性构建推荐模型
• 在Steam数据集上表现符合真实需求
• 适合注重数据高效利用的推荐场景

为更有效地利用互联网用户行为数据以提升推荐系统性能，本文提出一种新型协同过滤方法——局部协同过滤（Local Collaborative Filtering, LCF）。LCF通过挖掘用户间的局部相似性，并运用大数定律整合这些相似用户的数据，从而提升用户行为数据的利用效率。在Steam游戏数据集上的实验表明，LCF的推荐结果与真实世界需求高度一致，验证了其有效性与实用性。

原文摘要 · Abstract (English)

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.

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