Local Collaborative Filtering: A Collaborative Filtering Method that Utilizes Local Similarities among Users
This addresses the challenge of enhancing recommendation accuracy for users in online platforms, but it appears incremental as it builds on existing collaborative filtering techniques.
The paper tackled the problem of improving recommender systems by proposing Local Collaborative Filtering (LCF), a method that uses local user similarities and the law of large numbers to better utilize user behavior data, with experiments on the Steam game dataset showing results that align with real-world needs.
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.