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

TL;DR

This paper introduces Local Collaborative Filtering (LCF), a new method that leverages local user similarities and the law of large numbers to enhance recommender system performance using internet user data.

Contribution

The paper presents a novel CF approach that utilizes local similarities among users and integrates data via LLN to improve recommendation accuracy.

Findings

LCF outperforms traditional CF methods on the Steam dataset.

Local similarities improve recommendation relevance.

Experimental results align with real-world user behavior.

Abstract

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.