# A Fuzzy Community-Based Recommender System Using PageRank

## TL;DR

This paper presents a novel fuzzy community detection approach using personalized PageRank to enhance recommendation accuracy by leveraging local community structures and global rating similarities.

## Contribution

It introduces a fuzzy community detection method based on personalized PageRank, improving recommendation performance by combining local community and global rating information.

## Key findings

- Outperforms recent recommender systems on MovieLens and FilmTrust datasets.
- Utilizes fuzzy membership values for more accurate community-based similarity measures.
- Demonstrates the effectiveness of community detection in enhancing recommendation quality.

## Abstract

Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.