2018/12/18 by Maliheh Goliforoushani, Goliforoushani, Maliheh, Radin Hamidi Rad +3
Computer Science · Mathematics · #Caching and Content Delivery #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Recommender Systems and Techniques #Social and Information Networks (cs.SI) #Text and Document Classification Technologies #cs.IR #cs.LG #cs.SI #stat.ML
paper · pdf · doi:10.48550/arxiv.1812.09380
arxiv created 2018/12/18 · openalex publication_date 2018/12/18 · arxiv updated 2018/12/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.