2019/09/26 by Mostafa Khalaji, Khalaji, Mostafa, Chitra Dadkhah +1
Computer Science · Social Sciences · #Artificial intelligence #Cluster analysis #Collaborative filtering #Computer science #Data mining #Database #Digital Marketing and Social Media #FOS: Computer and information sciences #Fuzzy logic #Heuristic #Image Retrieval and Classification Techniques #Image and Video Quality Assessment #Information Retrieval (cs.IR) #Information retrieval #Machine Learning (cs.LG) #Machine learning #Measure (data warehouse) #MovieLens #Recommender Systems and Techniques #Recommender system #Scalability #Set (abstract data type) #Similarity (geometry) #Similarity measure #The Internet #World Wide Web #cs.IR #cs.LG
paper · pdf · doi:10.48550/arxiv.1909.13765
published in arXiv (Cornell University) (Cornell University) · 6 pages, Conference: 7th Iranian Joint Congress on Fuzzy and Intelligent Systems, 18th Conference on Fuzzy Systems and 17th Conference on Intelligent Systems At: Bojnord, Iran, University of Bojnord, p.p 562-568, January 2019. Persian format
arxiv created 2019/09/26 · openalex publication_date 2019/09/26 · arxiv updated 2019/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Nowadays, Recommender Systems have become a comprehensive system for helping and guiding users in a huge amount of data on the Internet. Collaborative Filtering offers to active users based on the rating of a set of users. One of the simplest and most comprehensible and successful models is to find users with a taste in recommender systems. In this model, with increasing number of users and items, the system is faced to scalability problem. On the other hand, improving system performance when there is little information available from ratings, that is important. In this paper, a hybrid recommender system called FNHSMHRS which is based on the new heuristic similarity measure (NHSM) along with a fuzzy clustering is presented. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system recommendations. The proposed system is based on the collaborative filtering model and is partnered with the heuristic similarity measure to improve the system's performance and accuracy. The evaluation of the proposed system based results on the MovieLens dataset carried out the results using MAE, Recall, Precision and Accuracy measures Indicating improvement in system performance and increasing the accuracy of recommendation to collaborative filtering methods which use other measures to find similarities.