2019/08/13 by Mostafa Khalaji, Khalaji, Mostafa, Nilufar Mohammadnejad +1 · 1 citation
Computer Science · #Artificial intelligence #Cluster analysis #Collaborative filtering #Computer science #Data Stream Mining Techniques #Data mining #Database #FOS: Computer and information sciences #Fuzzy logic #Heuristic #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Information retrieval #Machine learning #MovieLens #Precision and recall #Recommender Systems and Techniques #Recommender system #Scalability #Sentiment Analysis and Opinion Mining #Set (abstract data type) #Similarity (geometry) #Social and Information Networks (cs.SI) #cs.IR #cs.SI
paper · pdf · doi:10.48550/arxiv.1908.05608
Accepted in 4th International Conference on Researchers in Science & Engineering & International Congress on Civil, Architecture and Urbanism in Asia, Kasem Bundit University, Bangkok, Thailand
arxiv created 2019/08/13 · openalex publication_date 2019/08/13 · arxiv updated 2019/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Recommender systems are systems that are capable of offering the most suitable services and products to users. Through specific methods and techniques, the recommender systems try to identify the most appropriate items, such as types of information and goods and propose the closest to the user's tastes. Collaborative filtering offering active user suggestions based on the rating of a set of users is one of the simplest and most comprehensible and successful models for finding people in the same tastes in the recommender systems. In this model, with increasing number of users and movie, the system is subject to scalability. On the other hand, it is important to improve the performance of the system when there is little information available on the ratings. In this paper, a movie hybrid recommender system based on FNHSMHRS structure using resource allocation approach called FCNHSMRAHRS is presented. The FNHSMHRS structure was based on the heuristic similarity measure (NHSM), along with fuzzy clustering. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system suggestions. The proposed systems is based on collaborative filtering and, by using the heuristic similarity measure and applying the resource allocation approach, improves the performance, accuracy and precision of the system. The experimental results using MAE, Accuracy, Precision and Recall metrics based on MovieLens dataset show that the performance of the system is improved and the accuracy of recommendations in comparison of FNHSMHRS and collaborative filtering methods that use other similarity measures for finding similarity, is increased