2019/08/13 by Mostafa Khalaji, Nilufar Mohammadnejad
Computer Science · Social Sciences · #Artificial intelligence #Collaborative filtering #Computer science #Data mining #Digital Marketing and Social Media #Human Mobility and Location-Based Analysis #Information retrieval #Machine learning #Measure (data warehouse) #MovieLens #Precision and recall #Recall #Recommender Systems and Techniques #Recommender system #Similarity (geometry) #Word error rate #cs.IR #cs.SI
paper · pdf · doi:10.1007/s42452-019-1071-6
Accepted in SN Applied sciences journal
arxiv created 2019/08/13 · arxiv updated 2019/08/16 · openalex publication_date 2019/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users' tastes over a particular item. According to the cold start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5% and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974 respectively.