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Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 43، دوره 11، شماره 1، تیر 2020، صفحه 483-495 اصل مقاله (559.43 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2020.19127.2058 | ||
| نویسندگان | ||
| Nasim Tohidi؛ Chitra Dadkhah* | ||
| Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran | ||
| تاریخ دریافت: 23 آبان 1398، تاریخ بازنگری: 17 دی 1398، تاریخ پذیرش: 18 دی 1398 | ||
| چکیده | ||
| The growth amount of information on the Web makes it difficult for many web users to make decision and choose either information or goods. Thus, a recommender system is an approach that helps users to obtain their needs according to her/his preference within a massive amount of information rapidly without waste of time. The main advantage of using a recommender system in any online shopping or social media like Amazon, Netflix and Facebook is to increase the percentage of overall profits, customer satisfaction and retention. In this paper, we introduce an approach to increase the accuracy and to improve the performance of collaborative filtering recommender system. In this paper a hybrid approach is proposed to improve the performance of video collaborative filtering recommender system based on clustering and evolutionary algorithm. Proposed approach combines k-means clustering algorithm and two different evolutionary algorithms which are Accelerated Particle Swarm Optimization Algorithm (APSO) and Forest Optimization Algorithm (FOA). The main aim of this paper is to increase the accuracy of recommendation of user-based collaborative filtering video recommender system. Evaluation and computational results on the MovieLens dataset show that the proposed method has a better performance than the other related methods. | ||
| کلیدواژه‌ها | ||
| recommender system؛ accelerated particle swarm optimization؛ forest optimization algorithm؛ collaborative filtering؛ clustering | ||
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