文章基本信息

标题：Simulated Annealing with Levy Distribution for Fast Matrix Factorization-Based Collaborative Filtering
作者：Mostafa A. Shehata ; Mohammad Nassef ; Amr A. Badr 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：4
DOI：10.14569/IJACSA.2018.090445
出版社：Science and Information Society (SAI)
摘要：Matrix factorization is one of the best approaches for collaborative filtering because of its high accuracy in presenting users and items latent factors. The main disadvantages of matrix factorization are its complexity, and are very hard to be parallelized, especially with very large matrices. In this paper, we introduce a new method for collaborative filtering based on Matrix Factorization by combining simulated annealing with levy distribution. By using this method, good solutions are achieved in acceptable time with low computations, compared to other methods like stochastic gradient descent, alternating least squares, and weighted non-negative matrix factorization.
关键词：Simulated annealing; levy distribution; matrix factorization; collaborative filtering; recommender systems; metaheuristic optimization