期刊
JOURNAL OF INTERNET TECHNOLOGY
卷 23, 期 4, 页码 693-708出版社
LIBRARY & INFORMATION CENTER, NAT DONG HWA UNIV
DOI: 10.53106/160792642022072304005
关键词
Recommender systems; Collaborative filtering; Evolutionary algorithm; User characteristic clustering; Matrix factorization
With the rise of the Internet service industry, recommender systems have been widely used. This study proposes a recommendation algorithm based on evolutionary algorithm, combining user characteristic clustering and matrix factorization, to improve recommendation quality.
In recent years, with the rise of numerous Internet service industries, recommender systems have been widely used as never before. Users can easily obtain the information, products or services they need from the Internet, and businesses can also increase additional revenue through the recommender system. However, in today's recommender system, the data scale is very large, and the sparsity of the scoring data seriously affects the quality of the recommendation Thus, this study intends to propose a recommendation algorithm based on evolutionary algorithm, which combines user characteristic clustering and matrix factorization. In addition, the exponential ranking selection technology is employed for evolutionary algorithm. The experiment result shows that the proposed algorithm can obtain better result in terms of four indicators, mean square error, precision, recall, and F score for two benchmark datasets.
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