Applying Evolutionary-based User Characteristic Clustering and Matrix Factorization to Collaborative Filtering for Recommender Systems

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.

Automated summary

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.