Personalized information recommendation simulation system based on compound recommendation algorithm——A research tool to study the push effect of algorithm
Yuqiao Yan, Zhang Weiping, Luo Fangdan, Hongli Zhang · IOP Conference Series Materials Science and Engineering · 2020
Abstract The benefits and effects of algorithmic push are affecting all walks of life. It has also aroused the interest of media research and the discussion on the technical and moral issues involved in algorithmic push. In recent years, the discussion about “information cocoons” has become more and more intense. How to make effective quantitative calculation for algorithm recommendation is the key to empirical research on this problem. Therefore, this study designed a tool to effectively operate this variable around this problem. Based on the analysis of the main recommendation mechanism of algorithmic information distribution, this paper points out a method of how to measure the accuracy of algorithm recommendation in practical research, and designs a simulation personalized information recommendation system based on composite recommendation algorithm combining two mainstream recommendation mechanisms, collaborative filtering and text analysis.