doi:10.16186/j.cnki.1673-9787.2020.4.16

Received:2019/11/16

Revised:2020/01/06

Published:2020/07/15

Matrix factorization recommendation algorithm by fusing semantic similarity

MIN Lu1, WANG Gensheng1,2,3, Huang Xuejian2

1.School of Humanities， Jiangxi University of Finance and Economics，Nanchang  330013， Jiangxi，China;2.Computer Practice Teaching Center，Jiangxi University of Finance and Economics， Jiangxi，Nanchang  330013，Jiangxi，China;3.School of International Trade and Economics， Jiangxi University of Finance and Economics，Jiangxi，Nanchang  330013，Jiangxi，China

Abstract:In order to solve the problem that the matrix factorization recommendation algorithm does not consider the characteristics of the recommended objects， a matrix factorization recommendation algorithm based on items semantic similarity was proposed. Firstly， the knowledge map distributed representation learning algorithm was used to embed the semantic data of the recommendation object domain into a low - dimensional semantic space. Then， the semantic similarity between the objects was calculated， which was integrated into the objective optimization function of matrix factorization. From the semantic perspective， it made up for the shortcomings that the recommendation algorithm of matrix factorization did not consider the characteristics of the recommended objects. The results showed that the improved algorithm had higher accuracy，recall and coverage than the traditional matrix factorization recommendation algorithm.

Key words:recommendation algorithm;matrix factorization;knowledge map;distributed representation learning;semantic similarity

融合语义相似度的矩阵分解推荐算法_闵潞.pdf