Open AccessMatrix factorization recommendation algorithms based on knowledge map representation learning1
Author(s) -
Xuejian Huang,
Lü Min,
Gensheng Wang,
Guangming Tian,
Zhipeng Li
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1423/1/012065
Subject(s) - matrix decomposition , computer science , non negative matrix factorization , representation (politics) , factorization , pattern recognition (psychology) , similarity (geometry) , matrix (chemical analysis) , recommender system , artificial intelligence , object (grammar) , algorithm , incomplete lu factorization , theoretical computer science , machine learning , image (mathematics) , composite material , politics , political science , eigenvalues and eigenvectors , physics , materials science , quantum mechanics , law
The matrix factorization recommendation algorithm does not consider characteristics of the recommendation object itself, resulting in poor recommendation results. Therefore, a matrix factorization recommendation algorithm based on knowledge map representation learning is proposed. Firstly, the recommendation object is represented as a low dimensional semantic vector by using the knowledge map distributed representation learning algorithm. Then the semantic similarity between objects is calculated, and the semantic similarity is incorporated into the objective optimization function of matrix factorization, so that the feature vectors obtained by matrix factorization can also contain semantic knowledge, which makes up for the shortcoming of matrix factorization recommendation algorithm that does not consider characteristics of the recommendation object itself from the semantic perspective. The experimental results show that the improved algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm.