Open AccessHybrid matrix factorization recommendation algorithm based on item similarity
Author(s) -
Lü Min,
Xuejian Huang,
Gensheng Wang
Publication year - 2020
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 - 10.1088/1742-6596/1629/1/012064
Subject(s) - similarity (geometry) , matrix decomposition , non negative matrix factorization , computer science , recommender system , factorization , collaborative filtering , matrix (chemical analysis) , algorithm , data mining , pattern recognition (psychology) , information retrieval , artificial intelligence , image (mathematics) , eigenvalues and eigenvectors , physics , materials science , quantum mechanics , composite material
In order to solve the problem of sparse data in matrix factorization recommendation algorithm, a hybrid matrix factorization recommendation algorithm based on item similarity is proposed. First of all, Worde2vec is used to mine the content information of items in user’s comment data to get the content similarity of items; secondly, the attribute similarity of items is calculated according to the attribute information of items; then, the content similarity and attribute similarity are fused to get the final item similarity; finally, the similarity of item is integrated into the objective function of matrix factorization algorithm. The experimental results show that the hybrid recommendation algorithm has higher accuracy, recall and coverage than the traditional matrix factorization recommendation algorithm.