Open AccessHarmonic attentive multimodal neural network for movie recommendation
Author(s) -
J. X. Wang,
Yongjing Guo,
Tao Qi,
Qili Tang
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 - 10.1088/1742-6596/1550/2/022018
Subject(s) - computer science , artificial neural network , perspective (graphical) , preference , artificial intelligence , harmonic , deep neural networks , machine learning , mathematics , statistics , physics , quantum mechanics
A multimodal neural network based on harmonic self-attention was proposed for movie recommendation. This method can deal with the multi-source data and learn representations of users and items well. The multimodal neural network mainly consists of three sub-networks, ResNet, Bert, and LSTM. In addition, the harmonic self-attention mechanism can explore the user’s preference from the perspective of time. Experimental results show that compared with other three latest recommendation methods, HSMNN shows better performance in terms of HR and NDCG.