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A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis
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Author(s): Minghu Wu (Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China), Songnan Lv (Hubei University of technology, Wuhan, China), Chunyan Zeng (Hubei University of technology, Wuhan, China), Zhifeng Wang (Central China Normal University, Wuhan, China), Nan Zhao (Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China), Li Zhu (Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China), Juan Wang (Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar Energy, Hubei University of Technology, Wuhan, China)and Ming Wu (Hubei University of technology, Wuhan, China)
Copyright: 2020
Volume: 11
Issue: 1
Pages: 16
Source title:
International Journal of Mobile Computing and Multimedia Communications (IJMCMC)
Editor(s)-in-Chief: Agustinus Waluyo (Monash University, Australia)
DOI: 10.4018/IJMCMC.2020010103
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Abstract
With the development of the Internet and the popularity of Big Data, recommender systems have become an indispensable field due to its excellent ability to solve the problem of information overload. The existing recommender system mainly uses collaborative filtering to make recommendation by mining the interaction relationship between users and items. In order to better analyze the interaction relationship between users and items, a hybrid recommender method based on multiple dimension attention analysis is proposed. The idea is to fuse the embedded vectors of users and items into mapping vectors (or matrices) of different shapes through different methods, and learn the interactive relationship between users and items through the neural network model with attention mechanism. Experimental results show that compared with traditional analytical methods, multiple dimension analysis can more comprehensively explore the interaction between users and items, and the attention mechanism can greatly improve the analytical ability of the model.
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