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A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis

A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis

Minghu Wu, Songnan Lv, Chunyan Zeng, Zhifeng Wang, Nan Zhao, Li Zhu, Juan Wang, Ming Wu
Copyright: © 2020 |Volume: 11 |Issue: 1 |Pages: 16
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781799805533|DOI: 10.4018/IJMCMC.2020010103
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MLA

Wu, Minghu, et al. "A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis." IJMCMC vol.11, no.1 2020: pp.42-57. http://doi.org/10.4018/IJMCMC.2020010103

APA

Wu, M., Lv, S., Zeng, C., Wang, Z., Zhao, N., Zhu, L., Wang, J., & Wu, M. (2020). A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 11(1), 42-57. http://doi.org/10.4018/IJMCMC.2020010103

Chicago

Wu, Minghu, et al. "A Hybrid Recommender Method Based on Multiple Dimension Attention Analysis," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 11, no.1: 42-57. http://doi.org/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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