Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (13): 172-180.DOI: 10.3778/j.issn.1002-8331.1907-0294

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Hybrid Recommendation System Based on Self-Attention Model

TAN Taizhe, YAN Jiabin   

  1. Faculty of Computer, Guangdong University of Technology, Guangzhou 510006, China
  • Online:2020-07-01 Published:2020-07-02



  1. 广东工业大学 计算机学院,广州 510006


The recommendation system is becoming popular as an important way to solve the problem of information overload in the era of information explosion. Traditional recommendation systems generally have shortcomings such as low precision and unclear evaluation criteria. Introducing machine learning, especially deep learning techniques, into the recommendation system will effectively improve the above defects and bottlenecks. A hybrid recommendation system based on self-attention model is proposed. Firstly, the attention model in deep neural network is used to weight the item attributes of specific recommended products to providing user recognition degree of pre-recommended products. Then, the traditional loss ranking model is replaced by Ada-Boosting model, which makes the related evaluation indicators such as accuracy and recall rate greatly improved. Finally, based on the existing recommendation system evaluation indicators, a new evaluation criteria called ARD is introduced for the first time. By evaluating ARD, the recommendation system performance can be more accurately evaluated in the user experience dimension.

Key words: self-attention model, Ada-Boosting, collaborative filtering, hybrid recommendation



关键词: 注意力模型, 自适应增强, 协同过滤, 混合推荐