A survey of recommendation systems based on deep learning. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Liu, Baichuan; Zeng, Qingtao; Lu, Likun; Li, Yeli; You, Fucheng
• Is Part Of: Journal of physics. Volume 1754:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1754/1/012148
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Faced with massive amounts of data, people may not be able to choose the items they like. The recommendation system came into being, and it has achieved a breakthrough for a long time. Using deep learning can mine the hidden attributes of users' items and integrate them well, bringing new changes to the recommendation system. This article describes the deep learning-based recommendation system and the traditional recommendation system, and analyzes their advantages and disadvantages.