Hybrid Recommendation Based on Matrix Factorization and Deep Learning
Authors: 
Wei Song, 
Cong WangAuthors Info & Claims
Wei Song, Cong WangAuthors Info & ClaimsPages 81 - 85
Abstract
Deep learning (DL) is playing an increasingly important role in the field of recommender systems (RSs). In this paper, we enhance the performance of a DL-based RS by incorporating matrix factorization (MF), which gained a great deal of popularity as a result of the Netflix Prize competition. Thus, DL is responsible for learning the nonlinear relationship between users and items, whereas MF is used to describe the linear relationship between users and items. We use the typical DL architecture of the multilayer perceptron, and use layer normalization and the residual to improve its performance. Our experimental results showed that the proposed method can make recommendations accurately.
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- Hybrid Recommendation Based on Matrix Factorization and Deep Learning
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Published In
May 2022
139 pages
ISBN:9781450395632
DOI:10.1145/3538950
Copyright © 2022 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 July 2022
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- Refereed limited
Funding Sources
- National Natural Science Foundation of China
- Great Wall Scholar Program
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BDE 2022
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