Journal
INFORMATION SCIENCES
Volume 595, Issue -, Pages 217-230Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2022.02.050
Keywords
Recommender systems; Matrix factorization; Lateral connections; Deep neural network
Categories
Funding
- National Natural Science Foundation of China [61672252]
- Fundamental Research Funds for the Central Universities [2019kfyXKJC021]
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The recommender system is crucial in dealing with data explosion, and the application of deep neural networks has become a popular research topic. Methods like SDNN and DualCF have improved the efficiency of capturing user-item relations in recommendation systems, and their effectiveness has been verified through extensive experiments.
The recommender system is the key approach to alleviate the data explosion problem. Recently, with the rapid development of deep learning, there are several researches of employing deep neural networks (DNNs) on recommender systems. Most of these methods tend to capture the complex mapping relations between user-item representation and matching score via DNNs. These methods are mainly a pyramid structure which maps relations into low-dimensional space and then predicts the result by logistic regression. However, partial relations may be linearly indivisible in low-dimensional space. As we know, data that are hard to be separated in low-dimensional space can become much easier after being mapped into a high-dimensional space. Hence, motivated by the ladder network, we propose a Symmetric Deep Neural Networks (SDNN) with lateral connections, which can learn relations in both high-dimensional and low-dimensional spaces simultaneously. Moreover, considering that deep neural network is very inefficient in catching low-rank relations between users and items, we further combine SDNN with an improved deep matrix factorization model into a unified framework, and name this new model DualCF. Extensive experiments on three benchmark datasets are conducted and the results verify the effectiveness of SDNN and DualCF over state-of-the-art models for implicit feedback prediction. (C) 2022 Elsevier Inc. All rights reserved.
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