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Cross-Domain Explicit-Implicit-Mixed Collaborative Filtering Neural Network
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AI
DOI：10.1109/TSMC.2021.3129261.png)
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Abstract
En 中文
Collaborative filtering (CF) is a classical model for recommender systems. Though neural network-based CF models have been shown to perform well in some cases, they still suffer from the sparsity and cold-start issues. To address these issues, we propose a novel neural network-based CF model, termed the cross-domain explicit-implicit-mixed CF neural network (CEICFNet). The proposed model utilizes deep neural networks to learn latent factors not only from the explicit ratings and the implicit interactions but also in a cross-domain manner. In particular, domain-shared multilayer perception (MLP) networks are designed to learn the user rating latent factors and the user interaction latent factors from the explicit ratings and the implicit interactions, respectively, which act as bridges for transferring knowledge across domains. On the other hand, domain-specific MLP networks are designed to learn the item rating latent factors and the item interaction latent factors from the explicit ratings and the implicit interactions, respectively. Then, in each domain, based on the user rating latent factors and the item rating latent factors, the rating predictive representation for each user-item pair can be learned by an MLP. Similarly, the interaction predictive representation for each user-item pair can be learned. For integrating the explicit ratings and the implicit interactions, in each domain, a fully connected layer is used to automatically assign different weights to the rating predictive representations and the interaction predictive representations, based on which the final interaction probability can be generated. Extensive experiments are conducted on five datasets and the results have confirmed the effectiveness of our model.
Keywords:
Recommender systems
Deep learning
Bayes methods
Couplings
Bridges
Scientific computing
Recurrent neural networks
Cross-domain
explicit-implicit-mixed
neural network
recommender system
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W