A Deep Dual Adversarial Network for Cross-domain Recommendation
- Publisher:
- Institute of Electrical and Electronics Engineers
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Knowledge and Data Engineering, 2022, PP, (99), pp. 1-1
- Issue Date:
- 2022-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| A_Deep_Dual_Adversarial_Network_for_Cross-Domain_Recommendation.pdf | Published version | 1.74 MB |
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Data sparsity is a common issue for most recommender systems and can severely degrade the usefulness of a system. One of the most successful solutions has been cross-domain recommender systems, which supplement the sparse data of the target domain with knowledge transferred from a source domain rich with data that is in some way related. However, there are three challenges that, if overcome, could significantly improve the quality and accuracy of cross-domain recommendation: 1) ensuring latent feature spaces of the users and items are both maximally matched; 2) taking consideration of user-item relationship and their interaction in modelling user preference; 3) enabling a two-way cross-domain recommendation that both the source and the target domains benefit from a knowledge exchange. Hence, in this paper, we propose a novel deep neural network called Dual Adversarial network for Cross-Domain Recommendation. By training the shared encoders with a domain discriminator via dual adversarial learning, the latent feature spaces for both the users and items are maximally matched. Allowing the two domains to collaboratively benefit from each other results in better recommendations for both domains. Extensive experiments with real-world datasets on six tasks demonstrate that DA-CDR significantly outperforms seven state-of-the-art baselines.
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