A Parallel Deep Neural Network Using Reviews and Item Metadata for Cross-domain Recommendation

Wenxing Hong, Nannan Zheng, Ziang Xiong, Zhiqiang Hu
2020 IEEE Access  
Cross-domain recommendation is an effective technique to alleviate the data sparsity problem in recommender systems by utilizing the information from relevant domains. In this paper, we propose Crossdomain Deep Neural Network (CD-DNN) for the cross-domain recommendation. CD-DNN solves the rating prediction problem by modeling users and items using reviews and item metadata, which jointly learns features of users and items from not only the target domain but also other source domains. Latent
more » ... ors for users and items are learned by several parallel neural networks, and the relevance of user features and item features is learned by maximizing prediction accuracy. CD-DNN builds a single mapping for user features in the latent space, so that the network for user is optimized together with item features from other domains. Experimental results indicate that the proposed CD-DNN significantly outperforms other state-of-the-art recommendation approaches on four public datasets of Amazon and it alleviates the data sparsity problem by leveraging more data across domains. INDEX TERMS Cross-domain recommendation, convolutional neural networks, rating prediction.
doi:10.1109/access.2020.2977123 fatcat:4lfqtfpt4jdplf2j66do2p325y