Abstract
Cross-domain recommendation leverages a user’s historical interactions in the auxiliary domain to suggest items within the target domain, particularly for cold-start users with no prior activity in the target domain. Existing cross-domain recommendation models often overlook key aspects such as the complexities of transferring user interests between domains and the biases inherent in user behavior patterns. In contrast, our Extract-Map-Predict Neural Network Architecture (EMPNet) employs a disentanglement approach to map fine-grained user interests and utilize the biases inherent in the cross-domain recommendation. In feature extraction, we use the Bidirectional Encoder Representations from Transformers (BERT) and Identity-Enhanced Multi-Head Attention Mechanism to obtain the user and item feature vectors. In cross-domain user mapping, we disentangle the user feature vector into domain-shared and domain-specific interests for fine-grained cross-domain mapping to obtain the feature vector of cold-start users in the target domain. In rating prediction, we design a biased Attentional Factorization Machine (AFM) to utilize biases extracted from user and item features. We experimentally evaluate EMPNet on the Amazon dataset. The results show that it clearly outperforms the selected baselines.
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Notes
We skip RC-DFM as it requires extra data input.
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Funding
This work was supported in part by Beijing Natural Science Foundation(Grant No.L233034), in part by Zhejiang Lab Open Research Project (Grant No.K2022KG0AB03), in part by the National Natural Science Foundation of China (Grant No.62306287, No.62002027, No.62006023), in part by the National Key R &D Program of China (Grant No.2022YFE0137800), in part by Open Fund (DGERA 20231101) of Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources, Chengdu University of Technology, in part by Zhejiang Provincial Natural Science Foundation of China (Grant No.LY23F020012), in part by CCF-Zhipu AI Large Model Fund (Grant No. CCF-Zhipu202317), in part by SMP-IDATA Open Youth Fund (No.SMP2023-iData-005), in part by the Fundamental Research Funds for the Central Universities (Grant No.2023RC08, No.21623402), in part by the Open Project of Xiangjiang Laboratory (No.23XJ03006) and in part by the Open Projects of the Technology Innovation Center of Cultural Tourism Big Data of Hebei Province (Grant No.SG2019036-zd202205).
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Jinpeng Chen wrote the main manuscript text and provided the methodology and funding support. Fan Zhang carried out the experiments. Huan Li and Hua Lu conceived the study and participated in methodology design and coordination. Xiongnan Jin and Kuien Liu helped draft the manuscript. Hongjun Li and Yongheng Wang provided writing review and editing and prepared all figures and tables.
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Chen, J., Zhang, F., Li, H. et al. EMPNet: An extract-map-predict neural network architecture for cross-domain recommendation. World Wide Web 27, 12 (2024). https://doi.org/10.1007/s11280-024-01240-z
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DOI: https://doi.org/10.1007/s11280-024-01240-z