Abstract
Social recommendation can effectively improve recommendation performance by leveraging social relationships to alleviate the sparsity of user-item interaction data. Because these connections in social recommendation can be easily represented as graph-structured data, social recommendation based on graph neural networks has received significant attention. However, existing works focus on modeling the long-term preferences of users and rarely consider the effect of temporal factors on preferences, resulting in a failure to accurately learn the representation of present preferences. Moreover, existing works mainly utilize similarity to connect different items. But items in the same category often have more connections and correlations with each other, which can be employed to enhance the learning of item representations. Therefore, this work proposes DLREM (Deep Latent Representation Enhancement Method for Social Recommendation) to address the above limitations. Specifically, DLREM exploits dual graph attention networks to learn long-term representations of users and items separately and exploits recurrent neural networks to capture the dynamic preferences of users. In addition, attention mechanisms are used to model user social relationships and item correlations, enhancing the learning of user and item representations. Combining the enhanced deep latent representations of users and items can improve the accuracy of social recommendation. Experimental results on two public datasets show that our model achieves competitive performance compared with state-of-the-art models.
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Ciao and Epinions are openly available datasets and can be downloaded from http://www.cse.msu.edu/~tangjili/trust.html.
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Ciao and Epinions available from http://www.cse.msu.edu/~tangjili/trust.html.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 62272290, 62172088), and Shanghai Natural Science Foundation (No. 21ZR1400400).
Funding
National Natural Science Foundation of China (No. 62272290, 62172088), Shanghai Natural Science Foundation (No. 21ZR1400400).
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All authors contributed to the study conception and design. The main manuscript text was written by Xiaoyu Hou and Bofeng Zhang. The key code was written by Xiaoyu Hou and Guobing Zou. The experiments were conducted by Xiaoyu Hou and Guobing Zou. The data analysis was conceived and designed by Sen Niu. All authors read and approved the final manuscript.
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Hou, X., Zou, G., Zhang, B. et al. Deep latent representation enhancement method for social recommendation. J Intell Inf Syst 62, 57–75 (2024). https://doi.org/10.1007/s10844-023-00802-3
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DOI: https://doi.org/10.1007/s10844-023-00802-3