Abstract
With the rapid development of recommendation system, various side information has been utilized to remedy data sparsity and cold start problem. Social recommendation performs by modeling social information which brings high-order information beyond user-item interaction. However, existing works relay on GNN based social network embedding which may lead to over-smoothing problem. The process of graph diffusion encodes high-order feature also takes much noise into the model. We argue that the latent influence of social relations cannot be well captured which had not be well addressed in previous work. In this work, we propose a new recommendation framework named adversarial learning enhanced social influence graph neural network (SI-GAN) that can inherently fuses the adversarial learning enhanced social network feature and graph interaction feature. Specifically, we propose an interest-wise influence diffusion network which modeling the user-item interaction and learning the embedding of users and items through influence diffusion. We adopt the embedding of user by both interaction information and adversarial learning enhanced social network which are efficiently fused by feature fusion model. We utilize the structure of adversarial network to address the problem of over-smoothing and digging the latent feature representation. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed model.
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Acknowledgment
This work was supported by the National Key R &D Program of China [2022YFF0902703].
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Wang, J., Li, H., Mo, T., Li, W. (2023). Adversarial Learning Enhanced Social Interest Diffusion Model for Recommendation. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_24
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DOI: https://doi.org/10.1007/978-3-031-30672-3_24
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