Abstract
Knowledge Graph Recommendation (KGR), which aims to incorporate Knowledge Graphs (KGs) as auxiliary information into recommender systems and effectively improve model performance, has attracted considerable interest. Currently, KGR community has focused on designing Graph Neural Networks (GNNs)-based end-to-end KGR models. Unfortunately, existing GNNs-based KGR models are focused on extracting high-order attributes (knowledge) but suffer from restrictions in several vital aspects, such as 1) neglect of finer-grained feature interaction information via GNNs and 2) lack of adequate supervised signals, leading to undesirable performance. To tackle these gaps, we propose a novel Dual-view Self-supervised Co-training for Knowledge Graph Recommendation (DSCKG). We consider two different views, covering user-item collaborative view and KGs structural view. Precisely, for the collaborative view, we first extract high-order collaborative user/item representations with GNNs. Next, we impose a discrepancy regularization term to augment the self-discrimination of the user/item representations. As for the structural view, we initially utilize GNNs to extract high-order features. Next, we utilize novel Dual-core Convolutional Neural Networks to extract bit- and vector-level finer-grained feature interaction signals. DSCKG hence performs a high-quality self-supervised co-training paradigm across dual views, improving the node representation learning capability. Experimental results demonstrate DSCKG achieves remarkable improvements over SOTA methods.
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References
Gao, Z., Ma, H., Zhang, X., Wu, Z., Li, Z.: Co-contrastive self-supervised learning for drug-disease association prediction. In: Proceedings of PRICAI (2022)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of SIGIR (2020)
Jiang, Y., Ma, H., Zhang, X., Li, Z., Chang, L.: An effective two-way metapath encoder over heterogeneous information network for recommendation. In: Proceedings of ICMR (2022)
Li, Q., Ma, H., Zhang, R., Jin, W., Li, Z.: Co-contrastive learning for multi-behavior recommendation. In: Proceedings of PRICAI (2022)
Ma, Y., He, Y., Zhang, A., Wang, X., Chua, T.-S.: CrossCBR: cross-view contrastive learning for bundle recommendation. In: Proceedings of SIGKDD (2022)
van den Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv:1807.03748 (2018)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of WSDM (2018)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of CIKM (2018)
Wang, H., et al.: Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of SIGKDD (2019)
Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: Proceedings of WWW (2019)
Wang, W., Ma, H., Zhao, Y., Li, Z., He, X.: Tracking knowledge proficiency of students with calibrated q-matrix. Exp. Syst. Appl. (2022)
Wang, W., Ma, H., Zhao, Y., Yang, F., Chang, L.: SEEP: semantic-enhanced question embeddings pre-training for improving knowledge tracing. Inf. Sci. (2022)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: Knowledge graph attention network for recommendation. In: Proceedings of WWW (2019)
Wang, X., et al.: Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference (2021)
Wang, Z., Lin, G., Tan, H., Chen, Q., Liu, X.: CKAN: collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of SIGIR (2020)
Wei, Y., Ma, H., Zhang, R., Li, Z., Chang, L.: Exploring implicit relationships in social network for recommendation systems. In: Proceedings of PAKDD (2021)
Wu, J., et al.: Self-supervised graph learning for recommendation. In: Proceedings of SIGIR (2021)
Xia, X., Yin, H., Yu, J., Shao, Y., Cui, L.: Self-supervised graph co-training for session-based recommendation. In: Proceedings of CIKM (2021)
Yu, X., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of WSDM (2014)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of SIGKDD (2016)
Zhang, R., Ma, H., Li, Q., Wang, Y., Li, Z.:. Fire: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks. Appl. Intell. (2022)
Zhang, X., Ma, H., Gao, Z., Li, Z., Chang, L.: Exploiting cross-session information for knowledge-aware session-based recommendation via graph attention networks. Int. J. Intell. Syst. (2022)
Zhang, X., Ma, H., Yang, F., Li, Z., Chang, L.: Cross-view contrastive learning for knowledge-aware session-based recommendation. In: Proceedings of PRICAI (2022)
Acknowledgement
This work is supported by the Industrial Support Project of Gansu Colleges (2022CYZC-11), the Gansu Natural Science Foundation Project (21JR7RA114), the National Natural Science Foundation of China (62276073, 61762078) and NWNU Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2). NWNU Graduate Research Project Funding Program (2021KYZZ02103).
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Zhang, R., Ma, H., Li, Q., Wang, Y., Li, Z. (2023). Dual-View Self-supervised Co-training for Knowledge Graph 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_8
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