Abstract
Incorporating knowledge graph (KG) for recommendation has been well considered in recent researches since it can alleviate the sparsity and cold-start problem of collaborative filtering. To capture the rich semantics of knowledge graph, existing KG-based models utilize graph neural networks (GNNs). However, we empirically find that the feature transformation and nonlinear activation designs in GNN contribute little to the recommendation performance. We propose simplified knowledge-aware attention network (SKAN) that simplifies the knowledge-aware aggregation by removing the two designs. To ensure the personalization during propagation, we apply weighted aggregation with user-specific attentions. We further aggregate the interacted items of users to enhance the user representation learning. We apply the proposed model on three real-world datasets, and the empirical results suggest that simplified knowledge-aware attention network (SKAN) significantly outperforms several compelling state-of-the-art baselines.
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Acknowledgement
This work was in part supported by NSFC under grant No. 61872446, and The Science and Technology Innovation Program of Hunan Province under grant No. 2020RC4046.
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Zhang, H., Chen, Y., Li, X., Zhao, X. (2022). Simplifying Knowledge-Aware Aggregation for Knowledge Graph Collaborative Filtering. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_5
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DOI: https://doi.org/10.1007/978-3-031-20309-1_5
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