Abstract
The performance of recommender systems can be improved effectively by using knowledge graphs as auxiliary information. However, most of the knowledge graph-based recommendations focus on learning item representations in knowledge graphs, capture the collaborative signals between user interactions inadequately. The user-item bipartite graph contains explicit preference information of users, and the collaborative signals of user-item interactions help to enhance representations of users. A user interaction-aware knowledge graph recommendation model (UIKR) is proposed, which enhances user representation and introduces the higher-order collaborative signals in user interactions into the representation learning of items in knowledge graphs. Specifically, the high-order collaborative signals hidden in the user-item bipartite graph are captured to strengthen user representations. Then, the enhanced user representation is applied to the representation learning of items in knowledge graphs. A hybrid attention function is proposed to aggregate neighbor representation of items, which augments the propagation of user preferences in knowledge graphs and helps to learn personalized item representations. Finally, the user interaction-aware item representations and the enhanced user representations are used for recommendations. Extensive experiments are conducted on two standard datasets and the results show that proposed UIKR model outperforms current state-of-the-art baselines.
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Acknowledgements
This work is supported in part by the National Natural Science Foundation of China under Grants [62120106008, 61806065], and the Fundamental Research Funds for the Central Universities [JZ2022HGTB0239].
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Wang, R., Dong, B., Li, T., Wu, M., Bu, C., Wu, X. (2023). User Interaction-Aware Knowledge Graphs for Recommender Systems. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_2
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