Abstract
Knowledge graphs (KGs) have proven to be effective to improve the performance of recommendation. However, with the tremendous increase of users and items, existing methods still face several challenging problems: (1) path-based methods rely heavily on manually designed meta-path; (2) embedding-based methods lack sufficient considerations of user personality. To overcome the shortcomings of previous works, we propose a novel model, named KCER, short for leveraging Knowledge Context to Enhance Recommendation. Firstly, KCER generates the representation of knowledge context associating with specific user-item pairs. Then to obtain enriched user representations, we leverage a gated attention network to extracted meaningful information from the associated knowledge context and user dedicated ID embedding. We conduct extensive experiments on three real-world datasets to evaluate the model. The experimental results show the superiority of KCER compared with other state-of-the-art methods.
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This work is supported by the National Key Research and Development Program of China.
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Wang, J., Kou, Y., Zhang, Y., Gao, N., Tu, C. (2020). Leveraging Knowledge Context Information to Enhance Personalized Recommendation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_39
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