Abstract
Most existing graph neural network (GNN)-based knowledge-aware recommendation models rely on handcrafted feature engineering and do not allow for end-to-end training. As a state-of-the-art end-to-end framework, the Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (KGNN-LS) model can extend GNNs architecture to knowledge graphs to simultaneously capture semantic relations between entities as well as personalized user preferences for entities/items, thereby making effective recommendation. However, we believe that KGNN-LS still has two weaknesses: (1) In KGNN-LS, the weights of the edges in the graph are determined solely by user preferences for relations without considering user’s (potential) personalized interests in entities/items. (2) The sum pooling adopted by KGNN-LS cannot effectively aggregate the most representative information of the neighborhood. In this paper, we propose the improved Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (iKGNN-LS) model, which makes two improvements to KGNN-LS: (1) In iKGNN-LS, by introducing user-specific entity scoring functions, the edge weights are determined jointly by personalized user preferences for relations and for entities. (2) iKGNN-LS uses max pooling instead of sum pooling for neighborhood aggregation. Top-N recommendation experiments on three datasets show that iKGNN-LS outperforms KGNN-LS in terms of Precision@N, Recall@N, and F1-measure@N.
Keywords
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Aggarwal, C.C.: Evaluating recommender systems. Recommender Systems, pp. 225–254. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_7
Cao, Y., Wang, X., He, X., Hu, Z., Chua, T.: Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. In: Proceedings of the World Wide Web Conference, WWW 2019, pp. 151–161. ACM (2019). https://doi.org/10.1145/3308558.3313705
Guo, Q., Zhuang, F., Qin, C., et al.: A survey on knowledge graph-based recommender systems. CoRR abs/2003.00911 (2020). https://arxiv.org/abs/2003.00911
Ma, W., Zhang, M., Cao, Y., et al.: Jointly learning explainable rules for recommendation with knowledge graph. In: Proceedings of the World Wide Web Conference, WWW 2019, pp. 1210–1221. ACM (2019). https://doi.org/10.1609/aaai.v33i01.33015329
Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104(1), 11–33 (2016). https://doi.org/10.1109/JPROC.2015.2483592
Niepert, M., Ahmed, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, JMLR Workshop and Conference Proceedings, vol. 48, pp. 2014–2023. JMLR.org (2016). http://proceedings.mlr.press/v48/niepert16.html
Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: Proceedings of the World Wide Web Conference, WWW 2019 pp. 3307–3313. ACM (2019). https://doi.org/10.1145/3308558.3313417
Wang, H., Zhang, F., Zhang, M., et al.: Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 968–977. ACM (2019). https://doi.org/10.1145/3292500.3330836
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 950–958. ACM (2019). https://doi.org/10.1145/3292500.3330989
Xian, Y., Fu, Z., Muthukrishnan, S., Melo, G., Zhang, Y.: Reinforcement knowledge graph reasoning for explainable recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 285–294. ACM (2019). https://doi.org/10.1145/3331184.3331203
Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: Proceedings of the 7th International Conference on Learning Representations, ICLR 2019. OpenReview.net (2019). https://openreview.net/forum?id=ryGs6iA5Km
Xu, W., Xu, Z., Ye, L.: Computing user similarity by combining item ratings and background knowledge from linked open data. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 467–478. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_43
Ye, Y., Wang, X., Yao, J., et al.: Bayes EMbedding (BEM): refining representation by integrating knowledge graphs and behavior-specific networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 679–688. ACM (2019). https://doi.org/10.1145/3357384.3358014
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Zhao, B., Xu, Z., Tang, Y., Li, J., Liu, B., Tian, H. (2020). Effective Knowledge-Aware Recommendation via Graph Convolutional Networks. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_9
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