Abstract
As the knowledge graph can provide items with rich attributes, it has become an important way to alleviate cold start and sparsity in recommender systems. Recently, some knowledge graph based collaborative filtering methods use graph convolution networks to aggregate information from each item’s neighbors to capture the semantic relatedness, and significantly outperform the state-of-the-art methods. However, in the process of knowledge graph convolution, only the item nodes can make use of knowledge, while the user nodes only contain the original ID information. This gap in information modeling makes it difficult for prediction function to capture the user preference for high-order attribute nodes in knowledge graph, which leads to the introduction of noise data. In order to give full play to the ability of knowledge graph convolution in mining high-order knowledge, we propose Multi-Stage Knowledge Propagation Networks (MSKPN), an end-to-end recommender framework which combines the graph convolution on both knowledge graph and user-item graph. It uses the collaborative signal latent in user-item interactions to build an information propagation channel between the user nodes and item nodes, so as to complement user representations. We conduct extensive experiments on two public datasets, demonstrating that our MSKPN model significantly outperforms other state-of-the-art models. Further analyses are provided to verify the rationality of our model.
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References
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. WWW 2017, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052569
Xue, F., He, X., Wang, X., Xu, J., Liu, K., Hong, R.: Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Inf. Syst. 37(3), 1–25 (2019). https://doi.org/10.1145/3314578
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM (2016). https://doi.org/10.1145/2988450.2988454
Elkahky, A.M., Song, Y., He, X.: A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee (2015). https://doi.org/10.1145/2736277.2741667
Catherine, R., Cohen, W.: Personalized recommendations using knowledge graphs: a probabilistic logic programming approach. In: Proceedings of the 10th ACM Conference on Recommender Systems. ACM, September 2016. https://doi.org/10.1145/2959100.2959131
Yu, X., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM, February 2014. https://doi.org/10.1145/2556195.2556259
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Bonet, B., Koenig, S. (eds.) AAAI. pp. 2181–2187. AAAI Press (2015)
Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, August 2016. https://doi.org/10.1145/2939672.2939673
Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, October 2018. https://doi.org/10.1145/3269206.3271739
Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.S.: Explainable reasoning over knowledge graphs for recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5329–5336, July 2019. https://doi.org/10.1609/aaai.v33i01.33015329
Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference on WWW 2019. ACM Press (2019). https://doi.org/10.1145/3308558.3313417
Wang, H., 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. ACM, July 2019. https://doi.org/10.1145/3292500.3330836
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, July 2019. https://doi.org/10.1145/3331184.3331267
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (Poster). OpenReview.net (2017)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2008. ACM Press (2008). https://doi.org/10.1145/1401890.1401944
Rendle, S.: Factorization machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 1–22 (2012). https://doi.org/10.1145/2168752.2168771
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, August 2013. https://doi.org/10.1145/2487575.2487589
He, X., He, Z., Song, J., Liu, Z., Jiang, Y.G., Chua, T.S.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30(12), 2354–2366 (2018). https://doi.org/10.1109/tkde.2018.2831682
Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2017). https://doi.org/10.1145/3097983.3098063
Hu, B., Shi, C., Zhao, W.X., Yu, P.S.: Leveraging meta-path based context for top- n recommendation with a neural co-attention model. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, July 2018. https://doi.org/10.1145/3219819.3219965
Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2019). https://doi.org/10.1109/tkde.2018.2833443
Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017). https://doi.org/10.1109/tkde.2017.2754499
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Xue, F., Zhou, W., Hong, Z., Liu, K. (2021). Multi-stage Knowledge Propagation Network for Recommendation. In: Qin, B., Jin, Z., Wang, H., Pan, J., Liu, Y., An, B. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction. CCKS 2021. Communications in Computer and Information Science, vol 1466. Springer, Singapore. https://doi.org/10.1007/978-981-16-6471-7_19
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