Abstract
To achieve more accurate recommendations, a consensus of the research community is that not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., side information) should be utilized. Generally, both explicit and implicit information can be categorized according to the following assumptions: 1) Users with same behaviors are similar; 2) Items related to the same user are similar; 3) Items with same attributes are similar; and 4) Users with same interests are similar. However, none of existing studies has fully explored such information. To this end, we put forward Multi-dimension Interactions based Knowledge Graph Neural Networks (MI-KGNN), i.e., a GNN-based recommendation model that characterizes the similarity between users and items through embedding propagation in the knowledge graph. Specifically, apart from the traditional user-item and item-user interactions, we define another two types of interactions by introducing three different bipartite graphs. On one hand, we explore the interaction between items and the neighborhood during the information aggregation process. On the other hand, we explore the interaction between users and the neighborhood during embedding propagation. These interactions allow information to propagate in the direction indicated by the above four assumptions. In such a way, MI-KGNN effectively extracts both semantic information and structural information in the knowledge graph. Experimental results show that MI-KGNN significantly outperforms state-of-the-art methods in top-K recommendations.
This work is partially supported by the National Natural Science Foundation of China (No. 61725205, 617772428), and the Fundamental Research Funds for the Central Universities (No. 3102019AX10).
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References
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)
Cao, Y., Wang, X., He, X., Hu, Z., Chua, T.S.: Unifying knowledge graph learning and recommendation: towards a better understanding of user preferences. In: The World Wide Web Conference, pp. 151–161. ACM (2019)
Catherine, R., Cohen, W.: Personalized recommendations using knowledge graphs: a probabilistic logic programming approach. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 325–332. ACM (2016)
Cheng, H.T., et al.: Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 7–10. ACM (2016)
Cheng, Z., Ding, Y., Zhu, L., Kankanhalli, M.: Aspect-aware latent factor model: rating prediction with ratings and reviews. In: Proceedings of the 2018 World Wide Web Conference, pp. 639–648. International World Wide Web Conferences Steering Committee (2018)
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. International World Wide Web Conferences Steering Committee (2017)
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, pp. 1531–1540. ACM (2018)
Hu, L., Jian, S., Cao, L., Chen, Q.: Interpretable recommendation via attraction modeling: learning multilevel attractiveness over multimodal movie contents. In: IJCAI, pp. 3400–3406 (2018)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM (2008)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Ma, W., et al.: Jointly learning explainable rules for recommendation with knowledge graph. In: The World Wide Web Conference, pp. 1210–1221. ACM (2019)
McInerney, J., et al.: Explore, exploit, and explain: personalizing explainable recommendations with bandits. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 31–39. ACM (2018)
Monti, F., Bronstein, M., Bresson, X.: Geometric matrix completion with recurrent multi-graph neural networks. In: Advances in Neural Information Processing Systems, pp. 3697–3707 (2017)
Nandanwar, S., Moroney, A., Murty, M.N.: Fusing diversity in recommendations in heterogeneous information networks. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 414–422. ACM (2018)
Palumbo, E., Rizzo, G., Troncy, R.: Entity2rec: learning user-item relatedness from knowledge graphs for top-n item recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 32–36. ACM (2017)
Rendle, S.: Factorization machines with LIBFM. ACM Trans. Intell. Syst. Technol. (TIST) 3(3), 57 (2012)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J., et al.: Item-based collaborative filtering recommendation algorithms. In: WWW, vol. 1, pp. 285–295 (2001)
Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 453–462. ACM (2015)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)
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, pp. 417–426. ACM (2018)
Wang, H., et al.: Knowledge graph convolutional networks for recommender systems with label smoothness regularization. arXiv preprint arXiv:1905.04413 (2019)
Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference, pp. 3307–3313. ACM (2019)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. arXiv preprint arXiv:1905.07854 (2019)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. arXiv preprint arXiv:1905.08108 (2019)
Wu, Y., Liu, H., Yang, Y.: Graph convolutional matrix completion for bipartite edge prediction. In: KDIR, pp. 49–58 (2018)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983. ACM (2018)
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, pp. 283–292. ACM (2014)
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, pp. 353–362. ACM (2016)
Zhang, Y., Yin, H., Huang, Z., Du, X., Yang, G., Lian, D.: Discrete deep learning for fast content-aware recommendation. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 717–726. ACM (2018)
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, pp. 635–644. ACM (2017)
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Wang, Z., Wang, Z., Yu, Z., Guo, B., Zhou, X. (2020). MI-KGNN: Exploring Multi-dimension Interactions for Recommendation Based on Knowledge Graph Neural Networks. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_13
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