Abstract
Incorporating a knowledge graph (KG) into recommender systems has been widely studied by researchers. Existing methods mostly extract the paths connecting user-item pairs and model these paths or iteratively propagate the user preference over the KG. These methods can capture the semantics of entities and relations well and help to comprehend users’ interests. However, these methods ignore the implicit features between the KG’s external items and thus cannot fully capture the users’ preferences. To solve this problem, we propose a novel model named E nhanced K nowledge-aware P ath N etwork (EKPN) to exploit the KG and capture implicit features between items outside the KG for recommendation. EKPN consists of two neural network modules: one module captures explicit features between items in the knowledge graph by automatically generating paths from users to candidate items for recommendation; the other module explores implicit features between items outside the knowledge graph by utilizing users’ historical interactions. To better capture the implicit features between items outside the knowledge graph, we propose the activation gate mechanism. Finally, we use a fusion mechanism to combine the two modules to enhance each other and achieve higher performance. Extensive validation on two real-world datasets shows the superiority of EKPN over baselines.
Similar content being viewed by others
References
Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 635–644
Cheng H-T, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide & deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp 7–10
Guo H, Tang R, Ye Y, Li Z, He X (2017) Deepfm: A factorization-machine based neural network for CTR prediction. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp 1725–1731
Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G (2018) xdeepfm: Combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 1754–1763
Zhang F, Yuan N J, Lian D, Xie X, Ma W-Y (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, pp 353–362
Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp 2181–2187
Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) 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
Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: The World Wide Web Conference, pp 3307–3313
Wang H, Zhang F, Zhang M, Leskovec J, Zhao M, Li W, Wang Z (2019) 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, pp 968–977
Hamilton W L, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, pp 1024–1034
Wang X, He X, Cao Y, Liu M, Chua T-S (2019) KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 950–958
Velickovic P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: 6th International Conference on Learning Representations
Shi C, Zhang Z, Luo P, Yu P S, Yue Y, Wu B (2015) Semantic path based personalized recommendation on weighted heterogeneous information networks. In: Proceedings of the 24th ACM International Conference on Information and Knowledge Management, pp 453–462
Sun Z, Yang J, Zhang J, Bozzon A, Huang L-K, Xu C (2018) Recurrent knowledge graph embedding for effective recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp 297–305
Wang X, Wang D, Xu C, He X, Cao Y, Chua T-S (2019) Explainable reasoning over knowledge graphs for recommendation. In: The Thirty-Third AAAI Conference on Artificial Intelligence, pp 5329–5336
Xue H-J, Dai X, Zhang J, Huang S, Chen J (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp 3203–3209
Zhang L, Liu P, Gulla J A (2018) A deep joint network for session-based news recommendations with contextual augmentation. In: Proceedings of the 29th on Hypertext and Social Media, pp 201–209
Morales G D F, Gionis A, Lucchese C (2012) From chatter to headlines: harnessing the real-time web for personalized news recommendation. In: Proceedings of the Fifth International Conference on Web Search and Web Data Mining, pp 153–162
Bordes A, Usunier N, García-Durán A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013, pp 2787–2795
Chaudhari S, Azaria A, Mitchell T M (2017) An entity graph based recommender system. AI Commun 30(2):141–149
Sun Y, Han J, Yan X, Yu P S, Wu T (2011) Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endow 4(11):992–1003
He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp 173–182
Erhan D, Bengio Y, Courville A C, Manzagol P-A, Vincent P, Bengio S (2010) Why does unsupervised pre-training help deep learning?. J Mach Learn Res 11:625–660
Catherine R, Cohen W W (2016) Personalized recommendations using knowledge graphs: A probabilistic logic programming approach. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp 325–332
Bayer I, He X, Kanagal B, Rendle S (2017) A generic coordinate descent framework for learning from implicit feedback. In: Proceedings of the 26th International Conference on World Wide Web, pp 1341–1350
He X, Zhang H, Kan M-Y, Chua T-S (2016) Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp 549–558
Järvelin K, Kekäläinen J (2017) IR evaluation methods for retrieving highly relevant documents. SIGIR Forum 51(2):243–250
Kingma D P, Ba J (2015) Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Vol. 9 of JMLR Proceedings, pp 249–256
He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 355–364
Ai Q, Azizi V, Chen X, Zhang Y (2018) Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11(9):137
Wang Z, Lin G, Tan H, Chen Q, Liu X (2020) CKAN: collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval. ACM, pp 219–228
Acknowledgments
This work was supported in part by the Consulting Project of Chinese Academy of Engineering under Grant 2020-XY-5, 2018-XY-07, and in part by the Fundamental Research Funds for the Central Universities under Grant 2242021S30009, the Collaborative Innovation Center of Novel Software Technology and Industrialization.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, P., Ai, C., Yao, Y. et al. EKPN: enhanced knowledge-aware path network for recommendation. Appl Intell 52, 9308–9319 (2022). https://doi.org/10.1007/s10489-021-02758-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-021-02758-9