Abstract
Knowledge-aware recommendation has attracted increasing attention due to its wide application in alleviating data-sparse and cold-start, but the real-world knowledge graph (KG) contains many noises from irrelevant entities. Recently, contrastive learning, a self-supervised learning (SSL) method, has shown excellent anti-noise performance in recommendation task. However, the inconsistency between the use of noisy embeddings in SSL tasks and the original embeddings in recommendation tasks limits the model’s ability.
We propose a Multi-view Contrastive learning for Knowledge-aware Recommendation framework (MCKR) to solve the above problems. To remove inconsistencies, MCKR unifies the input of SSL and recommendation tasks and learns more representations from the contrastive learning method. To alleviate the noises from irrelevant entities, MCKR preprocesses the KG triples according to the type and randomly perturbs of graph structure with different weights. Then, a novel distance-based graph convolutional network is proposed to learn more reliable entity information in KG. Extensive experiments on three popular benchmark datasets present that our approach achieves state-of-the-art. Further analysis shows that MCKR also performs well in reducing data noise.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
See an empirical study in Sect. 5.3.
References
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597–1607. PMLR (2020)
Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9640–9649 (2021)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639–648 (2020)
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 (2018)
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long papers), pp. 687–696 (2015)
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)
Pujara, J., Augustine, E., Getoor, L.: Sparsity and noise: where knowledge graph embeddings fall short. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 1751–1756 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)
Sun, Z., Yang, J., Zhang, J., Bozzon, A., Huang, L.K., Xu, C.: Recurrent knowledge graph embedding for effective recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 297–305 (2018)
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 (2018)
Wang, H., Zhang, F., Xie, X., Guo, M.: DKN: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 1835–1844 (2018)
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 (2019)
Wang, X., He, X., Cao, Y., Liu, M., Chua, T.S.: KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 950–958 (2019)
Wang, X., et al.: Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference 2021, pp. 878–887 (2021)
Wu, J., et al.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726–735 (2021)
Xu, L., et al.: Recent advances in RecBole: extensions with more practical considerations (2022)
Yang, X., Zhang, X., Zhang, Z., Zhao, Y., Cui, R.: DTWSSE: data augmentation with a Siamese encoder for time series. In: U, L.H., Spaniol, M., Sakurai, Y., Chen, J. (eds.) Web and Big Data: 5th International Joint Conference, APWeb-WAIM 2021, Guangzhou, China, 23–25 August 2021, Proceedings, Part I 5. LNCS, vol. 12858, pp. 435–449. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85896-4_34
Yang, X., Zhang, Z., Cui, R.: TimeCLR: a self-supervised contrastive learning framework for univariate time series representation. Knowl.-Based Syst. 245, 108606 (2022)
Yang, Y., Huang, C., Xia, L., Li, C.: Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1434–1443 (2022)
Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., Nguyen, Q.V.H.: Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1294–1303 (2022)
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 (2016)
Zou, D., et al.: Multi-level cross-view contrastive learning for knowledge-aware recommender system. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1358–1368 (2022)
Acknowledgments
This work is jointly supported by National Natural Science Foundation of China (61877043) and National Natural Science of China (61877044).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yu, R., Li, Z., Zhao, M., Zhang, W., Yang, M., Yu, J. (2024). Multi-view Contrastive Learning for Knowledge-Aware Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14451. Springer, Singapore. https://doi.org/10.1007/978-981-99-8073-4_17
Download citation
DOI: https://doi.org/10.1007/978-981-99-8073-4_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8072-7
Online ISBN: 978-981-99-8073-4
eBook Packages: Computer ScienceComputer Science (R0)