Abstract
Knowledge-aware recommendation systems use knowledge graphs as side information to enhance the performance of recommendation systems. The existing methods often employ graph neural networks to process the relational networks and use contrastive learning to obtain more effective node representations. However, persistent challenges from active users’ noisy data and the cold-start problem related to inactive users impact model performance. Recent studies mainly use data augmentation methods to address these issues but only consider the enhancement during the training stage and neglect the pre-training stage. For this reason, we propose a Two-stage Enhancement for Recommendation Systems based on Contrastive Learning (TERSCL). This model primarily integrates two stages of data augmentation methods: one is the pre-training enhancement, where an enhanced adjacency matrix is obtained for subsequent training. The other generates a distinct dropout subgraph during each training round to participate in the subsequent graph encoding and contrastive learning process. Extensive experiments conducted on three benchmark datasets verify that TERSCL can significantly improve recommendation performance and mitigate the cold-start problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, X., Sun, Y., Zhou, T., Wen, Y., Zhang, F., Zeng, Q.: Recommending Online Course Resources Based on Knowledge Graph. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds.) WISA 2022. LNCS, vol 13579. Springer, Cham. (2022). https://doi.org/10.1007/978-3-031-20309-1_51
Abdollahpouri, H., Mansoury, M., Burke, R., Mobasher, B.: The unfairness of popularity bias in recommendation. ArXiv Preprint ArXiv:1907.13286. (2019)
Christakopoulou, E., Karypis, G.: Local item-item models for top-n recommendation. In: Proceedings Of The 10th ACM Conference On Recommender Systems, pp. 67-74 (2016)
Clauset, A., Shalizi, C., Newman, M.: Power-law distributions in empirical data. SIAM Rev. 51, 661–703 (2009)
Wang, W., Feng, F., He, X., Nie, L., Chua, T.: Denoising implicit feedback for recommendation. In: Proceedings Of The 14th ACM International Conference On Web Search And Data Mining, pp. 373–381 (2021)
Fan, W., Liu, X., Jin, W., Zhao, X., Tang, J., Li, Q.: Graph trend filtering networks for recommendation. In: Proceedings Of The 45th International ACM SIGIR Conference On Research And Development In Information Retrieval, pp. 112–121 (2022)
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)
Yu, J., Yin, H., Xia, X., Chen, T., Cui, L., Nguyen, Q.: 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)
Bayer, I., He, X., Kanagal, B., Rendle, S.: A generic coordinate descent framework for learning from implicit feedback. In: Proceedings Of The 26th International Conference On World Wide Web, pp. 1341–1350 (2017)
Liu, X., et al.: Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. 35, 857–876 (2021)
Yao, T., et al.: Self-supervised learning for large-scale item recommendations. In: Proceedings Of The 30th ACM International Conference On Information & Knowledge Management, pp. 4321–4330 (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)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. ArXiv Preprint ArXiv:1205.2618 (2012)
Wang, X., Liu, N., Han, H., Shi, C.: Self-supervised heterogeneous graph neural network with co-contrastive learning. In: Proceedings Of The 27th ACM SIGKDD Conference On Knowledge Discovery & Data Mining. pp. 1726–1736 (2021)
Yi, Z., Wang, X., Ounis, I., Macdonald, C.: Multi-modal graph contrastive learning for micro-video recommendation. In: Proceedings of The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1807–1811 (2022)
Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., Guo, M.: 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)
Zhang, F., Yuan, N., Lian, D., Xie, X., Ma, W.: 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)
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 (2014)
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, 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, pp. 968–977 (2019)
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, pp. 950–958 (2019)
Wang, X., et al.: Learning intents behind interactions with knowledge graph for recommendation. Proc. Web Conf. 2021, 878–887 (2021)
Chen, Y., Yang, Y., Wang, Y., Bai, J., Song, X., King, I.: Attentive knowledge-aware graph convolutional networks with collaborative guidance for personalized recommendation. In: 2022 IEEE 38th International Conference On Data Engineering (ICDE), pp. 299–311 (2022)
Wang, H., Zhou, B., Zhang, L., Ma, H.: Recommendation method for contrastive enhancement of neighborhood information. Comput. Mater. Continua. 78, 453–472 (2024)
Yan, F., Xu, X., Zhao, R., Sun, S., Ju, S.: Knowledge-aware recommender system with cross-views contrastive learning. Adv. Eng. Sci. 56, 44–53 (2024)
Acknowledgement
This work was supported by the Key Programs of the National Natural Science Foundation of China (Grant No.62137001). This work was jointly supported by the Key Technologies R&D Programs of Sichuan Province of China (Grant No.2023YFG0265).
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
Sun, S., Cai, T., Yan, F., Ju, S. (2024). Two-Stage Enhancement for Recommendation Systems Based on Contrastive Learning. In: Jin, C., Yang, S., Shang, X., Wang, H., Zhang, Y. (eds) Web Information Systems and Applications. WISA 2024. Lecture Notes in Computer Science, vol 14883. Springer, Singapore. https://doi.org/10.1007/978-981-97-7707-5_13
Download citation
DOI: https://doi.org/10.1007/978-981-97-7707-5_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-7706-8
Online ISBN: 978-981-97-7707-5
eBook Packages: Computer ScienceComputer Science (R0)