结合超图对比学习和关系聚类的知识感知推荐算法

doi:10.3778/j.issn.1673-9418.2305058

摘要/Abstract

摘要： 结合知识图谱的推荐算法通过引入知识图谱，获得项目的辅助信息，从而实现更好的推荐效果。然而推荐的过程中存在知识图谱中关系长尾分布、用户项目交互数据稀疏和异构信息利用不平衡的问题。针对这些问题，提出了一种结合超图对比学习和关系聚类的知识感知推荐算法（HC-CRKG）。通过关系聚类的方式重构知识图谱，缓解了知识图谱中关系的长尾分布问题；构建用户-项目-实体异构图，利用一种结合注意力机制的图卷积网络学习用户、项目的异构图嵌入；同时使用一种参数化的超图卷积网络，学习用户、项目的超图嵌入；在异构图嵌入和超图嵌入之间进行对比学习，为模型引入自监督信号，缓解数据稀疏性问题；将异构图嵌入和超图嵌入相结合，用于后续的推荐预测，进一步缓解了异构信息利用不平衡问题。模型在MovieLens-1M、Book-Crossing和Last.FM三个公开数据集上与CKAN、KGIC、VRKG4Rec等基线模型进行对比实验，实验结果表明在AUC、F1和Recall@K指标上，模型均取得了不同程度的提升。

Abstract: The recommendation algorithm combined with knowledge graph obtains the auxiliary information of items by introducing knowledge graph to achieve better recommendation effect. However, there are problems in the process of recommendation: long-tail distribution of relations in the knowledge graph, sparse user-item interaction data and unbalanced utilization of heterogeneous information. In response to these problems, a knowledge-aware recommendation algorithm combining hypergraph contrast learning and relational clustering (HC-CRKG) is proposed. Firstly, the knowledge graph is reconstructed by the way of relationship clustering, which alleviates the problem of long-tail distribution of relationships in the knowledge graph. Secondly, a user-item-entity heterogeneous graph is constructed, and a graph convolutional network combining attention mechanism is used to learn the heterogeneous graph embeddings of users and items. Meanwhile, a parametric hypergraph convolutional network is used to learn the hypergraph embeddings of users and items. Subsequently, contrast learning is performed between the heterogeneous graph embedding and the hypergraph embedding to introduce a self-supervised signal for the model to alleviate the data sparsity problem. Finally, the heterogeneous graph embedding and hypergraph embedding are combined for subsequent recommendation prediction, which further alleviates the heterogeneous information utilization imbalance problem. The model is tested against baseline models such as CKAN (collaborative knowledge-aware attentive network), KGIC (improving knowledge-aware recommendation with multi-level interactive contrastive learning), and VRKG4Rec (virtual relational knowledge graphs for recommendation) on three publicly available datasets MovieLens-1M, Book-Crossing and Last.FM. Experimental results show that the model achieves different degrees of improvement in AUC, F1 and Recall@K.

Key words: recommendation system, knowledge graph, graph convolution networks, hypergraph, constract learning, self-supervised learning, knowledge representation learning

王永贵, 陈书铭, 刘义海, 赖贞祥. 结合超图对比学习和关系聚类的知识感知推荐算法[J]. 计算机科学与探索, 2024, 18(8): 2140-2155.

WANG Yonggui, CHEN Shuming, LIU Yihai, LAI Zhenxiang. Knowledge-aware Recommendation Algorithm Combining Hypergraph Contrast Learning and Relational Clustering[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(8): 2140-2155.

参考文献

[1] GUO Q, ZHUANG F, QIN C, et al. A survey on knowledge graph-based recommender systems[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(8): 3549-3568.
[2] LIN Z, TIAN C, HOU Y, et al. Improving graph collaborative filtering with neighborhood-enriched contrastive learning[C]//Proceedings of the ACM Web Conference 2022, Lyon, Apr 25-29, 2022. New York: ACM, 2022: 2320-2329.
[3] ZHOU X, SUN A, LIU Y, et al. SelfCF: a simple framework for self-supervised collaborative filtering[J]. ACM Transactions on Recommender Systems, 2023, 1(2): 1-25.
[4] YANG Y, HUANG C, XIA L, et al. Knowledge graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 1434-1443.
[5] SHAH A, MOLOKWU B, KOBTI Z. HTransE: hybrid translation-based embedding for knowledge graphs[C]// Proceedings of the 2022 IEEE International Conference on Knowledge Graph, Orlando, Nov 30-Dec 1, 2022. Piscataway: IEEE, 2022: 233-240.
[6] REN L J, LU J, GUO W. Multi-source knowledge embedding research of knowledge graph[C]//Proceedings of the 3rd International Conference on Circuits, Systems and Devices, Chengdu, Aug 23-25, 2019. Piscataway: IEEE, 2019: 163-166.
[7] HUANG X, ZHANG J Y, LI D C, et al. Knowledge graph embedding based question answering[C]//Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, Feb 11-15, 2019. New York: ACM, 2019: 105-113.
[8] ZHANG F, YUAN N J, LIAN D, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 353-362.
[9] WANG H, ZHANG F, WANG J, et al. RippleNet: propagating user preferences on the knowledge graph for recommender systems[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Torino, Oct 22-26, 2018. New York: ACM, 2018: 417-426.
[10] WANG H, ZHAO M, XIE X, et al. Knowledge graph convolutional networks for recommender systems[C]//Proceedings of the 2019 World Wide Web Conference, San Francisco, May 13-17, 2019. New York: ACM, 2019: 3307-3313.
[11] DU Y, ZHU X, CHEN L, et al. HAKG: hierarchy-aware knowledge gated network for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 1390-1400.
[12] LU L, WANG B, ZHANG Z, et al. VRKG4Rec: virtual relational knowledge graph for recommendation[C]//Proceedings of the 16th ACM International Conference on Web Search and Data Mining, Singapore, Feb 27-Mar 3, 2023. New York: ACM, 2023: 526-534.
[13] XIA L, HUANG C, HUANG C, et al. Automated self-supervised learning for recommendation[C]//Proceedings of the ACM Web Conference 2023, Austin, Apr 30-May 4, 2023. New York: ACM, 2023: 992-1002.
[14] WU J, WANG X, FENG F, et al. Self-supervised graph learning for recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 11-15, 2021. New York: ACM, 2021: 726-735.
[15] YAO T, YI X, CHENG D Z, et al. Self-supervised learning for large-scale item recommendations[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Nov 1-5, 2021. New York: ACM, 2021: 4321-4330.
[16] ZOU D, WEI W, MAO X L, et al. Multi-level cross-view contrastive learning for knowledge-aware recommender system[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 1358-1368.
[17] CAI X, HUANG C, XIA L, et al. LightGCL: simple yet effective graph contrastive learning for recommendation [EB/OL]. [2023-04-12]. https://arxiv.org/abs/2302.08191.
[18] HE X, DENG K, WANG X, et al. LightGCN: simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 639-648.
[19] WANG J, DING K, HONG L, et al. Next-item recommendation with sequential hypergraphs[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. New York: ACM, 2020: 1101-1110.
[20] JI S, FENG Y, JI R, et al. Dual channel hypergraph collaborative filtering[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug 23-27, 2020. New York: ACM, 2020: 2020-2029.
[21] XIA L, HUANG C, XU Y, et al. Hypergraph contrastive collaborative filtering[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Jul 11-15, 2022. New York: ACM, 2022: 70-79.
[22] RUSCH T K, BRONSTEIN M M, MISHRA S. A survey on oversmoothing in graph neural networks[EB/OL]. [2023-04-12]. https://arxiv.org/abs/2303.10993.
[23] FENG Y, YOU H, ZHANG Z, et al. Hypergraph neural networks[C]//Proceedings of the 2019 AAAI Conference on Artificial Intelligence, Jan 27-Feb 1, 2019. Menlo Park: AAAI, 2019: 3558-3565.
[24] OORD A, LI Y, VINYALS O. Representation learning with contrastive predictive coding[EB/OL]. [2023-04-12]. https://arxiv.org/abs/1807.03748.
[25] WANG X, HE X, CAO Y, et al. KGAT: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, Aug 4-8, 2019. New York: ACM, 2019: 950-958.
[26] WANG X, HUANG T, WANG D, et al. Learning intents behind interactions with knowledge graph for recommendation[C]//Proceedings of the 2021 Web Conference, Ljubljana, Apr 19-23, 2021. New York: ACM, 2021: 878-887.
[27] WANG Z, LIN G, TAN H, et al. CKAN: collaborative knowledge-aware attentive network for recommender systems[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul 25-30, 2020. Nwe York: ACM, 2020: 219-228.
[28] ZOU D, WEI W, WANG Z, et al. Improving knowledge-aware recommendation with multi-level interactive contrastive learning[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, Oct 17-21, 2022. New York: ACM, 2022: 2817-2826.
[29] DATTA L. A survey on activation functions and their relation with Xavier and He normal initialization[EB/OL]. [2023-04-12]. https://arxiv.org/abs/2004.06632.
[30] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[EB/OL]. [2023-04-12]. https://arxiv.org/abs/1205.2618.