Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph

doi:10.3778/j.issn.1673-9418.2406021

Abstract

Abstract: To alleviate the cold-start problem that exists in traditional collaborative filtering recommendation systems, knowledge graphs have been introduced as a kind of auxiliary knowledge in recommendation systems. However, existing knowledge graph recommendation models have limitations in adequately modeling higher-order interactions, making it difficult to capture important information from higher-order neighbors. In addition, the sparsity problem of supervised signals also affects recommendation system performance. To address the above issues, a self-supervised knowledge-aware recommendation model integrating adaptive hypergraph is proposed. The model first utilizes a hybrid graph convolutional network to jointly learn the low-order interaction embeddings in the interaction graph and the higher-order interaction embeddings in the adaptive hypergraph. Secondly, it uses a relation-aware graph attention network to mine the rich knowledge information of users and items in the knowledge graph. Then, the model constructs a comparison learning task based on the three views, which mitigates the sparsity problem of the interaction data by introducing the self-supervised signals. Finally, the three kinds of embeddings are combined for subsequent recommendation prediction. The model is experimentally compared with benchmark models such as KGAT, KGIN, and KACL on several publicly available datasets. Compared with the best recommendation performance model among the seven compared models, on the MovieLens dataset, Recall@20 is improved by 1.22%, NDCG@20 is improved by 1.17%; on the Yelp2018 dataset, Recall@20 is improved by 1.41%, NDCG@20 is improved by 1.60%. Experimental results show that this model outperforms other benchmark models in terms of recommendation performance.

Key words: recommendation system, knowledge graph, adaptive hypergraph, self-supervised learning, relation-aware graph attention network

摘要： 为缓解传统协同过滤推荐系统存在的冷启动问题，知识图谱作为一种辅助知识被引入到推荐系统中。然而，现有的知识图谱推荐模型在充分地建模高阶相互作用方面存在局限性，难以捕获来自高阶邻居的重要信息。此外，监督信号的稀疏性问题也影响着推荐系统性能。为了解决上述问题，提出一种融合自适应超图的自监督知识感知推荐模型。该模型使用混合图卷积网络共同学习交互图中低阶交互嵌入与自适应超图中高阶交互嵌入；使用关系感知图注意网络挖掘知识图谱中用户与物品丰富的知识信息；模型在这三种视图基础上构建对比学习任务，通过引入自监督信号来缓解交互数据的稀疏性问题；将三种嵌入相结合，用于后续的推荐预测。该模型在多个公开数据集上与KGAT、KGIN、KACL等基准模型进行了对比实验，与7个对比模型中推荐性能最好的模型相比，在MovieLens数据集上，Recall@20提升了1.22%，NDCG@20提升了1.17%；在Yelp2018数据集上，Recall@20提升了1.41%，NDCG@ 20提升了1.60%。实验结果显示该模型的推荐性能优于其他基准模型。

ZHOU Jiaxuan, LIU Xianhui, ZHAO Xiaodong, HOU Wenlong, ZHAO Weidong. Self-Supervised Knowledge-Aware Recommendation Model Integrating Adaptive Hypergraph[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(5): 1217-1229.

周家旋, 柳先辉, 赵晓东, 侯文龙, 赵卫东. 融合自适应超图的自监督知识感知推荐模型[J]. 计算机科学与探索, 2025, 19(5): 1217-1229.

References

[1] SUN Z, GUO Q, YANG J, et al. Research commentary on recommendations with side information: a survey and research directions[J]. Electronic Commerce Research and Applications, 2019, 37: 100879.
[2] ZHANG F Z, YUAN N J, LIAN D F, et al. Collaborative knowledge base embedding for recommender systems[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 353-362.
[3] LIN Y K, LIU Z Y, SUN M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Pro-ceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2015: 2181-2187.
[4] WANG X, WANG D X, XU C R, et al. Explainable reasoning over knowledge graphs for recommendation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 5329-5336.
[5] JIN J R, QIN J R, FANG Y C, et al. An efficient neighborhood-based interaction model for recommendation on heterogeneous graph[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 75-84.
[6] XIAN Y K, FU Z H, MUTHUKRISHNAN S, et al. Reinforcement knowledge graph reasoning for explainable recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2019: 285-294.
[7] LU L Y, WANG B, ZHANG Z Z, et al. VRKG4Rec: virtual relational knowledge graph for recommendation[C]//Procee-dings of the 16th ACM International Conference on Web Search and Data Mining. New York: ACM, 2023: 526-534.
[8] WANG H, XU Y, YANG C, et al. Knowledge-adaptive contras-tive learning for recommendation[C]//Proceedings of the 16th ACM International Conference on Web Search and Data Mining. New York: ACM, 2023: 535-543.
[9] FERRARA A, ANELLI V W, MANCINO A C M, et al. KGFlex: efficient recommendation with sparse feature factorization and knowledge graphs[J]. ACM Transactions on Recommender Systems, 2023, 1(4): 1-30.
[10] BORDES A, USUNIER N, GARCIA-DURáN A, et al. Trans-lating embeddings for modeling multi-relational data[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems-Volume 2, 2013: 2787-2795.
[11] JI G L, HE S Z, XU L H, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2015: 687-696.
[12] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[EB/OL]. [2024-04-13]. https://arxiv.org/abs/1205.2618.
[13] WANG H W, ZHANG F Z, XIE X, et al. DKN[C]//Proceedings of the 2018 World Wide Web Conference. New York: ACM, 2018: 1835-1844.
[14] AI Q, AZIZI V, CHEN X, et al. Learning heterogeneous know-ledge base embeddings for explainable recommendation[J]. Algorithms, 2018, 11(9): 137.
[15] YU X, REN X, GU Q Q, et al. Collaborative filtering with entity similarity regularization in heterogeneous information networks[C]//Proceedings of the IJCAI-13 HINA Workshop, 2013.
[16] LUO C, PANG W, WANG Z, et al. Hete-CF: social-based collaborative filtering recommendation using heterogeneous relations[C]//Proceedings of the 2014 IEEE International Conference on Data Mining. Piscataway: IEEE, 2014: 917-922.
[17] LI D Z, QU H B, WANG J Q. A survey on knowledge graph-based recommender systems[C]//Proceedings of the 2023 China Automation Congress. Piscataway: IEEE, 2023: 2925-2930.
[18] WANG X, HE X N, CAO Y X, et al. KGAT: knowledge graph attention network for recommendation[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 950-958.
[19] WANG H W, ZHAO M, XIE X, et al. Knowledge graph convolutional networks for recommender systems[C]//Proceedings of the 2019 World Wide Web Conference. New York: ACM, 2019: 3307-3313.
[20] WANG X, HUANG T L, WANG D X, et al. Learning intents behind interactions with knowledge graph for recommendation[C]//Proceedings of the Web Conference 2021. New York: ACM, 2021: 878-887.
[21] 李鹏飞, 贺洋, 毋建宏. 融合全局特征的时空网络兴趣点推荐算法[J]. 计算机工程与应用, 2024, 60(11): 75-83.
LI P F, HE Y, WU J H. Spatio-temporal network interest point recommendation algorithm fusing global features[J]. Computer Engineering and Applications, 2024, 60(11): 75-83.
[22] 张俊三, 肖森, 高慧, 等. 基于邻域采样的多任务图推荐算法[J]. 计算机工程与应用, 2024, 60(9): 172-180.
ZHANG J S, XIAO S, GAO H, et al. Multi-task graph recom-mendation algorithm based on neighborhood sampling[J]. Computer Engineering and Applications, 2024, 60(9): 172-180.
[23] WANG J L, DING K Z, HONG L J, et al. Next-item recommendation with sequential hypergraphs[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020: 1101-1110.
[24] JI S Y, FENG Y F, JI R R, et al. Dual channel hypergraph collaborative filtering[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 2020-2029.
[25] YU J L, YIN H Z, LI J D, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[C]//Proceedings of the Web Conference 2021. New York: ACM, 2021: 413-424.
[26] LI Y F, GAO C, LUO H L, et al. Enhancing hypergraph neural networks with intent disentanglement for session-based recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 1997-2002.
[27] LAI Y T, SU Y J, WEI L W, et al. Multi-view spatial-temporal enhanced hypergraph network for next POI recommendation[C]//Proceedings of the 2023 International Conference on Database Systems for Advanced Applications. Cham: Springer, 2023: 237-252.
[28] 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. New York: ACM, 2022: 70-79.
[29] SHAO Z F, WANG S J, LU W P, et al. Filter-enhanced hyper-graph transformer for multi-behavior sequential recommendation[C]//Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2024: 6575-6579.
[30] WU J C, WANG X, FENG F L, et al. Self-supervised graph learning for recommendation[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2021: 726-735.
[31] ZOU D, WEI W, WANG Z Y, et al. Improving knowledge-aware recommendation with multi-level interactive contrastive learning[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. New York: ACM, 2022: 2817-2826.
[32] YU J L, XIA X, CHEN T, et al. XSimGCL: towards extremely simple graph contrastive learning for recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(2): 913-926.
[33] HE X N, DENG K, WANG X, et al. LightGCN[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2020: 639-648.
[34] CHEN X, XIONG K, ZHANG Y F, et al. Neural feature-aware recommendation with signed hypergraph convolutional network[J]. ACM Transactions on Information Systems, 2020, 39(1): 1-22.
[35] ZHU Y Q, XU Y C, YU F, et al. Deep graph contrastive representation learning[EB/OL]. [2024-03-16]. https://arxiv.org/ abs/2006.04131.
[36] RENDLE S, KRICHENE W, ZHANG L, et al. Neural collaborative filtering vs. matrix factorization revisited[C]//Proceedings of the 14th ACM Conference on Recommender Systems. New York: ACM, 2020: 240-248.
[37] CHEN W, HUANG P P, XU J M, et al. POG: personalized outfit generation for fashion recommendation at Alibaba iFashion[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 2662-2670.
[38] KRICHENE W, RENDLE S, KRICHENE W, et al. On sampled metrics for item recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 1748-1757.
[39] WANG H W, ZHANG F Z, ZHANG M D, et al. Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 968-977.
[40] WANG Z, LIN G Y, TAN H B, 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. New York: ACM, 2020: 219-228.
[41] DU Y T, ZHU X J, CHEN L, et al. HAKG[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 1390-1400.
[42] GLOROT X, BENGIO Y. Understanding the difficulty of training deep feedforward neural networks[C]//Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010: 249-256.
[43] KINGMA D P, BA J, HAMMAD M M. Adam: a method for stochastic optimization[EB/OL]. [2024-03-16]. https://arxiv.org/abs/1412.6980.