面向超图的可解释性对比元路径群组推荐

doi:10.3778/j.issn.1002-8331.2304-0290

摘要/Abstract

摘要： 在群组推荐中庞大且稀疏的数据往往容易忽视用户群组及项目之间的复杂依赖关系，因此融合不同用户偏好行为嵌入，使用户对群组依赖关系的表现更直观，同时为了在对比中增强视图效果，以获得更准确的推荐结果的目的，提出了一个面向超图的可解释性对比元路径群组推荐框架。通过聚合用户项目群组之间的依赖关系，构建元路径表现实体之间的不同类型交互，以促进实体的相似性，更准确地从数据中获取用户的组内、组外交互；通过将可解释性模型与对比学习相结合的技术，以提高模型的可解释性和性能；通过解释引导增强操作在模型框架上生成的正负视图上结合自监督对比学习，来解决上述问题。在真实数据集上进行实验，验证了所提出方法的有效性。

关键词: 群组推荐, 超图学习, 元路径, 推荐系统, 对比学习

Abstract: The large and sparse data in group recommendation often tend to ignore the complex dependencies between user groups and items, so fusing different user preference behavioral embeddings to make the performance of user dependencies on groups more intuitive, and also for the purpose of enhancing the view effect in comparison to obtain more accurate recommendation results, an interpretable comparison meta-path group recommendation framework based on hypergraph is proposed. By aggregating dependencies between groups of user items, it constructs meta-paths to represent different types of interactions between entities to promote entity similarity and more accurately obtain users’ in-group and out-group interactions from the data. By combining interpretable models with contrast learning techniques, it improves the interpretability and performance of the models. By interpreting guided enhancement operations on positive and negative views generated on the model framework combined with self-supervised contrast learning, it addresses the above issues. This experiment validates the effectiveness of the proposed approach by conducting experiments on real datasets.

Key words: group recommendation, hypergraph learning, meta-paths, recommendation system, contrastive learning

漆盛, 高榕, 邵雄凯, 吴歆韵, 万祥, 高海燕. 面向超图的可解释性对比元路径群组推荐[J]. 计算机工程与应用, 2024, 60(11): 268-280.

QI Sheng, GAO Rong, SHAO Xiongkai, WU Xinyun, WAN Xiang, GAO Haiyan. Hypergraph-Based Meta-Path Explanation Contrastive Learning for Group Recommendation[J]. Computer Engineering and Applications, 2024, 60(11): 268-280.

参考文献

[1] 李浩, 张亚钏, 康雁, 等. 融合循环知识图谱和协同过滤电影推荐算法[J]. 计算机工程与应用, 2020, 56(2): 106-114.
LI H, ZHANG Y C, KANG Y, et al. Fusion recurrent knowledge graph and collaborative filtering movie recommendation algorithm[J]. Computer Engineering and Applications, 2020, 56(2): 106-114.
[2] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[C]//Proceedings of the 4th International Conference on Learning Representations, 2016.
[3] 邱叶, 邵雄凯, 高榕, 等. 基于注意力门控神经网络的社会化推荐算法[J]. 计算机工程与应用, 2022, 58(5): 112-118.
QIU Y, SHAO X K, GAO R, et al. Social recommendation algorithm based on attention gated neural network[J]. Computer Engineering and Applications, 2022, 58(5): 112-118.
[4] SANKAR A, WU Y, WU Y, et al. Groupim: a mutual information maximization framework for neural group recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: 1279-1288.
[5] HE Z X, CHOW C Y, ZHANG J D. GAME: learning graphical and attentive multi-view embeddings for occasional group recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , 2020: 649-658.
[6] 梅雨竹, 胡竹林, 朱欣娟. 融合双层注意力机制的群组偏好融合策略研究[J]. 计算机工程与应用, 2023, 59(9): 272-279.
MEI Y Z, HU Z L, ZHU X J, et al. Research on group preference fusion strategies incorporating two-layer attention mechanisms[J]. Computer Engineering and Applications, 2023, 59(9): 272-279.
[7] LIM S H, JEONG Y W, PARK K H. Data placement and prefetching with accurate bit rate control for interactive media server[J]. ACM Transactions on Multimedia Computing, Communications, and Applications , 2008: 4(3): 1-25.
[8] CHEN Z, CAFARELLA M. Integrating spreadsheet data via accurate and low-effort extraction[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014: 1126-1135.
[9] YAO L, MAO C, LUO Y. Graph convolutional networks for text classification[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019: 7370-7377.
[10] HE W, JIANG Z, ZHANG C, et al. CurvaNet: geometric deep learning based on directional curvature for 3D shape analysis[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020: 2214-2224.
[11] CAO D, HE X, MIAO L, et al. Attentive group recommendation[C]//Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018: 645-654.
[12] YIN H, WANG Q, ZHENG K, et al. Social influence-based group representation learning for group recommendation[C]//Proceedings of the 2019 IEEE 35th International Conference on Data Engineering, 2019: 566-577.
[13] GUO L, YIN H, WANG Q, et al. Group recommendation with latent voting mechanism[C]//Proceedings of the 2020 IEEE 36th International Conference on Data Engineering, 2020: 121-132.
[14] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
[15] LE-KHAC P H, HEALY G, SMEATON A F. Contrastive representation learning: a framework and review[J]. IEEE Access, 2020, 8: 193907-193934.
[16] HE X, LIAO L, ZHANG H, et al. Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web, 2017: 173-182.
[17] CAO D, HE X, MIAO L, et al. Social-enhanced attentive group recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33(3): 1195-1209.
[18] ZHANG J, GAO M, YU J, et al. Double-scale self-supervised hypergraph learning for group recommendation[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021: 2557-2567.
[19] YIN H, WANG Q, ZHENG K, et al. Overcoming data sparsity in group recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(7): 3447-3460.
[20] GUO J, ZHU Y, LI A, et al. A social influence approach for group user modeling in group recommendation systems[J]. IEEE Intelligent Systems, 2016, 31(5): 40-48.
[21] FU T, LEE W C, LEI Z. Hin2vec: explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017: 1797-1806.
[22] DONG Y, CHAWLA N V, SWAMI A. Metapath2vec: scalable representation learning for heterogeneous networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017: 135-144.
[23] ZHAO H, YAO Q, LI J, et al. Meta-graph based recommendation fusion over heterogeneous information networks[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017: 635-644.
[24] WU C, WU F, HUANG Y, et al. User-as-graph: user modeling with heterogeneous graph pooling for news recommendation[C]//Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021: 1624-1630.
[25] AWAYSHEH F M, ALAZAB M, GARG S, et al. Big data resource management & networks: taxonomy, survey, and future directions[J]. IEEE Communications Surveys & Tutorials, 2021, 23(4): 2098-2130.
[26] FU X Y, ZHANG J N, MENG Z Q, et al. MAGNN: metapath aggregated graph neural network for heterogeneous graph embedding[C]//Proceedings of the Association for Computing Machinery, 2020: 2331-2341.
[27] LIU M, SHI J, LI Z, et al. Towards better analysis of deep convolutional neural networks[J]. IEEE Transactions on Visualization and Computer Graphics, 2016, 23(1): 91-100.
[28] SARANYA T, SRIDEVI S, DEISY C, et al. Performance analysis of machine learning algorithms in intrusion detection system: a review[J]. Procedia Computer Science, 2020, 171: 1251-1260.
[29] SINGH A, SENGUPTA S, LAKSHMINARAYANAN V. Explainable deep learning models in medical image analysis[J]. arXiv:2005.13799, 2020.
[30] ARRIETA B A, DíAZ RODRíGUEZ N, DEL SER J, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 33(3): 1566-2535.
[31] WANG L, LIM E P, LIU Z, et al. Explanation guided contrastive learning for sequential recommendation[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022: 2017-2027.
[32] VINH TRAN L, NGUYEN PHAM T A, TAY Y, et al. Interact and decide: medley of sub-attention networks for effective group recommendation[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 255-264.