多任务联合学习的图卷积神经网络推荐

doi:10.3778/j.issn.1002-8331.2303-0508

摘要/Abstract

摘要： 基于图神经网络的协同过滤推荐可以更有效地挖掘用户项目之间的交互信息，但其性能依然受到数据稀疏和表征学习质量不高问题的影响，因此提出一种多任务联合学习的图卷积神经网络推荐（multi-task joint learning for graph convolutional neural network recommendations, MTJL-GCN）模型。利用图神经网络在用户-项目交互图上所聚集到的同质结构信息与初始嵌入信息形成结构邻居关系，设计节点邻居关系的对比学习辅助任务来缓解数据稀疏问题；向节点的原始表征添加随机的统一噪声进行表征级数据增强，构建节点表征关系的对比学习辅助任务，并提出直接优化对齐性和均匀性两个属性的学习目标来提高表征学习质量；将图协同过滤推荐任务与对比学习辅助任务和直接优化学习目标进行联合训练，从而提升推荐性能。在Amazon-books和Yelp2018两个公开数据集上进行实验，该模型在Recall@k和NDCG@k两个推荐性能指标上的表现均优于基线模型，证明了MTJL-GCN模型的有效性。

Abstract: Collaborative filtering recommendation based on graph neural network can mine the interaction information between users and items more effectively, but its performance is still affected by the problems of sparse data and low quality of representation learning. Therefore, a multi-task joint learning model for graph convolutional neural network recommendation (MTJL-GCN) is proposed. Firstly, the graph neural network is used to gather the homogeneous structural information on the user-item interaction graph and form the structural neighbor relationship with the initial embedding information, and the comparative learning assistance task of node neighbor relationship is designed to alleviate the data sparse problem. Secondly, random unified noise is added to the original representation of nodes to enhance the representation-level data, and a comparative learning assistance task is constructed for the node representation relationship. The learning objectives of alignment and uniformity are proposed to improve the quality of representation learning. Finally, the graph collaborative filtering recommendation task is combined with the comparative learning assistance task and the direct optimization of learning objectives for joint training, so as to improve the recommendation performance. Experiments on two public datasets of Amazon-books and Yelp2018 show that the model performs better than the baseline model in the two recommended performance indexes of Recall@k and NDCG@k, which proves the validity of the MTJL-GCN model.

Key words: recommendation algorithms, graph convolutional neural network, contrast learning, representational learning, data sparse, collaborative filtering

王永贵, 邹赫宇. 多任务联合学习的图卷积神经网络推荐[J]. 计算机工程与应用, 2024, 60(4): 306-314.

WANG Yonggui, ZOU Heyu. Multi-Task Joint Learning for Graph Convolutional Neural Network Recommendations[J]. Computer Engineering and Applications, 2024, 60(4): 306-314.

参考文献

[1] RICCI F, ROKACH L, SHAPIRA B. Introduction to recommender systems handbook[M]//Recommender systems handbook. Boston: Springer, 2011: 1-35.
[2] MAO K, ZHU J, WANG J, et al. SimpleX: a simple and strong baseline for collaborative filtering[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021: 1243-1252.
[3] GUO G, ZHANG J, THALMANN D. Merging trust in collaborative filtering to alleviate data sparsity and cold start[J]. Knowledge-Based Systems, 2014, 57: 57-68.
[4] WANG X, HE X, WANG M, et al. Neural graph collaborative filtering[C]//Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 165-174.
[5] 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, 2020: 639-648.
[6] WANG H, ZHAO M, XIE X, et al. Knowledge graph convolutional networks for recommender systems[C]//Proceedings of the 2019 World Wide Web Conference, 2019: 3307-3313.
[7] 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 & Data Mining, 2019: 950-958.
[8] CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]//Proceedings of the 37th International Conference on Machine Learning, 2020: 1597-1607.
[9] GAO T, YAO X, CHEN D. SimCSE: simple contrastive learning of sentence embeddings[J]. arXiv:2104.08821, 2021.
[10] GRILL J B, STRUB F, ALTCHé F, et al. Bootstrap your own latent—a new approach to self-supervised learning[C]//Advances in Neural Information Processing Systems 33, 2020: 21271-21284.
[11] 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, 2021: 726-735.
[12] XIA X, YIN H, YU J, et al. Self-supervised hypergraph convolutional networks for session-based recommendation[C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021: 4503-4511.
[13] CHEN Y, LIU Z, LI J, et al. Intent contrastive learning for sequential recommendation[C]//Proceedings of the ACM Web Conference 2022, 2022: 2172-2182.
[14] WANG C, YU Y, MA W, et al. Towards representation alignment and uniformity in collaborative filtering[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022: 1816-1825.
[15] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[J]. arXiv:1609.02907, 2016.
[16] WANG X, JIN H, ZHANG A, et al. Disentangled graph collaborative filtering[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: 1001-1010.
[17] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian personalized ranking from implicit feedback[J]. arXiv:1205.2618, 2012.
[18] WANG T, ISOLA P. Understanding contrastive representation learning through alignment and uniformity on the hypersphere[C]//Proceedings of the 37th International Conference on Machine Learning, 2020: 9929-9939.
[19] YU J, YIN H, XIA X, et al. Are graph augmentations necessary? Simple graph contrastive learning for recommendation[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022: 1294-1303.
[20] JAISWAL A, BABU A R, ZADEH M Z, et al. A survey on contrastive self-supervised learning[J]. Technologies, 2020, 9(1): 2.
[21] KHOSLA P, TETERWAK P, WANG C, et al. Supervised contrastive learning[C]//Advances in Neural Information Processing Systems 33, 2020: 18661-18673.
[22] LIU X, ZHANG F, HOU Z, et al. Self-supervised learning: generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(1): 857-876.
[23] ZOU D, WEI W, MAO X L, et al. Multi-level cross-view contrastive learning for knowledge-aware recommender system[J]. arXiv:2204.08807, 2022.
[24] 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, 2022: 2320-2329.
[25] SHUAI J, ZHANG K, WU L, et al. A review-aware graph contrastive learning framework for recommendation[J]. arXiv: 2204.12063, 2022.
[26] OORD A, LI Y, VINYALS O. Representation learning with contrastive predictive coding[J]. arXiv:1807.03748, 2018.
[27] 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.