Global Information Attention Enhancement for Session-Based Recommendation

doi:10.3778/j.issn.1002-8331.2201-0116

Abstract

Abstract: In recent years, the application and research of session-based recommender system（SRS） is a popular direction of recommender system. How to use user session information to further improve user satisfaction and recommendation accuracy is the main task of session-based recommendation system. At present, most SBR models only model user preferences based on the target session, ignoring the item conversion information from other sessions, thus failing to fully understand user preferences. To address its limitations, this paper proposes a global context information graph neural networks for session-based recommendation（GCI-GNN）. This model takes advantage of the item conversion relationship across all sessions to capture user preferences more accurately. Specifically, GCI-GNN first learns object vector representations from both the target session and the global session. Secondly, location awareness attention network is used to incorporate reverse location information into object embedding. Finally, more effective recommendation can be achieved by learning user representation based on session length information. In this paper, experiments are carried out on Diginetica and Yoochoose data sets. The experimental results show that, compared with the optimal benchmark model, the GCI-GNN model improves the indexes of Diginetica data set by more than 2 percentage points. On Yoochoose data set, the GCI-GNN model improves more than 1 percentage point in each index, which verifies the effectiveness of the GCI-GNN model.

Key words: session recommendation, graph neural network, user interest, attention network

摘要： 近年来，基于会话推荐系统（session-based recommender system，SRS）的应用和研究是推荐系统的一个热门方向。如何利用用户会话信息进一步提升用户满意度和推荐精确度，是基于会话推荐系统的主要任务。目前大多数SBR模型仅基于目标会话对用户偏好建模，忽略了来自其他会话的物品转换信息，导致无法全面了解用户偏好。为了解决其局限性，提出融合全局上下文信息注意力增强的图神经网络模型（global context information graph neural networks for session-based recommendation，GCI-GNN）。该模型利用所有会话上的物品转换关系，更准确地获取用户偏好。具体而言，GCI-GNN从目标会话和全局会话学习物品向量表示。使用位置感知注意网络，将反向位置信息纳入物品嵌入中。考虑会话长度信息学习用户表示进而达到更有效的推荐。在Diginetica和Yoochoose数据集上进行实验，实验结果表明，相对最优的基准模型，GCI-GNN模型在Diginetica数据集各项指标上的提高超过2个百分点，在Yoochoose数据集上，GCI-GNN模型在各项指标上的提高超过1个百分点，验证了GCI-GNN模型的有效性。

关键词: 会话的推荐, 图神经网络, 用户兴趣, 注意力网络

WANG Yonggui, WANG Yang, TAO Mingyang, CAI Yongwang. Global Information Attention Enhancement for Session-Based Recommendation[J]. Computer Engineering and Applications, 2022, 58(21): 131-141.

王永贵, 王阳, 陶明阳, 蔡永旺. 融合全局信息的注意力增强会话推荐方法[J]. 计算机工程与应用, 2022, 58(21): 131-141.

References

[1] RESNICK P，VARIAN H R.Recommender systems[J].Communications of the ACM，1997，40（3）：56-58.
[2] SARWAR B，KARYPIS G，KONSTAN J，et al.Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web，2001：285-295.
[3] 曾义夫，牟其林，周乐，等.基于图表示学习的会话感知推荐模型[J].计算机研究与发展，2020，57（3）：590-603.
ZENG Y F，MU Q L，ZHOU L，et al.Graph embedding based session perception model for next-click recommendation[J].Journal of Computer Research and Development，2020，57（3）：590-603.
[4] BAIER C，HAVERKORT B，HERMANNS H，et al.Model-checking algorithms for continuous-time Markov chains[J].IEEE Transactions on Software Engineering，2003，29（6）：524-541.
[5] 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.
[6] TAN Y K，XU X，LIU Y.Improved recurrent neural networks for session-based recommendations[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems，2016：17-22.
[7] WU S，TANG Y，ZHU Y，et al.Session-based recommendation with graph neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019.
[8] KOREN Y，BELL R，VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer，2009，42（8）：30-37.
[9] GU W，DONG S，ZENG Z.Increasing recommended effectiveness with Markov chains and purchase intervals[J].Neural Computing & Applications，2014，25（5）：1153-1162.
[10] RENDLE S，FREUDENTHALER C，SCHMIDT-THIEME L.Factorizing personalized Markov chains for next-basket-recommendation[C]//Proceedings of the 19th International Conference on World Wide Web.New York：ACM，2010：811-820.
[11] QUADRANA M，KARATZOGLOU A，HIDASI B，et al.Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the Eleventh ACM Conference on Recommender Systems，2017：130-137.
[12] LI J，REN P，CHEN Z，et al.Neural attentive session-based recommendation[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management，2017：1419-1428.
[13] LIU Q，ZENG Y，MOKHOSI R，et al.STAMP：short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2018：1831-1839.
[14] WANG S，HU L，WANG Y，et al.Sequential recommender systems：challenges，progress and prospects[C]//28th International Joint Conference on Artificial Intelligence，2019：6332-6338.
[15] ZHOU J，CUI G，HU S，et al.Graph neural networks：a review of methods and applications[J].AI Open，2020，1：57-81.
[16] AUDA G，KAMEL M，RAAFAT H.A new neural network structure with cooperative modules[C]//Proceedings of 1994 IEEE International Conference on Neural Networks（ICNN’94），1994：1301-1306.
[17] ZHANG Y，QI P，MANNING C D.Graph convolution over pruned dependency trees improves relation extraction[C]//Proceedings of the Empirical Methods in Natural Language Processing，2018：2205-2215.
[18] BASTINGS J，TITOV I，AZIZ W，et al.Graph convolutional encoders for syntax-aware neural machine translation[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing，2017：1957-1967.
[19] YANG Z，YANG D，DYER C，et al.Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，2016：1480-1489.
[20] RHEE S，SEO S，KIM S.Hybrid approach of relation networks and localized graph convolutional filtering for breast cancer subtype classification[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence，2018：3527-3534.
[21] WANG X，YE Y，GUPTA A.Zero-shot recognition via semantic embeddings and knowledge graphs[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2018：6857-6866.
[22] QI S，WANG W，JIA B，et al.Learning human-object interactions by graph parsing neural networks[C]//Proceedings of the European Conference on Computer Vision（ECCV），2018：401-417.
[23] LI Y，ZEMEL R，BROCKSCHMIDT M，et al.Gated graph sequence neural networks[C]//Proceedings of International Conference on Leaning Representations，2016.
[24] WU S，ZHANG M，JIANG X，et al.Personalized graph neural networks with attention mechanism for session-aware recommendation[J].IEEE Transactions on Knowledge and Data Engineering，2022，34（8）：3946-3957.
[25] 邢长征，赵宏宝，张全贵，等.融合评论文本层级注意力和外积的推荐方法[J].计算机科学与探索，2020，14（6）：947-957.
XING C Z，ZHAO H B，ZHANG Q G，et al.Review text hierarchical attention and outer product for recommendation method[J].Journal of Frontiers of Computer Science and Technology，2020，14（6）：947-957.
[26] XU C，ZHAO P，LIU Y，et al.Graph contextualized self-attention network for session-based recommendation[C]//28th International Joint Conference on Artificial Intelligence，2019：3940-3946.
[27] QIU R，LI J，HUANG Z，et al.Rethinking the item order in session-based recommendation with graph neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management，2019：579-588.
[28] CHEN T，WONG R C W.Handling information loss of graph neural networks for session-based recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining，2020：1172-1180.
[29] FANG J.Session-based recommendation with self-attention networks[J].arXiv：2102.01922，2021.
[30] ZHOU H，TAN Q，HUANG X，et al.Temporal augmented graph neural networks for session-based recommendations[C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval，2021：1798-1802.
[31] YU F，ZHU Y，LIU Q，et al.TAGNN：target attentive graph neural networks for session-based recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval，Xi’an，China，25-30 July 2020：1921-1924.