主题增强的多层次图神经网络会话推荐模型

doi:10.16451/j.cnki.issn1003-6059.202302006

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (738 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Session-based recommendation(SBR) aims to provide recommendations for anonymous users or users who are not logged in based on data in the session. The existing research models a single item in the session as the smallest unit, ignoring the item representation in different receptive fields. Moreover, the implicit topic information contained in the session sequence is not mined. To alleviate these issues, a topic-enhanced multi-level graph neural network(TEMGNN) for SBR is proposed. Firstly, a multi-level item embedding learning module is designed to broaden the receptive fields of item and obtain the representation of items at different granularities. Then, the proposed multi-level graph neural network is employed to propagate the item information with and cross granularities, capturing richer item embedding representation. Furthermore, a topic learning module is proposed to extract the topic commonalities of items in hidden space and automatically form topic representations of items by explicit vector space projection without relying on any item attribute information. Thus, the recommendation performance of the model is enhanced. Experiments on three benchmark datasets show the superiority of TEMGNN.

Key words： Recommendation System Graph Neural Network Item Representation Learning Item Topic Representation

Received: 29 September 2022

ZTFLH:

TP391

Fund:National Natural Science Foundation of China(No.62141201), Natural Science Foundation of Chongqing(No.CSTB2022NSCQ-MSX1672), Major Project of Science and Technology Research Program of Chongqing Education Commission of China(No.KJZD-M202201102)

Corresponding Authors: ZHU Xiaofei, Ph.D., professor. His research interests include natural language processing, data mining and information retrieval.

About author:: TANG Gu, master student. His research interests include recommendation systems and natural language processing.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	TANG Gu
	ZHU Xiaofei

Cite this article:

TANG Gu,ZHU Xiaofei. Topic-Enhanced Multi-level Graph Neural Network for Session-Based Recommendation[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(2): 174-186.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202302006 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I2/174

[1] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-Based Colla-borative Filtering Recommendation Algorithms // Proc of the 10th International Conference on World Wide Web. New York USA: ACM, 2001: 285-295.
[2] HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al. Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems, 2004, 22(1): 5-53.
[3] CHEN W Y, CAI F, CHEN H H, et al. Joint Neural Collaborative Filtering for Recommender Systems. ACM Transactions on Information Systems, 2019, 37(4). DOI: 10.1145/3343117.
[4] RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L.Factorizing Personalized Markov Chains for Next-Basket Recommendation // Proc of the 19th International Conference on World Wide Web. New York USA: ACM, 2010: 811-820.
[5] SHANI G, HECKERMAN D, BRAFMAN R I.An MDP-Based Re-commender System. Journal of Machine Learning Research, 2005, 6: 1265-1295.
[6] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-Based Recommendations with Recurrent Neural Networks[C/OL].[2022-09-20]. https://arxiv.org/pdf/1511.06939.pdf.
[7] LI J, REN P J, CHEN Z M, et al. Neural Attentive Session-Based Recommendation // Proc of the ACM Conference on Information and Knowledge Management. New York USA: ACM, 2017: 1419-1428.
[8] LIU Q, ZENG Y F, MOKHOSI R, et al. STAMP: Short-Term Attention/Memory Priority Model for Session-Based Recommendation // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York USA: ACM, 2018: 1831-1839.
[9] YUAN J H, SONG Z H, SUN M Y, et al. Dual Sparse Attention Network for Session-Based Recommendation. Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021, 35(5): 4635-4643.
[10] WU S, TANG Y Y, ZHU Y Q, et al. Session-Based Recommendation with Graph Neural Networks // Proc of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Conference and 9th AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto USA: AAAI, 2019: 346-353.
[11] CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Lear-ning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg USA: ACL, 2014: 1724-1734.
[12] REN P J, LI J, CHEN Z M, et al. RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-Based Recommendation // Proc of the 33rd AAAI Conference on Artificial Intelligence and 31st Innovative Applications of Artificial Intelligence Confe-rence and 9th AAAI Symposium on Educational Advances in Artificial Intelligence. Palo Alto USA: AAAI, 2019: 4806-4813.
[13] CHEN W Y, CAI F, CHEN H H, et al. A Dynamic Co-attention Network for Session-Based Recommendation // Proc of the 28th ACM International Conference on Information and Knowledge Ma-nagement. New York USA: ACM, 2019: 1461-1470.
[14] LI Y J, TARLOW D, BROCKSCHMIDT M, et al. Gated Graph Sequence Neural Networks[C/OL].[2022-09-20]. https://arxiv.org/pdf/1511.05493v4.pdf.
[15] QIU R H, LI J J, HUANG Z, et al. Rethinking the Item Order in Session-Based Recommendation with Graph Neural Networks // Proc of the 28th ACM International Conference on Information and Knowledge Management. New York USA: ACM, 2019: 579-588.
[16] YU F, ZHU Y Q, LIU Q, et al. TAGNN: Target Attentive Graph Neural Networks for Session-Based Recommendation // Proc of the 43rd International ACM SIGIR Conference on Research and Deve-lopment in Information Retrieval. New York USA: ACM, 2020: 1921-1924.
[17] WANG J L, DING K Z, ZHU Z W, et al. Session-Based Reco-mmendation with Hypergraph Attention Networks // Proc of the SIAM International Conference on Data Mining. Philadelphia USA: SIAM, 2021: 82-90.
[18] WANG Z Y, WEI W, CONG G, et al. Global Context Enhanced Graph Neural Networks for Session-Based Recommendation // Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York USA: ACM, 2020: 169-178.
[19] XIA X, YIN H Z, YU J L, et al. Self-Supervised Graph Co-trai-ning for Session-Based Recommendation // Proc of the 30th ACM International Conference on Information and Knowledge Management. New York USA: ACM, 2021: 2180-2190.
[20] GUO J Y, YANG Y M, SONG X C, et al. Learning Multi-granularity Consecutive User Intent Unit for Session-Based Recommendation // Proc of the 15th ACM International Conference on Web Search and Data Mining. New York USA: ACM, 2022: 343-352.
[21] HE X N, DENG K, WANG X, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation // Proc of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York USA: ACM, 2020: 639-648.
[22] HE C Y, ANNAVARAM M, AVESTIMEHR S. Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge // Proc of the 34th International Conference on Neural Information Processing Systems. Cambridge, USA: MIT Press, 2020: 14068-14080.
[23] BENGIO Y, LÉONARD N, COURVILLE A. Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation[C/OL]. [2022-09-20]. https://arxiv.org/pdf/1308.3432.pdf.
[24] MADRY A, MAKELOV A, SCHMIDT L, et al. Towards Deep Learning Models Resistant to Adversarial Attacks[C/OL].[2022-09-20]. https://openreview.net/pdf?id=rJzIBfZAb.
[25] WANG M R, REN P J, MEI L, et al. A Collaborative Session-Based Recommendation Approach with Parallel Memory Modules // Proc of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York USA: ACM, 2019: 345-354.
[26] KANG W C, MCAULEY J.Self-Attentive Sequential Recommendation // Proc of the IEEE International Conference on Data Mi-ning. Washington USA: IEEE, 2018: 197-206.
[27] PAN Z Q, CAI F, CHEN W Y, et al. Star Graph Neural Networks for Session-Based Recommendation // Proc of the 29th ACM International Conference on Information and Knowledge Management. New York USA: ACM, 2020: 1195-1204.
[28] LUO A J, ZHAO P P, LIU Y C, et al. Collaborative Self-Attention Network for Session-Based Recommendation // Proc of the 29th International Joint Conference on Artificial Intelligence. San Francisco USA: IJCAI, 2020: 2591-2597.