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| Recurrent Neural Network and Attention Enhanced Gated Graph Neural Network for Session-Based Recommendation |
| LI Weiyue1,2, ZHU Zhiguo1,2, DONG Hao1,2, JIANG Pan1,2, GAO Ming1,2 |
1. School of Management Science and Engineering, Dongbei Uni-versity of Finance and Economics, Dalian 116025;
2. Key Laboratory of Liaoning Province for Data Analytics and Decision-Marking Optimization, Dongbei University of Finance and Economics, Dalian 116025 |
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Abstract Most of existing session-based recommender systems with graph neural networks are capable of capturing the adjacent contextual relation of products effectively in the session graph. However, few of them focus on the sequential relation. Both relations are important for precise recommendations in e-commerce scenarios. To solve the problem, a recurrent neural network and attention enhanced gated graph neural network for session-based recommender system is proposed based on bidirectional long short-term memory. The model is designed to complement the advantages of different network structures and learn the user's interest preferences expressed during the current session more fully. Specifically, a parallel framework is adopted in the proposed model to learn the neighborhood contextual features and temporal relation among products respectively within user session clickstreams in e-commerce scenarios. Attention mechanisms are applied to denoise the features. Finally, the adaptive fusion method of both features is employed based on gating mechanism. Experiments on three real-world datasets show the superiority of the proposed model. The model code in the paper is available at https://github.com/usernameAI/RAGGNN.
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Received: 12 October 2023
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| Fund:National Natural Science Foundation of China(No.72172025,72101051,71802023), Humanities and Social Sciences Foundation of the Ministry of Education of China(No.21YJAZH130), Scientific Research Foundation of the Education Department of Liaoning Province(No.LJKMZ20221606) |
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Corresponding Authors:
ZHU Zhiguo, Ph.D., professor. His research interests include data mining and business intelligence.
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| About author:: LI Weiyue, Ph.D. candidate. His research interests include recommender systems.DONG Hao, Ph.D. candidate. His research interests include business intelligence and recommender systems.JIANG Pan, Ph.D. candidate. Her research interests include electronic commerce and business intelligence.GAO Ming, Ph.D., professor. His research interests include cloud computing, big data and artificial intelligence. |
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[1] KERSBERGEN B, SPRANGERS O, SCHELTER S.Serenade-Low-Latency Session-Based Recommendation in e-Commerce at Scale // Proc of the International Conference on Management of Data. New York,USA: ACM, 2022: 150-159.
[2] GUO X J, WANG S G, ZHAO H Q, et al. Intelligent Online Selling Point Extraction for e-Commerce Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(11): 12360-12368.
[3] PAN Z Q, CAI F, CHEN W Y, et al. Collaborative Graph Learning for Session-Based Recommendation. ACM Transactions on Information Systems, 2022, 40(4). DOI: 10.1145/3490479.
[4] 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.
[5] MA H, YANG H X, LYU M R, et al. SoRec: Social Recommendation Using Probabilistic Matrix Factorization // Proc of the 17th ACM Conference on Information and Knowledge Management. New York,USA: ACM, 2008: 931-940.
[6] CHANGCHIEN S W, LU T C.Mining Association Rules Procedure to Support On-Line Recommendation by Customers and Products Fragmentation. Expert Systems with Applications, 2001, 20(4): 325-335.
[7] 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.
[8] YU F, LIU Q, WU S, et al. A Dynamic Recurrent Model for Next Basket Recommendation // Proc of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,USA: ACM, 2016: 729-732.
[9] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-Based Recommendations with Recurrent Neural Networks[C/OL].[2023-09-27]. https://arxiv.org/pdf/1511.06939.pdf.
[10] QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Persona-lizing Session-Based Recommendations with Hierarchical Recurrent Neural Networks // Proc of the 11th ACM Conference on Reco-mmender Systems. New York,USA: ACM, 2017: 130-137.
[11] RUOCCO M, SKREDE O S L, LANGSETH H. Inter-Session Mo-deling for Session-Based Recommendation // Proc of the 2nd Work-shop on Deep Learning for Recommender Systems. New York,USA: ACM, 2017: 24-31.
[12] WANG S J, HU L, WANG Y, et al. Modeling Multi-purpose Se-ssions for Next-Item Recommendations via Mixture-Channel Purpose Routing Networks // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCIA, 2019: 3771-3777.
[13] TAN Y K, XU X X, LIU Y.Improved Recurrent Neural Networks for Session-Based Recommendations // Proc of the 1st Workshop on Deep Learning for Recommender Systems. New York,USA: ACM, 2016: 17-22.
[14] BOGINA V, KUFLIK T.Incorporating Dwell Time in Session-Based Recommendations with Recurrent Neural Networks[C/OL]. [2023-09-27].https://ceur-ws.org/Vol-1922/paper11.pdf.
[15] 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.
[16] 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.
[17] YAO J P, LING Y, HOU P C, et al. A Graph Neural Network Model for Deciphering the Biological Mechanisms of Plant Electrical Signal Classification. Applied Soft Computing, 2023, 137. DOI: 10.1016/j.asoc.2023.110153.
[18] GU T Q, ZHAO H, HE Z Z, et al. Integrating External Knowledge into Aspect-Based Sentiment Analysis Using Graph Neural Network. Knowledge-Based Systems, 2023, 259. DOI: 10.1016/j.knosys.2022.110025.
[19] SONG G W, ZHAO T L, WANG S W, et al. Stock Ranking Prediction Using a Graph Aggregation Network Based on Stock Price and Stock Relationship Information. Information Sciences, 2023. DOI: 10.1016/j.ins.2023.119236.
[20] WANG L, SONG Z Y, ZHANG X Y, et al. SAT-GCN: Self-Attention Graph Convolutional Network-Based 3D Object Detection for Autonomous Driving. Knowledge-Based Systems, 2023, 259. DOI: 10.1016/j.knosys.2022.110080.
[21] WU S, TANG Y Y, ZHU Y Q, et al. Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 346-353.
[22] XU C F, ZHAO P P, LIU Y C, et al. Graph Contextualized Self-Attention Network for Session-Based Recommendation // Proc of the 28th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2019: 3940-3946.
[23] SONG W P, XIAO Z P, WANG Y F, et al. Session-Based Social Recommendation via Dynamic Graph Attention Networks // Proc of the 12th ACM International Conference on Web Search and Data Mining. New York,USA: ACM, 2019: 555-563.
[24] 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.
[25] 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.
[26] SHEN Q, WU L F, PANG Y T, et al. Multi-behavior Graph Contextual Aware Network for Session-Based Recommendation[C/OL].[2023-09-27]. https://arxiv.org/pdf/2109.11903.pdf.
[27] HUANG C, CHEN J H, XIA L H, et al. Graph-Enhanced Multi-task Learning of Multi-level Transition Dynamics for Session-Based Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4123-4130.
[28] FENG L X, CAI Y Q, WEI E L, et al. Graph Neural Networks with Global Noise Filtering for Session-Based Recommendation. Neurocomputing, 2022, 472: 113-123.
[29] LU Y J, KONG Y Y, SUN Z T, et al. Current Interest Enhanced Graph Neural Networks for Session-Based Recommendation // Proc of the 26th International Conference on Automation and Computing. Washington,USA: IEEE, 2021. DOI: 10.23919/ICAC50006.2021.9594121.
[30] VASWANI A, SHAZEER N, PARMAR N, et al.Attention Is All You Need // Proc of the 31st International Conference on Neural Information Processing Systems. Cambridge,USA: MIT Press, 2017: 6000-6010.
[31] GRAVES A, SCHMIDHUBER J.Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures. Neural Networks, 2005, 18(5/6): 602-610.
[32] LI Y J, TARLOW D, BROCKSCHMIDT M, et al. Gated Graph Sequence Neural Networks[C/OL].[2023-09-27]. https://arxiv.org/pdf/1511.05493.pdf.
[33] XIAO B, BENBASAT I. e-Commerce Product Recommendation Agents: Use, Characteristics, and Impact. MIS Quarterly, 2007, 31(1): 137-209.
[34] HOCHREITER S, SCHMIDHUBER J.Long Short-Term Memory. Neural Computation, 1997, 9(8): 1735-1780.
[35] CHUNG J, GULCEHRE C, CHO K H, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[C/OL].[2023-09-27]. https://arxiv.org/pdf/1412.3555v1.pdf.
[36] ZHAO W X, MU S L, HOU Y P, et al. RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms // Proc of the 30th ACM International Conference on Information and Knowledge Management. New York,USA: ACM,2021: 4653-4664.
[37] CHEN J F, ZHU G H, HOU H J, et al. AutoGSR: Neural Architecture Search for Graph-Based Session Recommendation // Proc of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York,USA: ACM, 2022: 1694-1704.
[38] SANG S, YUAN W H, LI W X, et al. Position-Aware Graph Neural Network forSession-Based Recommendation. Knowledge-Based Systems, 2023, 262. DOI: 10.1016/j.knosys.2022.110201.
[39] LI Z H, WANG X Z, YANG C, et al. Exploiting Explicit and Implicit Item Relationships for Session-Based Recommendation // Proc of the 16th ACM International Conference on Web Search and Data Mining. New York,USA: ACM, 2023: 553-561.
[40] WANG H S, YAN S R, WU C Q, et al. Cross-View Temporal Graph Contrastive Learning forSession-Based Recommendation. Knowledge-Based Systems, 2023, 264. DOI: 10.1016/j.knosys.2023.110304.
[41] TANG G, ZHU X F, GUO J F, et al. Time Enhanced Graph Neu-ral Networks forSession-Based Recommendation. Knowledge-Based Systems, 2022, 251. DOI: 10.1016/j.knosys.2022.109204.
[42] 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.
[43] WANG J L, DING K Z, ZHU Z W, et al. Session-Based Recommendation with Hypergraph Attention Networks // Proc of the SIAM International Conference on Data Mining. Philadelphia,USA: SIAM, 2021: 82-90.
[44] KINGMA D P, BA J. ADAM: A Method for Stochastic Optimization[C/OL]. [2023-09-27]. https://arxiv.org/pdf/1412.6980.pdf.
[45] CHEN T, WONG R C W. Handling Information Loss of Graph Neural Networks for Session-Based Recommendation // Proc of the 26th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining. New York,USA: ACM, 2020: 1172-1180.
[46] LINDEN G, SMITH B, YORK J.Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 2003, 7(1): 76-80.
[47] XIE X, SUN F, LIU Z Y, et al. Contrastive Learning for Sequential Recommendation // Proc of the IEEE 38th International Conference on Data Engineering. Cambridge,USA: IEEE, 2022: 1259-1273.
[48] QIU R H, HUANG Z, YIN H Z, et al. Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation // Proc of the 15th ACM International Conference on Web Search and Data Mining. New York,USA: ACM, 2022: 813-823.
[49] HOU Y P, HU B B, ZHANG Z Q, et al. CORE: Simple and Effe-ctive Session-Based Recommendation within Consistent Representation Space // Proc of the 45th International ACM SIGIR Confe-rence on Research and Development in Information Retrieval. New York,USA: ACM, 2022: 1796-1801.
[50] GUPTA P, GARG D, MALHOTRA P, et al. NISER: Normalized Item and Session Representations with Graph Neural Networks[C/OL].[2023-09-27]. https://arxiv.org/pdf/1909.04276v2.pdf.
[51] 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. |
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2024, Vol. 37 
