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Intention-aware denoising graph neural network for session-based recommendation

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Abstract

Session-based recommendation anticipates the next potential interest of users based on their previous anonymous interactions, which is a crucial and indispensable component of many online services. Recently, deep learning methods have attracted extensive attention for session-based recommendations due to their outstanding abilities in capturing user preferences. However, most existing methods fail to model the latent user intentions reflected by correlated items and ignore the noisy signals that exist in session sequences. To address these issues, we present a novel Intention-aware Denoising Graph Neural Network (ID-GNN) for session-based recommendation. Specifically, we propose an item graph construction module to explore the correlation of items in session sequences. Furthermore, we aggregate information on the constructed graph and employ an intention extraction matrix to capture the latent user intentions reflected by correlated items. Additionally, we introduce a relative sorting approach and a denoising threshold to adaptively filter out noisy user intentions. Experimental results on two e-commerce datasets demonstrate that ID-GNN outperforms state-of-the-art methods.

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Notes

  1. https://tianchi.aliyun.com/dataset/42

  2. https://www.kaggle.com/datasets/chadgostopp/recsys-challenge-2015

References

  1. De Maio C, Gallo M, Hao F, Yang E (2021) Who and where: context-aware advertisement recommendation on Twitter. Soft Comput 25:379–387

    Article  Google Scholar 

  2. Gan M, Ma Y (2022) DeepInteract: Multi-view features interactive learning for sequential recommendation. Expert Syst Appl 204:117305

    Article  Google Scholar 

  3. Gan M, Kwon O-C (2022) A knowledge-enhanced contextual bandit approach for personalized recommendation in dynamic domains. Knowl-Based Syst 251:109158

    Article  Google Scholar 

  4. Liu C, Li Y, Lin H, Zhang C (2022) Gnnrec: Gated graph neural network for session-based social recommendation model. J Intell Inf Syst 1–20

  5. Hao Y, Shi J (2022) Jointly Recommendation Algorithm of KNN Matrix Factorization with Weights. J Electr Eng Technol 17(6):3507–3514

    MathSciNet  Google Scholar 

  6. Sun X, Zhang L (2022) Multi-order nearest neighbor prediction for recommendation systems. Digital Signal Processing 127:103540

    Article  Google Scholar 

  7. Zhang Y, Shi Z, Zuo W, Yue L, Liang S, Li X (2020) Joint Personalized Markov Chains with social network embedding for cold-start recommendation. Neurocomputing 386:208–220

    Article  Google Scholar 

  8. Wen W, Wang W, Hao Z, Cai R (2023) Factorizing time-heterogeneous Markov transition for temporal recommendation. Neural Netw 159:84–96

    Article  Google Scholar 

  9. Zhang J, Ma C, Mu X, Zhao P, Zhong C, Ruhan A (2021) Recurrent convolutional neural network for session-based recommendation. Neurocomputing 437:157–167

    Article  Google Scholar 

  10. Ma Y, Gan M (2021) Deepassociate: A deep learning model exploring sequential influence and history-candidate association for sequence recommendation. Expert Syst Appl 185:115587

    Article  Google Scholar 

  11. Chang Y, Zhou W, Cai H, Fan W, Hu L, Wen J (2023) Meta-relation assisted knowledge-aware coupled graph neural network for recommendation. Inf Process Manag 60(3):103353

    Article  Google Scholar 

  12. Gwadabe TR, Liu Y (2022) Improving graph neural network for session-based recommendation system via non-sequential interactions. Neurocomputing 468:111–122

    Article  Google Scholar 

  13. Chen W, Ren P, Cai F, Sun F, de Rijke M (2020) Improving end-to-end sequential recommendations with intent-aware diversification. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. pp 175–184

  14. Wang S, Hu L, Wang Y, Sheng QZ, Orgun M, Cao L (2020) Intention nets: psychology-inspired user choice behavior modeling for next-basket prediction. Proceedings of the AAAI Conference on Artificial Intelligence 34:6259–6266

    Article  Google Scholar 

  15. Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) Stamp: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp 1831–1839

  16. Kang W-C, McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM). pp 197–206. IEEE

  17. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. pp 285–295

  18. Jannach D, Ludewig M (2017) When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems. pp 306–310

  19. Garg D, Gupta P, Malhotra P, Vig L, Shroff G (2019) Sequence and time aware neighborhood for session-based recommendations: Stan. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 1069–1072

  20. Shani G, Heckerman D, Brafman RI, Boutilier C (2005) An mdp-based recommender system. J Mach Learn Res 6(9)

  21. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web. pp 811–820

  22. Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X (2015) Learning hierarchical representation model for nextbasket recommendation. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 403–412

  23. Xue Z, He G, Liu J, Jiang Z, Zhao S, Lu W (2023) Re-examining lexical and semantic attention: Dual-view graph convolutions enhanced BERT for academic paper rating. Inf Process Manag 60(2):103216

    Article  Google Scholar 

  24. Li R, Mai Z, Zhang Z, Jang J, Sanner S (2023) Transcam: Transformer attention-based cam refinement for weakly supervised semantic segmentation. J Vis Commun Image Represent 92:103800

    Article  Google Scholar 

  25. Ohri K, Kumar M (2021) Review on self-supervised image recognition using deep neural networks. Knowl-Based Syst 224:107090

    Article  Google Scholar 

  26. Paoletti M, Haut J, Plaza J, Plaza A (2018) A new deep convolutional neural network for fast hyperspectral image classification. ISPRS J Photogramm Remote Sens 145:120–147

    Article  Google Scholar 

  27. Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: International Conference on Learning Representations. pp 1-10

  28. Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. pp 1419–1428

  29. Wang M, Ren P, Mei L, Chen Z, Ma J, de Rijke M (2019) A collaborative session-based recommendation approach with parallel memory modules. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 345–354

  30. Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 33:346–353

    Article  Google Scholar 

  31. Xu C, Zhao P, Liu Y, Sheng VS, Xu J, Zhuang F, Fang J, Zhou X (2019) Graph contextualized self-attention network for session-based recommendation. IJCAI 19:3940–3946

    Google Scholar 

  32. Chen T, Wong RC-W (2020) Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp 1172–1180

  33. Wang Z, Wei W, Cong G, Li X-L, Mao X-L, Qiu M (2020) Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 169–178

  34. Qiu R, Yin H, Huang Z, Chen T (2020) Gag: Global attributed graph neural network for streaming session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 669–678

  35. Liu L, Wang L, Lian T (2021) Case4sr: Using category sequence graph to augment session-based recommendation. Knowl-Based Syst 212:106558

    Article  Google Scholar 

  36. Wang X, Zhu M, Bo D, Cui P, Shi C, Pei J (2020) Am-gcn: Adaptive multi-channel graph convolutional networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp 1243–1253

  37. Zhang X, Zitnik M (2020) Gnnguard: Defending graph neural networks against adversarial attacks. Advances in Neural Information Processing Systems 33:9263–9275

    Google Scholar 

  38. Nie W, Chang R, Ren M, Su Y, Liu A (2021) I-gcn: incremental graph convolution network for conversation emotion detection. IEEE Transactions on Multimedia

  39. Chen H, Li Y (2022) Multi-constraints in deep graph convolutional networks with initial residual. Appl Intell 1–13

  40. Yu D, Zhang R, Jiang Z, Wu Y, Yang Y (2020) Graph-revised convolutional network. Joint European conference on machine learning and knowledge discovery in databases. Springer, pp 378–393

  41. Zhao T, Liu Y, Neves L, Woodford O, Jiang M, Shah N (2021) Data augmentation for graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35:11015-11023

    Article  Google Scholar 

  42. Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR Conference on Research and Development in Information Retrieval. pp 165–174

  43. Zheng Y, Gao C, Chen L, Jin D, Li Y (2021) Dgcn: Diversified Recommendation with Graph Convolutional Networks. Proceedings of the Web Conference 2021:401–412

    Google Scholar 

  44. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: Simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp 639–648

  45. Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. Proceedings of the AAAI Conference on Artificial Intelligence 35:4503–4511

    Article  Google Scholar 

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Acknowledgements

We thank the handling editors and reviewers for their effort and constructive expert comments. This work is supported by the National Natural Science Foundation of China (No.72271024, No.71871019, No.71471016).

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Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, China

    Shanshan Hua & Mingxin Gan

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  1. Shanshan Hua
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  2. Mingxin Gan
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Contributions

Shanshan Hua: Conceptualization, Data curation, Methodology, Validation, Visualization, Writing-original draft. Mingxin Gan: Funding acquisition, Project administration, Resources, Supervision, writing-review and editing.

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Correspondence to Mingxin Gan.

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Hua, S., Gan, M. Intention-aware denoising graph neural network for session-based recommendation. Appl Intell 53, 23097–23112 (2023). https://doi.org/10.1007/s10489-023-04736-9

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