Abstract
The session-based recommendation predicts the next user’s interest item based on an anonymous user–item interaction sequence. However, most existing methods focus on capturing sequential signals or item-transition patterns within the current session while ignoring potential collaborative behaviors among different users from other sessions that could positively affect the recommended performance of the current session. To address these issues, we propose a Neighbor-Enhanced Graph Transition Network , which uses a diverse graph neural network to model complex interactions at the item level between the current session and its neighboring sessions. We create a Current Feature Encoder to investigate the user’s current preference and a Neighbor Feature Encoder to generate useful collaborative information by considering the popularity of item-transition pairs from neighbor sessions. Then, we propose a fusion function that combines the two types of features mentioned above. We use a positional attention mechanism to investigate the impact of items in different positions on the user’s true intention. The experimental results over three real-world datasets demonstrate that our proposed model generally outperforms other state-of-the-art methods.







Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Jannach D, Ludewig M, Lerche L (2017) Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model User-Adap Inter 27(3):351–392. https://doi.org/10.1007/s11257-017-9194-1
Wang S, Cao L, Wang Y, Sheng QZ, Orgun MA, Lian D (2021) A survey on session-based recommender systems. ACM Comput Surv (CSUR) 54(7):1–38. https://doi.org/10.1145/3465401
Shani G, Heckerman D, Brafman RI, Boutilier C (2005) An mdp-based recommender system. J Mach Learn Res 6(9). http://jmlr.org/papers/v6/shani05a.html
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. https://doi.org/10.1145/1772690.1772773
Zimdars A, Chickering DM, Meek C (2013) Using temporal data for making recommendations. arXiv preprint arXiv:1301.2320
Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939
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. https://doi.org/10.1145/3132847.3132926
Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 346–353. https://doi.org/10.1609/aaai.v33i01.3301346
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. In: IJCAI, vol 19, pp 3940–3946. https://doi.org/10.24963/ijcai.2019/547
Qiu R, Li J, Huang Z, Yin H (2019) Rethinking the item order in session-based recommendation with graph neural networks. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 579–588. https://doi.org/10.1145/3357384.3358010
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. https://doi.org/10.1145/3394486.3403170
Pan Z, Cai F, Chen W, Chen H, de Rijke M (2020) Star graph neural networks for session-based recommendation. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 1195–1204. https://doi.org/10.1145/3340531.3412014
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. https://doi.org/10.1145/3331184.3331210
Luo A, Zhao P, Liu Y, Zhuang F, Wang D, Xu J, Fang J, Sheng VS (2020) Collaborative self-attention network for session-based recommendation. In: IJCAI, pp 2591–2597. https://doi.org/10.24963/ijcai.2020/359
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. https://doi.org/10.1145/3219819.3219950
Ye R, Zhang Q, Luo H (2020) Cross-session aware temporal convolutional network for session-based recommendation. In: 2020 International conference on data mining workshops (ICDMW). IEEE, pp 220–226. https://doi.org/10.1109/ICDMW51313.2020.00039
Eirinaki M, Vazirgiannis M, Kapogiannis D (2005) Web path recommendations based on page ranking and markov models. In: Proceedings of the 7th annual ACM international workshop on web information and data management, pp 2–9. https://doi.org/10.1145/1097047.1097050
Tan YK, Xu X, Liu Y (2016) Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st workshop on deep learning for recommender systems, pp 17–22. arXiv:1606.08117
Kang W-C., McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM), pp 197–206. https://doi.org/10.1109/ICDM.2018.00035. IEEE
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp. 5998–6008. arXiv:1706.03762
Yuan J, Song Z, Sun M, Wang X, Zhao WX (2021) Dual sparse attention network for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4635–4643. https://ojs.aaai.org/index.php/AAAI/article/view/16593
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. https://doi.org/10.1145/371920.372071
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. https://doi.org/10.1145/3109859.3109872
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. https://doi.org/10.1145/3331184.3331322
Gori M, Monfardini G, Scarselli F (2005) A new model for learning in graph domains. In: Proceedings. 2005 IEEE International joint conference on neural networks, vol 2. IEEE, pp 729–734. https://doi.org/10.1109/IJCNN.2005.1555942
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International conference on machine learning. PMLR, pp 1263–1272. arXiv:1704.01212
Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493
Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903
Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. User Model User-Adap Inter 28(4):331–390
Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. https://doi.org/10.3115/v1/d14-1179
Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: ICML. https://icml.cc/Conferences/2010/papers/432.pdf
Sun F, Liu J, Wu J, Pei C, Lin X, Ou W, Jiang P (2019) Bert4rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 1441–1450. arXiv:1904.06690
Guo L, Yin H, Wang Q, Chen T, Zhou A, Quoc Viet Hung N (2019) Streaming session-based recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1569–1577. https://doi.org/10.1145/3292500.3330839
Vinyals O, Bengio S, Kudlur M (2015) Order matters: sequence to sequence for sets. arXiv preprint arXiv:1511.06391
Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 35, pp 4503–4511
Feng L, Cai Y, Wei E, Li J (2022) Graph neural networks with global noise filtering for session-based recommendation. Neurocomputing 472:113–123
Kingma DP, Ba J, Adam (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Wu L, Li S, Hsieh C-J, Sharpnack J (2020) Sse-pt: sequential recommendation via personalized transformer. In: Fourteenth ACM conference on recommender systems, pp 328–337. https://doi.org/10.1145/3383313.3412258
Acknowledgements
This work was supported by National Natural Science Foundation of China (NSFC), “From Learning Outcome to Proactive Learning: Towards a Human-centered AI Based Approach to Intervention on Learning Motivation” (No. 62077027), the Department of Science and Technology of Jilin Province, China (20200801002GH), and the European Union’s Horizon 2020 FET Proactive project “WeNet—The Internet of us” (No. 823783).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Yi, Z., Song, R., Li, J. et al. Neighbor-enhanced graph transition network for session-based recommendation. Int. J. Mach. Learn. & Cyber. 14, 1317–1331 (2023). https://doi.org/10.1007/s13042-022-01702-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-022-01702-8