Session-Based Recommender Systems - Models and Tasks

Dmitry Yakupov, Dmitry Namiot


Recommender systems were one of the first mass applications of data analysis in various fields. The reason is their final result (recommendations) that is transparent to end users and clear metrics for measuring the quality of their work. End-users can always evaluate the usefulness of recommendations, formal measurements can always operate on conversion, whatever it means - purchases of recommended products, clicks on links, etc. Most often, the work of recommender systems is based on the generalization and analysis of the preferences of other users (which includes consideration of various aspects of their behavior), and the available information about the current user. At the same time, there is a class of tasks when recommendations should (or only can) be based on the current actions of the user. For example, in an e-commerce system, an unauthorized (anonymous) user visits various pages of a site. Or the user's preferences in the system are only short-term. All these examples are typical for a separate large class of recommender systems - recommender systems for sessions, where a session is understood as a sequence of user actions. The recommender system in this case solves one of three tasks: recommends the next product (content, activity, etc.) within the current session, recommends the following products (activities, etc.) until the end of the current session, recommends the next possible session. The article contains an overview of the described tasks and models for such recommender systems.

Full Text:

PDF (Russian)


Deuk Hee Park, Hyea Kyeong Kim, Il Young Choi, Jae Kyeong Kim. A Literature Review and Classification of Recommender Systems on Academic Journals. // Journal of Intelligence and Information Systems. 2011.

Shoujin Wang, Gabriella Pasi, Liang Hu, and Longbing Cao. The Era of Intelligent Recommendation: Editorial on Intelligent Recommendation with Advanced AI and Learning. // IEEE Intelligent Systems. 2020.

Charu C Aggarwal. Content-based recommender systems. // Recommender Systems. Springer. 2016.

J Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. Collaborative filtering recommender systems. // Adaptive Web. Springer. 2007.

Robin Burke. Hybrid recommender systems: survey and experiments. // User Modeling and User-Adapted Interaction 12(4). 2002.

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, and Defu Lian. A Survey on Session-based Recommender Systems. // ACM Comput. Surv. 9, 4, Article 39. 2021.

Dietmar Jannach, Malte Ludewig, and et al. Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. // User Modeling and User-Adapted Interaction 27(6). 2017.

Shahab Saquib Sohail, Jamshed Siddiqui, Rashid Ali. Classifications of Recommender Systems: A review. // Engineering Science and Technology Review. 2017.

J. Bobadilla, F. Ortega, A. Hernando, A. Gutierrez. Recommender systems survey. // Recommender systems survey. Knowledge-Based Systems 46, с. 109–132. 2013.

Fajie Yuan, Alexandros Karatzoglou и др. A simple convolutional generative network for next item recommendation. // WSDM, с. 582–590. 2019.

Shoujin Wang, Liang Hu, Yan Wang и др. Sequential recommender systems: challenges, progress and prospects. // IJCAI. AAAI Press, с. 6332–6338. 2019.

Wenjing Meng, Deqing Yang, Yanghua Xiao. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. // SIGIR. 2020.

Ivica Obadic, Gjorgji Madjarov, Ivica Dimitrovski, and Dejan Gjorgjevikj. Addressing Item-Cold Start Problem in Recommendation Systems using Model Based Approach and Deep Learning. // ICT Innovations. 2017.

Yibo Chen, Chanle Wu, Ming Xie, Xiaojun Guo. Solving the Sparsity Problem in Recommender Systems Using Association Retrieval. // Journal of Computers 6(9), c. 1896-1902. 2011.

Malte Ludewig, Noemi Mauro и др. Performance comparison of neural and non-neural approaches to session-based recommendation. // RecSys. с. 462–466. 2019.

Shuai Zhang, Lina Yao, Aixin Sun, Yi Tay. Deep learning based recommender system: a survey and new perspectives. // CSUR (52, 1), c. 1–38. 2019.

Balzs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk. Session-based recommendations with recurrent neural networks. // ICLR, c. 1–10. 2016.

Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai и др. Hierarchical temporal convolutional networks for dynamic recommender systems. // WWW. с. 2236–2246. 2019.

Feng Yu, Qiang Liu и др. A dynamic recurrent model for next basket recommendation. // SIGIR. ACM, с. 729–732. 2016.

Qiao Liu, Yifu Zeng, Refuoe Mokhosi, Haibin Zhang. STAMP: short-Term attention/memory priority model for session-based recommendation. // SIGKDD. ACM, с. 1831–1839. 2018.

Shoujin Wang, Liang Hu, Longbing Cao и др. Attention-based transactional context embedding for next-item recommendation. // AAAI, с. 2532–2539. 2018.

Steffen Rendle, Christoph Freudenthaler, Lars Schmidt-Thieme. Factorizing personalized markov chains for next-basket recommendation. // WWW. ACM, с. 811–820. 2010.

Jiawei Han, Jian Pei, Yiwen Yin. Mining frequent patterns without candidate generation. // ACM Sigmod Record, Vol. 29. ACM, 1–12. 2000.

Bamshad Mobasher and et al. 2001. Effective personalization based on association rule discovery from web usage data. In WIDM. ACM, 9–15.

Shoujin Wang and Longbing Cao. 2017. Inferring implicit rules by learning explicit and hidden item dependency. IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, 3 (2017), 935–946.

R Forsati, MR Meybodi, and A Ghari Neiat. 2009. Web page personalization based on weighted association rules. In ICECT. IEEE, 130–135.

Liang Yan and Chunping Li. 2006. Incorporating pageview weight into an association-rule-based web recommendation system. In AI. Springer, 577–586.

Marнa N Moreno, Francisco J Garcнa, and et al. 2004. Using association analysis of web data in recommender systems. In EC-Web. Springer, 11–20.

Bo Shao, Dingding Wang, Tao Li, and Mitsunori Ogihara. 2009. Music recommendation based on acoustic features and user access patterns. IEEE Transactions on Audio, Speech, and Language Processing 17, 8 (2009), 1602–1611.

Ghim-Eng Yap, Xiao-Li Li, and S Yu Philip. 2012. Effective next-items recommendation via personalized sequential pattern mining. In DASFAA. Springer, 48–64.

Utpala Niranjan, RBV Subramanyam, and V Khanaa. 2010. Developing a web recommendation system based on closed sequential patterns. In ICT. Springer, 171–179.

Wei Song and Kai Yang. 2014. Personalized recommendation based on weighted sequence similarity. In Practical Applications of Intelligent Systems. Springer, 657–666.

Keunho Choi, Donghee Yoo, Gunwoo Kim, and Yongmoo Suh. 2012. A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electronic Commerce Research and Applications 11, 4 (2012), 309–317.

Duen-Ren Liu, Chin-Hui Lai, and Wang-Jung Lee. 2009. A hybrid of sequential rules and collaborative filtering for product recommendation. Information Sciences 179, 20 (2009), 3505–3519.

Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2017. Deep reinforcement learning for list-wise recommendations. arXiv preprint arXiv:1801.00209 (2017).

ShoujinWang, Liang Hu, YanWang, and et al. 2020. Intention2Basket: a neural intention-driven approach for dynamic next-basket planning. In IJCAI. 2333–2339.

Malte Ludewig and Dietmar Jannach. 2018. Evaluation of session-based recommendation algorithms. UMUAI 28, 4-5 (2018), 331–390.

Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In RecSys. ACM, 306–310.

Guy Shani, David Heckerman, and Ronen I Brafman. 2005. An MDP-based recommender system. JMLR 6, Sep (2005), 1265–1295.

Magdalini Eirinaki, Michalis Vazirgiannis, and et al. 2005. Web path recommendations based on page ranking and markov models. In WIDM. ACM, 2–9.

Zhiyong Zhang and Olfa Nasraoui. 2007. Efficient hybrid Web recommendations based on Markov click stream models and implicit search. In WI. 621–627.

Shuo Chen, Josh L Moore, and et al. 2012. Playlist prediction via metric embedding. In SIGKDD. ACM, 714–722.

Xiang Wu, Qi Liu, Enhong Chen, Liang He, and et al. 2013. Personalized next-song recommendation in online karaokes. In RecSys. ACM, 137–140.

Negar Hariri, Bamshad Mobasher, and Robin Burke. 2012. Context-aware music recommendation based on latent topic sequential patterns. In RecSys. 131–138.

Elena Zheleva, John Guiver, Eduarda Mendes Rodrigues, and et al. 2010. Statistical models of music-listening sessions in social media. In WWW. 1019–1028.

Priit Järv. 2019. Predictability limits in session-based next item recommendation. In RecSys. 146–150.

Hans-Jürgen Bandelt and Andreas WM Dress. 1992. A canonical decomposition theory for metrics on a finite set. Advances in Mathematics 92, 1 (1992), 47–105.

Fajie Yuan, Alexandros Karatzoglou, and et al. 2019. A simple convolutional generative network for next item recommendation. In WSDM. 582–590.

Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. 2013. Where you like to go next: successive point-of-interest recommendation. In IJCAI. 2605–2611.

Dawen Liang, Jaan Altosaar, and et al. 2016. Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In RecSys. ACM, 59–66.

Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In EMNLP. 1532–1543.

Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013. Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168 (2013).

Liang Hu, Longbing Cao, ShoujinWang, and et al. 2017. Diversifying personalized recommendation with user-session context. In IJCAI. 1858–1864.

Shoujin Wang, Liang Hu, and Longbing Cao. 2017. Perceiving the next choice with comprehensive transaction embeddings for online recommendation. In ECML-PKDD. Springer, 285–302.

Xiangyu Zhao, Long Xia, Liang Zhang, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2018. Deep reinforcement learning for page-wise recommendations. In RecSys. 95–103.

Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In DLRS. ACM, 17–22.

Chen Wu and Ming Yan. 2017. Session-aware information embedding for e-commerce product recommendation. In CIKM. ACM, 2379–2382.

Dietmar Jannach, Malte Ludewig, and et al. 2017. Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. UMUAI 27, 3-5 (2017), 351–392.

Yang Song and et al. 2016. Multi-rate deep learning for temporal recommendation. In SIGIR. ACM, 909–912.

Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, and et al. 2020. Future data helps training: modeling future contexts for session-based recommendation. In The Web Conference. 303–313.

Jiaxi Tang and KeWang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565–573.

Trinh Xuan Tuan and Tu Minh Phuong. 2017. 3D convolutional networks for session-based recommendation with content features. In RecSys. ACM, 138–146.

Keunchan Park, Jisoo Lee, and Jaeho Choi. 2017. Deep neural networks for news recommendations. In CIKM. ACM, 2255–2258.

Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, and et al. 2019. Graph contextualized self-attention network for session based recommendation. In IJCAI. 3940–3946.

Feng Yu and et al. 2020. TAGNN: target attentive graph neural networks for session-based recommendation. In SIGIR.1–5.

Wen Wang, Wei Zhang, Shukai Liu, and et al. 2020. Beyond clicks: modeling multi-relational item graph for sessionbased target behavior prediction. In The Web Conference. 3056–3062.

Ruihong Qiu, Jingjing Li, Zi Huang, and Hongzhi Yin. 2019. Rethinking the item order in session-based recommendation with graph neural networks. In CIKM. 579–588.

Mao Ye, Xingjie Liu, and Wang-Chien Lee. 2012. Exploring social influence for recommendation: a generative model approach. In SIGIR. 671–680.

Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin. 2020. Exploiting cross-session information for session-based recommendation with graph neural networks. TOIS 38 (2020), 1–23. Issue 3

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NIPS. 5998–6008.

Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. 2019. Modeling multipurposesessions for next-item recommendations via mixture-channel purpose routing networks. In IJCAI. AAAI Press, 3771–3777.

Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky, Xiaoshui Huang, Lin Xiao, and Wenpeng Lu. 2020. Jointly modeling intra- and inter-transaction dependencies with hierarchical attentive transaction embeddings for next-item recommendation. IEEE Intelligent Systems (2020), 1–7.

Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, and et al. 2018. Sequential recommender system based on hierarchical attention network. In IJCAI. 3926–3932.

Xu Chen, Hongteng Xu, Yongfeng Zhang, and et al. 2018. Sequential recommendation with user memory networks. In WSDM. 108–116.

Adam Santoro, Sergey Bartunov, Matthew Botvinick, and et al. 2016. Meta-learning with memory-augmented neural networks. In ICML. 1842–1850.

Meirui Wang, Pengjie Ren, Lei Mei, Zhumin Chen, Jun Ma, and Maarten de Rijke. 2019. A collaborative session-based recommendation approach with parallel memory modules. In SIGIR. 345–354.

Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, and et al. 2019. Towards neural mixture recommender for long range dependent user sequences. In WWW. 1782–1793.

Riccardo Guidotti, Giulio Rossetti, Luca Pappalardo, Fosca Giannotti, and Dino Pedreschi. 2017. Market basket prediction using user-centric temporal annotated recurring sequences. In ICDM. IEEE, 895–900.

Guglielmo Faggioli, Mirko Polato, and Fabio Aiolli. 2020. Recency aware collaborative filtering for next basket recommendation. In UMAP. 80–87.

Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for next basket recommendation. In SIGIR. ACM, 403–412.

Duc-Trong Le, Hady W Lauw, and Yuan Fang. 2019. Correlation-sensitive next-basket recommendation. In IJCAI. AAAI Press, 2808–2814.

Haoji Hu, Xiangnan He, Jinyang Gao, and Zhi-Li Zhang. 2020. Modeling personalized item frequency information for next-basket recommendation. In SIGIR. ACM.

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012).

Ruining He and Julian McAuley. 2016. Fusing similarity models with markov chains for sparse sequential recommendation. In ICDM. IEEE, 191–200.

Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM. 843–852.

Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, and et al. 2017. Neural attentive session-based recommendation. In CIKM. ACM, 1419–1428.

Shu Wu, Yuyuan Tang, Yanqiao Zhu, and et al. 2019. Session-based recommendation with graph neural networks. In AAAI. 346–353.

Pengjie Ren, Zhumin Chen, Jing Li, and et al. 2019. RepeatNet: a repeat aware neural recommendation machine for session-based recommendation. In AAAI, Vol. 33. 4806–4813.

Bo Song, Yi Cao, and et al. 2019. Session-based recommendation with hierarchical memory networks. In CIKM. 2181–2184.

Tianwen Chen and Raymond Chi-Wing Wong. 2020. Handling information loss of graph neural networks for session-based recommendation. In SIGKDD. 1172–1180.

List of Recommender Systems. Retrieved: May, 2022.

NVIDIA-Merlin/Transformers4Rec. Retrieved: May, 2022.

Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, Even Oldridge. Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation. RecSys '21: Fifteenth ACM Conference on Recommender Systems. September 2021, pages 143–153.

Gabriel de Souza Pereira Moreira, Felipe Ferreira, and Adilson Marques da Cunha. 2018. News session-based recommendations using deep neural networks. In Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems. 15–23.

Gabriel De Souza Pereira Moreira, Dietmar Jannach, and Adilson Marques Da Cunha. 2019. Contextual hybrid session-based news recommendation with recurrent neural networks. IEEE Access 7 (2019), 169185–169203.

Shiming Sun, Yuanhe Tang, Zemei Dai, and Fu Zhou. 2019. Self-attention network for session-based recommendation with streaming data input. IEEE Access 7 (2019), 110499–110509.2.

Artificial Intelligence in Cybersecurity. (in Russian) Retrieved: May, 2022.

Ilyushin, Eugene, Dmitry Namiot, and Ivan Chizhov. "Attacks on machine learning systems-common problems and methods." International Journal of Open Information Technologies 10.3 (2022): 17-22. (in Russian)

Namiot, Dmitry, Eugene Ilyushin, and Ivan Chizhov. "The rationale for working on robust machine learning." International Journal of Open Information Technologies 9.11 (2021): 68-74.


  • There are currently no refbacks.

Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162