Shoujin Wang
Quan Z. Sheng
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Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.
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- Gediminas Adomavicius and Alexander Tuzhilin. 2015. Context-aware recommender systems. In Recommender Systems Handbook. Springer, 191–226.Google Scholar
- Charu C. Aggarwal. 2016. Content-based recommender systems. In Recommender Systems. Springer, 139–166.Google Scholar
- Susan C. Anyosa, João Vinagre, and Alípio M. Jorge. 2018. Incremental matrix co-factorization for recommender systems with implicit feedback. In Companion of the Web Conference. 1413–1418.Google Scholar
- Hans-Jürgen Bandelt and Andreas W. M. Dress. 1992. A canonical decomposition theory for metrics on a finite set. Adv. Math. 92, 1 (1992), 47–105.Google ScholarCross Ref
- Alex Beutel, Paul Covington, Sagar Jain, et al.2018. Latent cross: Making use of context in recurrent recommender systems. In WSDM. ACM, 46–54.Google Scholar
- Veronika Bogina and Tsvi Kuflik. 2017. Incorporating dwell time in session-based recommendations with recurrent neural networks. In Proceedings of the RecTemp Workshop co-located with ACM RecSys’17. 57–59.Google Scholar
- Robin Burke. 2002. Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact. 12, 4 (2002), 331–370.Google ScholarDigital Library
- Longbing Cao. 2015. Coupling learning of complex interactions. Inf. Process. Manage. 51, 2 (2015), 167–186.Google ScholarCross Ref
- Longbing Cao. 2016. Non-IID recommender systems: A review and framework of recommendation paradigm shifting. Engineering 2, 2 (2016), 212–224.Google ScholarCross Ref
- Longbing Cao. 2018. Data Science Thinking: The Next Scientific, Technological and Economic Revolution. Springer.Google Scholar
- Sotirios P. Chatzis, Panayiotis Christodoulou, et al.2017. Recurrent latent variable networks for session-based recommendation. In DLRS. 38–45.Google Scholar
- Shuo Chen, Josh L. Moore, et al.2012. Playlist prediction via metric embedding. In SIGKDD. ACM, 714–722.Google Scholar
- Tianwen Chen and Raymond Chi-Wing Wong. 2020. Handling information loss of graph neural networks for session-based recommendation. In SIGKDD. 1172–1180.Google Scholar
- Wanyu Chen, Fei Cai, et al.2019. A dynamic co-attention network for session-based recommendation. In CIKM. 1461–1470.Google Scholar
- Xu Chen, Hongteng Xu, Yongfeng Zhang, et al.2018. Sequential recommendation with user memory networks. In WSDM. 108–116.Google Scholar
- 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.Google Scholar
- Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, et al.2016. Wide & deep learning for recommender systems. In DLRS. ACM, 7–10.Google Scholar
- 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. Electr. Commerce Res. Appl. 11, 4 (2012), 309–317.Google ScholarDigital Library
- Panayiotis Christodoulou, Sotirios P. Chatzis, et al.2017. A variational recurrent neural network for session-based recommendations using bayesian personalized ranking. In ISD. 1–9.Google Scholar
- Tim Donkers, Benedikt Loepp, and Jürgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. In RecSys. ACM, 152–160.Google Scholar
- Magdalini Eirinaki, Michalis Vazirgiannis, et al.2005. Web path recommendations based on page ranking and markov models. In WIDM. ACM, 2–9.Google Scholar
- Michael D. Ekstrand, John T. Riedl, Joseph A. Konstan, et al. 2011. Collaborative filtering recommender systems. Found. Trends Hum.–Comput. Interact. 4, 2 (2011), 81–173.Google Scholar
- Ali Mamdouh Elkahky, Yang Song, et al.2015. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In WWW. 278–288.Google Scholar
- Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. 2020. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. Trans. Inf. Syst. 39, 1 (2020), 1–42.Google ScholarDigital Library
- Shanshan Feng, Xutao Li, Yifeng Zeng, et al.2015. Personalized ranking metric embedding for next new POI recommendation. In IJCAI. 2069–2075.Google Scholar
- Dušan Fister, Matjaž Perc, and Timotej Jagrič. 2021. Two robust long short-term memory frameworks for trading stocks. Appl Intell (2021). https://doi.org/10.1007/s10489-021-02249-xGoogle ScholarDigital Library
- R. Forsati, M. R. Meybodi, and A. Ghari Neiat. 2009. Web page personalization based on weighted association rules. In ICECT. IEEE, 130–135.Google Scholar
- P. Moreira Gabriel De Souza, Dietmar Jannach, and Adilson Marques Da Cunha. 2019. Contextual hybrid session-based news recommendation with recurrent neural networks. IEEE Access 7 (2019), 169185–169203.Google ScholarCross Ref
- Diksha Garg, Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig, and Gautam Shroff. 2019. Sequence and time aware neighborhood for session-based recommendations: Stan. In SIGIR. 1069–1072.Google Scholar
- Asnat Greenstein-Messica, Lior Rokach, and Michael Friedman. 2017. Session-based recommendations using item embedding. In IUI. ACM, 629–633.Google Scholar
- Lei Guo, Hongzhi Yin, Qinyong Wang, et al.2019. Streaming session-based recommendation. In SIGKDD. 1569–1577.Google Scholar
- Kyle Haas, Stuart Morton, et al.2019. Using similarity metrics on real world data and patient treatment pathways to recommend the next treatment. In AMIA Summits on Translational Science Proceedings, 398.Google Scholar
- Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining frequent patterns without candidate generation. In ACM Sigmod Record, Vol. 29. ACM, 1–12.Google ScholarDigital Library
- Negar Hariri, Bamshad Mobasher, and Robin Burke. 2012. Context-aware music recommendation based on latent topic sequential patterns. In RecSys. 131–138.Google Scholar
- Ruining He and Julian McAuley. 2016. VBPR: Visual bayesian personalized ranking from implicit feedback. In AAAI. 144–150.Google Scholar
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In SIGIR. ACM, 549–558.Google Scholar
- Balázs Hidasi and Alexandros Karatzoglou. 2018. Recurrent neural networks with top-k gains for session-based recommendations. In CIKM. 843–852.Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based recommendations with recurrent neural networks. In ICLR. 1–10.Google Scholar
- Balázs Hidasi, Massimo Quadrana, et al.2016. Parallel recurrent neural network architectures for feature-rich session-based recommendations. In RecSys. ACM, 241–248.Google Scholar
- Binbin Hu, Chuan Shi, and Jian Liu. 2017. Playlist recommendation based on reinforcement learning. In ICIS. Springer, 172–182.Google Scholar
- Haoji Hu, Xiangnan He, Jinyang Gao, and Zhi-Li Zhang. 2020. Modeling personalized item frequency information for next-basket recommendation. In SIGIR. 1–10.Google Scholar
- Liang Hu, Jian Cao, Guandong Xu, et al.2013. Cross-domain collaborative filtering via bilinear multilevel analysis. In IJCAI. AAAI Press, 2626–2632.Google Scholar
- Liang Hu, Longbing Cao, Shoujin Wang, et al.2017. Diversifying personalized recommendation with user-session context. In IJCAI. 1858–1864.Google Scholar
- Dietmar Jannach and Malte Ludewig. 2017. Determining characteristics of successful recommendations from log data: A case study. In SAC. ACM, 1643–1648.Google Scholar
- Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In RecSys. ACM, 306–310.Google Scholar
- Dietmar Jannach, Malte Ludewig, et al.2017. Session-based item recommendation in e-commerce: on short-term intents, reminders, trends and discounts. User Model. User-Adapt. Interact. 27, 3-5 (2017), 351–392.Google ScholarDigital Library
- Dietmar Jannach, Bamshad Mobasher, and Shlomo Berkovsky. 2020. Research directions in session-based and sequential recommendation. User Model. User-Adapt. Interact. 30, 4 (2020), 609–616.Google ScholarCross Ref
- Priit Järv. 2019. Predictability limits in session-based next item recommendation. In RecSys. 146–150.Google Scholar
- How Jing and Alexander J. Smola. 2017. Neural survival recommender. In WSDM. ACM, 515–524.Google Scholar
- Duc-Trong Le, Yuan Fang, and Hady W Lauw. 2016. Modeling sequential preferences with dynamic user and context factors. In ECML-PKDD. Springer, 145–161.Google Scholar
- Lukas Lerche, Dietmar Jannach, and Malte Ludewig. 2016. On the value of reminders within e-commerce recommendations. In UMAP. ACM, 27–35.Google Scholar
- Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, et al.2017. Neural attentive session-based recommendation. In CIKM. ACM, 1419–1428.Google Scholar
- Yuqi Liet al.2017. Learning graph-based embedding for time-aware product recommendation. In CIKM. ACM, 2163–2166.Google Scholar
- Zhi Li, Hongke Zhao, Qi Liu, Zhenya Huang, Tao Mei, and Enhong Chen. 2018. Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In SIGKDD. 1734–1743.Google Scholar
- Defu Lian, Vincent W. Zheng, and Xing Xie. 2013. Collaborative filtering meets next check-in location prediction. In WWW. ACM, 231–232.Google Scholar
- Dawen Liang, Jaan Altosaar, et al.2016. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In RecSys. ACM, 59–66.Google Scholar
- Duen-Ren Liu, Chin-Hui Lai, and Wang-Jung Lee. 2009. A hybrid of sequential rules and collaborative filtering for product recommendation. Inf. Sci. 179, 20 (2009), 3505–3519.Google ScholarDigital Library
- Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018. STAMP: Short-Term attention/memory priority model for session-based recommendation. In SIGKDD. ACM, 1831–1839.Google ScholarDigital Library
- Xin Liu, Yong Liu, Karl Aberer, and Chunyan Miao. 2013. Personalized point-of-interest recommendation by mining users’ preference transition. In CIKM. ACM, 733–738.Google Scholar
- Babak Loni, Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Cross-domain collaborative filtering with factorization machines. In ECIR. Springer, 656–661.Google Scholar
- Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook. Springer, 73–105.Google Scholar
- Pablo Loyola, Chen Liu, and Yu Hirate. 2017. Modeling user session and intent with an attention-based encoder-decoder architecture. In RecSys. ACM, 147–151.Google Scholar
- Malte Ludewig and Dietmar Jannach. 2018. Evaluation of session-based recommendation algorithms. User Model. User-Adapt. Interact. 28, 4-5 (2018), 331–390.Google ScholarDigital Library
- Malte Ludewig, Noemi Mauro, et al.2019. Performance comparison of neural and non-neural approaches to session-based recommendation. In RecSys. 462–466.Google Scholar
- Malte Ludewig, Noemi Mauro, Sara Latifi, and Dietmar Jannach. 2021. Empirical analysis of session-based recommendation algorithms. User Model. User-Adapt. Interact. 31, 1 (2021), 149–181.Google ScholarDigital Library
- Wenjing Meng, Deqing Yang, and Yanghua Xiao. 2020. Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In SIGIR. 1–10.Google Scholar
- Fei Mi and Boi Faltings. 2020. Memory augmented neural model for incremental session-based recommendation. In IJCAI. 1–7.Google Scholar
- Fei Mi, Xiaoyu Lin, and Boi Faltings. 2020. Ader: Adaptively distilled exemplar replay towards continual learning for session-based recommendation. In RecSys. 408–413.Google Scholar
- Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013. Exploiting similarities among languages for machine translation. arXiv:1309.4168. Retrieved from https://arxiv.org/abs/1309.4168.Google Scholar
- Bamshad Mobasheret al.2001. Effective personalization based on association rule discovery from web usage data. In WIDM. ACM, 9–15.Google Scholar
- María N. Moreno, Francisco J. García, et al.2004. Using association analysis of web data in recommender systems. In EC-Web. Springer, 11–20.Google Scholar
- Utpala Niranjan, R. B. V. Subramanyam, and V. Khanaa. 2010. Developing a web recommendation system based on closed sequential patterns. In ICT. Springer, 171–179.Google Scholar
- Roberto Pagano, Paolo Cremonesi, et al.2016. The contextual turn: From context-aware to context-driven recommender systems. In RecSys. ACM, 249–252.Google Scholar
- Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. TKDE 22, 10 (2010), 1345–1359.Google ScholarDigital Library
- Weike Pan and Qiang Yang. 2013. Transfer learning in heterogeneous collaborative filtering domains. Artif. Intell. 197 (2013), 39–55.Google ScholarDigital Library
- Keunchan Park, Jisoo Lee, and Jaeho Choi. 2017. Deep neural networks for news recommendations. In CIKM. ACM, 2255–2258.Google Scholar
- Michael J. Pazzani and Daniel Billsus. 2007. Content-based recommendation systems. In The Adaptive Web. Springer, 325–341.Google Scholar
- Wenjie Pei, Jie Yang, Zhu Sun, et al.2017. Interacting attention-gated recurrent networks for recommendation. In CIKM. ACM, 1459–1468.Google Scholar
- Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In EMNLP. 1532–1543.Google Scholar
- Ladislav Peska and Peter Vojtas. 2017. Using implicit preference relations to improve recommender systems. J. Data Semant. 6, 1 (2017), 15–30.Google ScholarCross Ref
- Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin. 2020. Exploiting cross-session information for session-based recommendation with graph neural networks. Trans. Inf. Syst. 38, 3 (2020), 1–23.Google ScholarDigital Library
- 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.Google Scholar
- Ruihong Qiu, Hongzhi Yin, Zi Huang, and Tong Chen. 2020. Gag: Global attributed graph neural network for streaming session-based recommendation. In SIGIR. 669–678.Google ScholarDigital Library
- Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-aware recommender systems. Comput. Surv. 51, 4 (2018), 1–36.Google ScholarDigital Library
- Massimo Quadranaet al.2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In RecSys. ACM, 130–137.Google Scholar
- Pengjie Ren, Zhumin Chen, Jing Li, et al.2019. RepeatNet: a repeat aware neural recommendation machine for session-based recommendation. In AAAI, Vol. 33. 4806–4813.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. ACM, 811–820.Google Scholar
- Massimiliano Ruocco, Ole Steinar Lillestøl Skrede, and Helge Langseth. 2017. Inter-session modeling for session-based recommendation. In DLRS. ACM, 24–31.Google Scholar
- Adam Santoro, Sergey Bartunov, Matthew Botvinick, et al.2016. Meta-learning with memory-augmented neural networks. In ICML. 1842–1850.Google Scholar
- J. Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. 2007. Collaborative filtering recommender systems. In The Adaptive Web. Springer, 291–324.Google Scholar
- Tobias Schnabel, Paul N. Bennett, Susan T. Dumais, and Thorsten Joachims. 2018. Short-term satisfaction and long-term coverage: Understanding how users tolerate algorithmic exploration. In WSDM. ACM, 513–521.Google Scholar
- Guy Shani, David Heckerman, and Ronen I. Brafman. 2005. An MDP-based recommender system. J. Mach. Learn. Res. 6, (Sep. 2005), 1265–1295.Google Scholar
- Bo Shao, Dingding Wang, Tao Li, and Mitsunori Ogihara. 2009. Music recommendation based on acoustic features and user access patterns. IEEE Trans. Aud. Speech Lang. Process. 17, 8 (2009), 1602–1611.Google ScholarCross Ref
- Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. Comput. Surv. 47, 1 (2014), 3.Google ScholarDigital Library
- Elena Smirnova and Flavian Vasile. 2017. Contextual sequence modeling for recommendation with recurrent neural networks. In DLRS. 2–9.Google Scholar
- Bo Song, Yi Cao, et al.2019. Session-based recommendation with hierarchical memory networks. In CIKM. 2181–2184.Google Scholar
- Wei Song and Kai Yang. 2014. Personalized recommendation based on weighted sequence similarity. In Practical Applications of Intelligent Systems. Springer, 657–666.Google Scholar
- Yang Songet al.2016. Multi-rate deep learning for temporal recommendation. In SIGIR. ACM, 909–912.Google Scholar
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved recurrent neural networks for session-based recommendations. In DLRS. ACM, 17–22.Google Scholar
- Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, et al.2019. Towards neural mixture recommender for long range dependent user sequences. In WWW. 1782–1793.Google Scholar
- Jiaxi Tang and Ke Wang. 2018. Personalized top-n sequential recommendation via convolutional sequence embedding. In WSDM. 565–573.Google Scholar
- Md Mehrab Tanjim, Congzhe Su, Ethan Benjamin, et al.2020. Attentive sequential models of latent intent for next item recommendation. In The Web Conference. 2528–2534.Google Scholar
- Maryam Tavakol and Ulf Brefeld. 2014. Factored MDPs for detecting topics of user sessions. In RecSys. 33–40.Google Scholar
- Trinh Xuan Tuan and Tu Minh Phuong. 2017. 3D convolutional networks for session-based recommendation with content features. In RecSys. ACM, 138–146.Google Scholar
- Bartłomiej Twardowski. 2016. Modelling contextual information in session-aware recommender systems with neural networks. In RecSys. ACM, 273–276.Google Scholar
- Moshe Unger. 2015. Latent context-aware recommender systems. In RecSys. ACM, 383–386.Google Scholar
- Flavian Vasile, Elena Smirnova, and Alexis Conneau. 2016. Meta-Prod2Vec: Product embeddings using side-information for recommendation. In RecSys. ACM, 225–232.Google Scholar
- 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.Google Scholar
- Shengxian Wan, Yanyan Lan, Pengfei Wang, Jiafeng Guo, Jun Xu, and Xueqi Cheng. 2015. Next basket recommendation with neural networks. In RecSys. ACM, 1–2.Google Scholar
- Dongjing Wang, Shuiguang Deng, et al.2016. Learning music embedding with metadata for context aware recommendation. In ICMR. ACM, 249–253.Google Scholar
- Hongwei Wang, Fuzheng Zhang, et al.2018. Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In CIKM. 417–426.Google Scholar
- 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.Google Scholar
- Nan Wang, Shoujin Wang, Yan Wang, et al.2020. Modelling local and global dependencies for next-item recommendations. In WISE. Springer, 285–300.Google Scholar
- 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.Google Scholar
- Shoujin Wang and Longbing Cao. 2017. Inferring implicit rules by learning explicit and hidden item dependency. IEEE Trans. Syst. Man Cybernet. Syst. 50, 3 (2017), 935–946.Google ScholarCross Ref
- 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 Intell. Syst. (2020), 1–7. https://doi.org/10.1109/MIS.2020.2997362Google ScholarDigital Library
- 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.Google Scholar
- Shoujin Wang, Liang Hu, Longbing Cao, et al.2018. Attention-based transactional context embedding for next-item recommendation. In AAAI. 2532–2539.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, et al.2019. Sequential recommender systems: challenges, progress and prospects. In IJCAI. AAAI Press, 6332–6338.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, et al.2020. Intention nets: Psychology-inspired user choice behavior modeling for next-basket prediction. In AAAI. 6259–6266.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, et al.2020. Intention2Basket: A neural intention-driven approach for dynamic next-basket planning. In IJCAI. 2333–2339.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, et al.2021. Graph learning based recommender systems: A review. In IJCAI. 1–9.Google Scholar
- Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. 2019. Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In IJCAI. AAAI Press, 3771–3777.Google Scholar
- Shoujin Wang, Gabriella Pasi, Liang Hu, and Longbing Cao. 2020. The era of intelligent recommendation: Editorial on intelligent recommendation with advanced AI and learning. IEEE Intell. Syst. 35, 5 (2020), 3–6.Google ScholarCross Ref
- Wen Wang, Wei Zhang, Shukai Liu, et al.2020. Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In The Web Conference. 3056–3062.Google Scholar
- Zhitao Wang, Chengyao Chen, et al.2018. Variational recurrent model for session-based recommendation. In CIKM. 1839–1842.Google Scholar
- Chen Wu and Ming Yan. 2017. Session-aware information embedding for e-commerce product recommendation. In CIKM. ACM, 2379–2382.Google Scholar
- Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017. Recurrent recommender networks. In WSDM. ACM, 495–503.Google Scholar
- Shu Wu, Yuyuan Tang, Yanqiao Zhu, et al.2019. Session-based recommendation with graph neural networks. In AAAI. 346–353.Google Scholar
- Xiang Wu, Qi Liu, Enhong Chen, Liang He, et al.2013. Personalized next-song recommendation in online karaokes. In RecSys. ACM, 137–140.Google Scholar
- Zhuoran Xiong, Xiao-Yang Liu, Shan Zhong, Hongyang Yang, and Anwar Walid. 2018. Practical deep reinforcement learning approach for stock trading. arXiv:1811.07522. Retrieved from https://arxiv.org/abs/1811.075229059.Google Scholar
- Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, et al.2019. Graph contextualized self-attention network for session-based recommendation. In IJCAI. 3940–3946.Google Scholar
- Liang Yan and Chunping Li. 2006. Incorporating pageview weight into an association-rule-based web recommendation system. In AI. Springer, 577–586.Google Scholar
- 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.Google Scholar
- Mao Ye, Xingjie Liu, and Wang-Chien Lee. 2012. Exploring social influence for recommendation: a generative model approach. In SIGIR. 671–680.Google Scholar
- Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, et al.2018. Sequential recommender system based on hierarchical attention network. In IJCAI. 3926–3932.Google Scholar
- Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai, et al.2019. Hierarchical temporal convolutional networks for dynamic recommender systems. In WWW. 2236–2246.Google Scholar
- Feng Yuet al.2020. TAGNN: Target attentive graph neural networks for session-based recommendation. In SIGIR. 1–5.Google Scholar
- Feng Yu, Qiang Liu, et al.2016. A dynamic recurrent model for next basket recommendation. In SIGIR. ACM, 729–732.Google Scholar
- Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, et al.2020. Future data helps training: modeling future contexts for session-based recommendation. In The Web Conference. 303–313.Google Scholar
- Fajie Yuan, Alexandros Karatzoglou, et al.2019. A simple convolutional generative network for next item recommendation. In WSDM. 582–590.Google Scholar
- Shuai Zhang, Yi Tay, Lina Yao, Aixin Sun, and Jake An. 2019. Next item recommendation with self-attentive metric learning. In RecNLP. 1–9.Google Scholar
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. Comput. Surv. 52, 1 (2019), 1–38.Google ScholarDigital Library
- Zhiyong Zhang and Olfa Nasraoui. 2007. Efficient hybrid Web recommendations based on Markov click stream models and implicit search. In WI. 621–627.Google Scholar
- Wei Zhao, Wenyou Wang, Jianbo Ye, Yongqiang Gao, et al.2017. Leveraging long and short-term information in content-aware movie recommendation. arXiv:1712.09059. Retrieved from https://arxiv.org/abs/1712.09059.Google Scholar
- 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.Google Scholar
- Xiangyu Zhao, Liang Zhang, Long Xia, Zhuoye Ding, Dawei Yin, and Jiliang Tang. 2017. Deep reinforcement learning for list-wise recommendations. arXiv:1801.00209. Retrieved from https://arxiv.org/abs/1801.00209.Google Scholar
- Yan Zhao, Shoujin Wang, Yan Wang, Hongwei Liu, and Weizhe Zhang. 2020. Double-wing mixture of experts for streaming recommendations. In WISE. Springer, 269–284.Google Scholar
- Elena Zheleva, John Guiver, Eduarda Mendes Rodrigues, et al.2010. Statistical models of music-listening sessions in social media. In WWW. 1019–1028.Google Scholar
- Fan Zhou, Zijing Wen, Kunpeng Zhang, Goce Trajcevski, and Ting Zhong. 2019. Variational session-based recommendation using normalizing flows. In WWW. 3476–3482.Google Scholar
- Feng Zhu, Chaochao Chen, et al.2019. DTCDR: a framework for dual-target cross-domain recommendation. In CIKM. 1533–1542.Google Scholar
- Feng Zhu, Yan Wang, Chaochao Chen, et al.2021. Cross-domain recommendation: challenges, progress, and prospects. arXiv:2103.01696. Retrived from https://arxiv.org/abs/2103.01696.Google Scholar
- Lixin Zou, Long Xia, et al.2020. Pseudo dyna-Q: A reinforcement learning framework for interactive recommendation. In WSDM. 816–824.Google Scholar
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