Fetching the paper…
Reading the bibliography…
Sequential recommender systems (SRS) have gained increasing popularity due to their remarkable proficiency in capturing dynamic user preferences.
Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation. In Proceedings of the ACM Web Conference 2022 . 1923–1932
Mingyue Cheng, Zhiding Liu, Qi Liu, Shenyang Ge, and Enhong Chen. 2022 · 1932
Earlier work this paper cites.
Matrix factorization techniques for recommender systems
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009 · 2009
Earlier work this paper cites.
Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on World wide web . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
BPR: Bayesian personalized ranking from implicit feedback
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012 · 2012
Earlier work this paper cites.
Optimizing top-n collaborative filtering via dynamic negative item sampling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval . 785–788
Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013 · 2013
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015 · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems . 191–198
Paul Covington, Jay Adams, and Emre Sargin. 2016 · 2016
Earlier work this paper cites.
Parallel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM conference on recommender systems . 241–248
Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Chia-Wei Liu, Ryan Lowe, Iulian V Serban, Michael Noseworthy, Laurent Charlin, and Joelle Pineau. 2016 · 2016
Earlier work this paper cites.
DeepFM: a factorization-machine based neural network for CTR prediction
Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. 2017 · 2017
Earlier work this paper cites.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Earlier work this paper cites.
Deep & cross network for ad click predictions
Ruoxi Wang, Bin Fu, Gang Fu, and Mingliang Wang. 2017 · 2017
Earlier work this paper cites.
Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM) . IEEE, 197–206
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Illuminating recommendation by understanding the explicit item relations
Qi Liu, Hong-Ke Zhao, Le Wu, Zhi Li, and En-Hong Chen. 2018 · 2018
Earlier work this paper cites.
Personalized top-n sequential recommendation via convolutional sequence embedding. In Proceedings of the eleventh ACM international conference on web search and data mining . 565–573
Jiaxi Tang and Ke Wang. 2018 · 2018
Cited alongside, same era.
Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 2019
Cited alongside, same era.
BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management . 1441–1450
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019 · 2019
Cited alongside, same era.
Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 346–353
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
Cited alongside, same era.
One Person, One Model–Learning Compound Router for Sequential Recommendation
Zhiding Liu, Mingyue Cheng, Qi Liu, Enhong Chen, et al · 2022
Later among the works it cites.
Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining . 813–823
Ruihong Qiu, Zi Huang, Hongzhi Yin, and Zijian Wang. 2022 · 2022
Later among the works it cites.
Sequential modeling with multiple attributes for watchlist recommendation in e-commerce. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . 937–946
Uriel Singer, Haggai Roitman, Yotam Eshel, Alexander Nus, Ido Guy, Or Levi, Idan Hasson, and Eliyahu Kiperwasser. 2022 · 2022
Later among the works it cites.
Contrastive learning for sequential recommendation. In 2022 IEEE 38th international conference on data engineering (ICDE) . IEEE, 1259–1273
Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Graph contextualized self-attention network for session-based recommendation.. In IJCAI , Vol. 19. 3940–3946
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019 · 2019
Cited alongside, same era.
A simple convolutional generative network for next item recommendation. In Proceedings of the twelfth ACM international conference on web search and data mining . 582–590
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M Jose, and Xiangnan He. 2019 · 2019
Cited alongside, same era.
Search-based user interest modeling with lifelong sequential behavior data for click-through rate prediction. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management . 2685–2692
Qi Pi, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Xiaoqiang Zhu, and Kun Gai. 2020 · 2020
Cited alongside, same era.
User behavior retrieval for click-through rate prediction. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 2347–2356
Jiarui Qin, Weinan Zhang, Xin Wu, Jiarui Jin, Yuchen Fang, and Yong Yu. 2020 · 2020
Cited alongside, same era.
Learning recommender systems with implicit feedback via soft target enhancement. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval . 575–584
Mingyue Cheng, Fajie Yuan, Qi Liu, Shenyang Ge, Zhi Li, Runlong Yu, Defu Lian, Senchao Yuan, and Enhong Chen. 2021 · 2021
Cited alongside, same era.
Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation. In Proceedings of the 15th ACM Conference on Recommender Systems . 143–153
Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge. 2021 · 2021
Cited alongside, same era.
Lightweight self-attentive sequential recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 967–977
Yang Li, Tong Chen, Peng-Fei Zhang, and Hongzhi Yin. 2021 · 2021
Cited alongside, same era.
The world is binary: Contrastive learning for denoising next basket recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval . 859–868
Yuqi Qin, Pengfei Wang, and Chenliang Li. 2021 · 2021
Cited alongside, same era.
Recommender Transformers with Behavior Pathways
Zhiyu Yao, Xinyang Chen, Sinan Wang, Qinyan Dai, Yumeng Li, Tanchao Zhu, and Mingsheng Long. 2022 · 2022
Later among the works it cites.
A revisiting study of appropriate offline evaluation for top-N recommendation algorithms
Wayne Xin Zhao, Zihan Lin, Zhichao Feng, Pengfei Wang, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
Filter-enhanced MLP is all you need for sequential recommendation. In Proceedings of the ACM Web Conference 2022 . 2388–2399
Kun Zhou, Hui Yu, Wayne Xin Zhao, and Ji-Rong Wen. 2022 · 2022
Later among the works it cites.
A Survey on User Behavior Modeling in Recommender Systems
Zhicheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, and Ruiming Tang. 2023 · 2023
Closest in time.
AutoDenoise: Automatic Data Instance Denoising for Recommendations
Weilin Lin, Xiangyu Zhao, Yejing Wang, Yuanshao Zhu, and Wanyu Wang. 2023 · 2023
Closest in time.
Learning to Retrieve User Behaviors for Click-Through Rate Estimation
Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Guangpeng Zhao, Hao Li, Ruiming Tang, Xiuqiang He, and Yong Yu. 2023 · 2023
Closest in time.
Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness. In International Conference on Database Systems for Advanced Applications . Springer, 196–211
Mingyue Cheng, Hao Zhang, Qi Liu, Fajie Yuan, Zhi Li, Zhenya Huang, Enhong Chen, Jun Zhou, and Longfei Li. 2024 · 2024
Closest in time.
Not all tokens are what you need for pretraining
Zhenghao Lin, Zhibin Gou, Yeyun Gong, Xiao Liu, Ruochen Xu, Chen Lin, Yujiu Yang, Jian Jiao, Nan Duan, Weizhu Chen, et al · 2024
Closest in time.
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation
Yucong Luo, Qitao Qin, Hao Zhang, Mingyue Cheng, Ruiran Yan, Kefan Wang, and Jie Ouyang. 2024 · 2024
Closest in time.
A Comprehensive Survey on Cross-Domain Recommendation: Taxonomy, Progress, and Prospects
Hao Zhang, Mingyue Cheng, Qi Liu, Junzhe Jiang, Xianquan Wang, Rujiao Zhang, Chenyi Lei, and Enhong Chen. 2025 · 2025
Closest in time.