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Large Language Models (LLMs) are emerging as promising approaches to enhance session-based recommendation (SBR), where both prompt-based and fine-tuning-based methods have been widely investigated to align LLMs with SBR.
TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 1921–1924
Feng Yu, Yanqiao Zhu, Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2020 · 1924
Earlier work this paper cites.
Enhancing hypergraph neural networks with intent disentanglement for session-based recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval . 1997–2002
Yinfeng Li, Chen Gao, Hengliang Luo, Depeng Jin, and Yong Li. 2022 · 2002
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 (WWW ’10) . 811–820
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010 · 2010
Earlier work this paper cites.
Session-based recommendations with recurrent neural networks. In 4th International Conference on Learning Representations, ICLR 2016
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. [n. d.] · 2016
Earlier work this paper cites.
Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems . Association for Computing Machinery, 17–22
Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016 · 2016
Earlier work this paper cites.
Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM) . 1419–1428
Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, and Jun Ma. 2017 · 2017
Earlier work this paper cites.
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018 . ACM, 843–852
Balázs Hidasi and Alexandros Karatzoglou. 2018 · 2018
Earlier work this paper cites.
STAMP: Short-term attention/memory priority model for session-based recommendation.. In KDD . ACM, 1831–1839
Qiao Liu, Yifu Zeng, Refuoe Mokhosi, and Haibin Zhang. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Association for Computational Linguistics, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Research commentary on recommendations with side information: A survey and research directions
Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, and Robin Burke. 2019 · 2019
Earlier work this paper cites.
Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . 3771–3777
Shoujin Wang, Liang Hu, Yan Wang, Quan Z. Sheng, Mehmet Orgun, and Longbing Cao. 2019 · 2019
Earlier work this paper cites.
Session-based recommendation with graph neural networks. In Proceedings of the Thirty-Third AAAI Conference
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019 · 2019
Earlier work this paper cites.
Graph contextualized self-attention network for session-based recommendation. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI) . 3940–3946
Chengfeng Xu, Pengpeng Zhao, Yanchi Liu, Victor S. Sheng, Jiajie Xu, Fuzhen Zhuang, Junhua Fang, and Xiaofang Zhou. 2019 · 2019
Earlier work this paper cites.
Incorporating User Micro-Behaviors and Item Knowledge into Multi-Task Learning for Session-Based Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20) . 1091–1100
Wenjing Meng, Deqing Yang, and Yanghua Xiao. 2020 · 2020
Earlier work this paper cites.
Star graph neural networks for session-based recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM) . 1195–1204
Zhiqiang Pan, Fei Cai, Wanyu Chen, Honghui Chen, and Maarten de Rijke. 2020 · 2020
Cited alongside, same era.
Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison. In Proceedings of the 14th ACM Conference on Recommender Systems
Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, and Cong Geng. 2020 · 2020
Cited alongside, same era.
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 . 169–178
Ziyang Wang, Wei Wei, Gao Cong, Xiao-Li Li, Xian-Ling Mao, and Minghui Qiu. 2020 · 2020
Cited alongside, same era.
Language models as recommender systems: Evaluations and limitations. In NeurIPS 2021 Workshop on I (Still) Can’t Believe It’s Not Better
Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021 · 2021
Cited alongside, same era.
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Yunfan Gao, Tao Sheng, Youlin Xiang, Yun Xiong, Haofen Wang, and Jiawei Zhang. 2023 · 2023
Later among the works it cites.
Large language models are zero-shot rankers for recommender systems
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2023 · 2023
Later among the works it cites.
GenRec: Large Language Model for Generative Recommendation
Jianchao Ji, Zelong Li, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Juntao Tan, and Yongfeng Zhang. 2023 · 2023
Later among the works it cites.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023 · 2023
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Multi-modality is all you need for transferable recommender systems
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AutoGSR: Neural Architecture Search for Graph-based Session Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22) . 1694–1704
Jingfan Chen, Guanghui Zhu, Haojun Hou, Chunfeng Yuan, and Yihua Huang. 2022 · 2022
Cited alongside, same era.
Recommendation as language processing (RLP): a unified pretrain, personalized prompt & predict paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys) . 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Cited alongside, same era.
Multi-Faceted Global Item Relation Learning for Session-Based Recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’22) . 1705–1715
Qilong Han, Chi Zhang, Rui Chen, Riwei Lai, Hongtao Song, and Li Li. 2022 · 2022
Cited alongside, same era.
CORE: Simple and Effective Session-Based Recommendation within Consistent Representation Space (SIGIR ’22) . 1796–1801
Yupeng Hou, Binbin Hu, Zhiqiang Zhang, and Wayne Xin Zhao. 2022 · 2022
Cited alongside, same era.
LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR)
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
An Attribute-Driven Mirror Graph Network for Session-Based Recommendation (SIGIR ’22) . 1674–1683
Siqi Lai, Erli Meng, Fan Zhang, Chenliang Li, Bin Wang, and Aixin Sun. 2022 · 2022
Cited alongside, same era.
DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation
Zhu Sun, Hui Fang, Jie Yang, Xinghua Qu, Hongyang Liu, Di Yu, Yew-Soon Ong, and Jie Zhang. 2022 · 2022
Cited alongside, same era.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems , Vol. 35. Curran Associates, Inc., 24824–24837
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Youhua Li, Hanwen Du, Yongxin Ni, Pengpeng Zhao, Qi Guo, Fajie Yuan, and Xiaofang Zhou. 2023 · 2023
Later among the works it cites.
Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark. 2023 · 2023
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Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, and William Yang Wang. 2023 · 2023
Later among the works it cites.
Automatic Prompt Optimization with “Gradient Descent” and Beam Search. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Singapore, 7957–7968
Reid Pryzant, Dan Iter, Jerry Li, Yin Lee, Chenguang Zhu, and Michael Zeng. 2023 · 2023
Later among the works it cites.
Reflexion: Language Agents with Verbal Reinforcement Learning
Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023 · 2023
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Large Language Models for Intent-Driven Session Recommendations
Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, and Yew-Soon Ong. 2023 · 2023
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LLaMA: Open and Efficient Foundation Language Models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Later among the works it cites.
Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Lei Wang and Ee-Peng Lim. 2023 · 2023
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DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
Yu Wang, Zhiwei Liu, Jianguo Zhang, Weiran Yao, Shelby Heinecke, and Philip S. Yu. 2023 · 2023
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LlamaRec: Two-Stage Recommendation using Large Language Models for Ranking
Zhenrui Yue, Sara Rabhi, Gabriel de Souza Pereira Moreira, Dong Wang, and Even Oldridge. 2023 · 2023
Later among the works it cites.
Beyond Co-occurrence: Multi-modal Session-based Recommendation
Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Liang Yang, and Hongfei Lin. 2023c · 2023
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Large language model with graph convolution for recommendation
Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai Wu, Yining Ma, Jie Zhang, and Youchen Sun. 2024 · 2024
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ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models. In Proceedings of the Seventeen ACM International Conference on Web Search and Data Mining
Qijiong Liu, Nuo Chen, Tetsuya Sakai, and Xiao-Ming Wu. 2024 · 2024
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