Fetching the paper…
Reading the bibliography…
Designing effective prompts can empower LLMs to understand user preferences and provide recommendations with intent comprehension and knowledge utilization capabilities.
BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI . AUAI Press, 452–461
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009 · 2009
Earlier work this paper cites.
The Probabilistic Relevance Framework: BM25 and Beyond
Stephen E. Robertson and Hugo Zaragoza. 2009 · 2009
Earlier work this paper cites.
Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011). In RecSys . ACM, 387–388
Iván Cantador, Peter Brusilovsky, and Tsvi Kuflik. 2011 · 2011
Earlier work this paper cites.
The MovieLens Datasets: History and Context
F. Maxwell Harper and Joseph A. Konstan. 2016 · 2016
Earlier work this paper cites.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. In NIPS . 6379–6390
Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, and Igor Mordatch. 2017 · 2017
Earlier work this paper cites.
Self-Attentive Sequential Recommendation. In ICDM . IEEE Computer Society, 197–206
Wang-Cheng Kang and Julian J. McAuley. 2018 · 2018
Earlier work this paper cites.
Adversarial advantage actor-critic model for task-completion dialogue policy learning. In ICASSP . IEEE, 6149–6153
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, and Kam-Fai Wong. 2018 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT (1) . Association for Computational Linguistics, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. In EMNLP/IJCNLP (1) . Association for Computational Linguistics, 188–197
Jianmo Ni, Jiacheng Li, and Julian J. McAuley. 2019 · 2019
Earlier work this paper cites.
Language Models as Knowledge Bases?. In EMNLP/IJCNLP (1) . Association for Computational Linguistics, 2463–2473
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019 · 2019
Earlier work this paper cites.
Language Models are Few-Shot Learners. In NeurIPS
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR . ACM, 639–648
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020 · 2020
Earlier work this paper cites.
How Can We Know What Language Models Know
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Earlier work this paper cites.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In EMNLP (1) . Association for Computational Linguistics, 4222–4235
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Earlier work this paper cites.
Making Pre-trained Language Models Better Few-shot Learners. In ACL/IJCNLP (1) . Association for Computational Linguistics, 3816–3830
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Earlier work this paper cites.
Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference. In EACL . Association for Computational Linguistics, 255–269
Timo Schick and Hinrich Schütze. 2021 · 2021
Earlier work this paper cites.
Calibrate Before Use: Improving Few-shot Performance of Language Models. In ICML (Proceedings of Machine Learning Research, Vol. 139) . PMLR, 12697–12706
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Earlier work this paper cites.
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning. In EMNLP . Association for Computational Linguistics, 3369–3391
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P. Xing, and Zhiting Hu. 2022 · 2022
Earlier work this paper cites.
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In RecSys . ACM, 299–315
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022 · 2022
Earlier work this paper cites.
Towards Universal Sequence Representation Learning for Recommender Systems. In KDD . ACM, 585–593
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen. 2022 · 2022
Earlier work this paper cites.
LoRA: Low-Rank Adaptation of Large Language Models. In ICLR . OpenReview.net
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.
Instance-wise Prompt Tuning for Pretrained Language Models
Yuezihan Jiang, Hao Yang, Junyang Lin, Hanyu Zhao, An Yang, Chang Zhou, Hongxia Yang, Zhi Yang, and Bin Cui. 2022 · 2022
Cited alongside, same era.
Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity. In ACL (1) . Association for Computational Linguistics, 8086–8098
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Cited alongside, same era.
Learning To Retrieve Prompts for In-Context Learning. In NAACL-HLT . Association for Computational Linguistics, 2655–2671
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
Cited alongside, same era.
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. In NeurIPS
A Survey on Large Language Models for Recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, and Enhong Chen. 2023b · 2023
Later among the works it cites.
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach
Junjie Zhang, Ruobing Xie, Yupeng Hou, Wayne Xin Zhao, Leyu Lin, and Ji-Rong Wen. 2023b · 2023
Later among the works it cites.
A Survey of Large Language Models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2023
Later among the works it cites.
Unified Parameter-Efficient Unlearning for LLMs
Chenlu Ding, Jiancan Wu, Yancheng Yuan, Jinda Lu, Kai Zhang, Alex Su, Xiang Wang, and Xiangnan He. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Graph convolution machine for context-aware recommender system
Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, and Xing Xie. 2022 · 2022
Cited alongside, same era.
TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation. In RecSys . ACM, 1007–1014
Keqin Bao, Jizhi Zhang, Yang Zhang, Wenjie Wang, Fuli Feng, and Xiangnan He. 2023 · 2023
Cited alongside, same era.
Uncovering ChatGPT’s Capabilities in Recommender Systems. In RecSys . ACM, 1126–1132
Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu, Zihua Si, Chen Xu, Zhongxiang Sun, Xiao Zhang, and Jun Xu. 2023 · 2023
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
Cited alongside, same era.
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders. In WWW . ACM, 1162–1171
Yupeng Hou, Zhankui He, Julian J. McAuley, and Wayne Xin Zhao. 2023 · 2023
Cited alongside, same era.
Instance-Aware Prompt Learning for Language Understanding and Generation
Feihu Jin, Jinliang Lu, Jiajun Zhang, and Chengqing Zong. 2023 · 2023
Cited alongside, same era.
A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, and Fake News. In INRA@RecSys (CEUR Workshop Proceedings, Vol. 3561) . CEUR-WS.org
Xinyi Li, Yongfeng Zhang, and Edward C. Malthouse. 2023 · 2023
Cited alongside, same era.
CIRS: Bursting Filter Bubbles by Counterfactual Interactive Recommender System
Chongming Gao, Shiqi Wang, Shijun Li, Jiawei Chen, Xiangnan He, Wenqiang Lei, Biao Li, Yuan Zhang, and Peng Jiang. 2024 · 2024
Closest in time.
Large Language Models are Zero-Shot Rankers for Recommender Systems. In ECIR (2) (Lecture Notes in Computer Science, Vol. 14609) . Springer, 364–381
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian J. McAuley, and Wayne Xin Zhao. 2024 · 2024
Closest in time.
Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search. In SIGIR . ACM, 796–806
Hideaki Joko, Shubham Chatterjee, Andrew Ramsay, Arjen P. de Vries, Jeff Dalton, and Faegheh Hasibi. 2024 · 2024
Closest in time.
Rethinking Branching on Exact Combinatorial Optimization Solver: The First Deep Symbolic Discovery Framework. In The Twelfth International Conference on Learning Representations
Yufei Kuang, Jie Wang, Haoyang Liu, Fangzhou Zhu, Xijun Li, Jia Zeng, HAO Jianye, Bin Li, and Feng Wu. 2024 · 2024
Closest in time.
LLaRA: Large Language-Recommendation Assistant. In SIGIR . ACM, 1785–1795
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, Xiang Wang, and Xiangnan He. 2024 · 2024
Closest in time.
Invariant Graph Learning Meets Information Bottleneck for Out-of-Distribution Generalization
Wenyu Mao, Jiancan Wu, Haoyang Liu, Yongduo Sui, and Xiang Wang. 2024 · 2024
Closest in time.
Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning
Yongduo Sui, Wenyu Mao, Shuyao Wang, Xiang Wang, Jiancan Wu, Xiangnan He, and Tat-Seng Chua. 2024 · 2024
Closest in time.
Large Language Models for Intent-Driven Session Recommendations. In SIGIR . ACM, 324–334
Zhu Sun, Hongyang Liu, Xinghua Qu, Kaidong Feng, Yan Wang, and Yew Soon Ong. 2024 · 2024
Closest in time.
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
Ziyan Wang, Yingpeng Du, Zhu Sun, Haoyan Chua, Kaidong Feng, Wenya Wang, and Jie Zhang. 2024a · 2024
Closest in time.
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
Jiancan Wu, Xiang Wang, Xingyu Gao, Jiawei Chen, Hongcheng Fu, and Tianyu Qiu. 2024 · 2024
Closest in time.
PepRec: Progressive Enhancement of Prompting for Recommendation. In EMNLP . Association for Computational Linguistics, 17941–17953
Yakun Yu, Shiang Qi, Baochun Li, and Di Niu. 2024 · 2024
Closest in time.
Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning. In SIGIR . ACM, 1796–1806
Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, and Maarten de Rijke. 2024 · 2024
Closest in time.
Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming. In The Thirteenth International Conference on Learning Representations
Haoyang Liu, Jie Wang, Zijie Geng, Xijun Li, Yuxuan Zong, Fangzhou Zhu, Jianye HAO, and Feng Wu. 2025 · 2025
Closest in time.
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation
Wenyu Mao, Shuchang Liu, Haoyang Liu, Haozhe Liu, Xiang Li, and Lanatao Hu. 2025 · 2025
Closest in time.
Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics. In Proceedings of the Genetic and Evolutionary Computation Conference Companion . 2067–2075
Hongyu Liu, Haoyang Liu, Yufei Kuang, Jie Wang, and Bin Li. 2024a · 2075
Closest in time.
Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. In AAMAS . International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, 2085–2087
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinícius Flores Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, and Thore Graepel. 2018 · 2087
Closest in time.