Fetching the paper…
Reading the bibliography…
Adapting Large Language Models (LLMs) for recommendation requires careful consideration of the decoding process, given the inherent differences between generating items and natural language.
Session-based recommendations with recurrent neural networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016 · 2016
Earlier work this paper cites.
Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016 · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Adapting large language models by integrating collaborative semantics for recommendation
Bowen Zheng et al. 2024 · 2017
Earlier work this paper cites.
Self-attentive sequential recommendation
Wang-Cheng Kang and Julian McAuley. 2018 · 2018
Earlier work this paper cites.
Personalized top-n sequential recommendation via convolutional sequence embedding
Jiaxi Tang and Ke Wang. 2018 · 2018
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Earlier work this paper cites.
On sampled metrics for item recommendation
Walid Krichene and Steffen Rendle. 2020 · 2020
Earlier work this paper cites.
Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2020 · 2020
Cited alongside, same era.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2022 · 2022
Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
Cited alongside, same era.
Leveraging large language models for sequential recommendation
Jesse Harte, Wouter Zorgdrager, Panos Louridas, Asterios Katsifodimos, Dietmar Jannach, and Marios Fragkoulis. 2023 · 2023
Cited alongside, same era.
How to index item ids for recommendation foundation models
Wenyue Hua, Shuyuan Xu, Yingqiang Ge, and Yongfeng Zhang. 2023 · 2023
Towards open-world recommendation with knowledge augmentation from large language models
Yunjia Xi et al. 2023 · 2023
Later among the works it cites.
Where to go next for recommender systems? ID- vs. modality-based recommender models revisited
Zheng Yuan et al. 2023 · 2023
Later among the works it cites.
Large language models for recommendation: Past, present, and future
Keqin Bao, Jizhi Zhang, Xinyu Lin, Yang Zhang, Wenjie Wang, and Fuli Feng. 2024 · 2024
Closest in time.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
Closest in time.
Llara: Large language-recommendation assistant
Jiayi Liao, Sihang Li, Zhengyi Yang, Jiancan Wu, Yancheng Yuan, 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…
Cited alongside, same era.
A critical study on data leakage in recommender system offline evaluation
Yitong Ji, Aixin Sun, Jie Zhang, and Chenliang Li. 2023 · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Interpretability in the wild: a circuit for indirect object identification in gpt-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. 2022 · 2023
Cited alongside, same era.
Recmind: Large language model powered agent for recommendation
Yancheng Wang et al. 2023 · 2023
Cited alongside, same era.
A survey on large language models for recommendation
Likang Wu et al. 2023 · 2023
Cited alongside, same era.
A bi-step grounding paradigm for large language models in recommendation systems
Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Chong Chen, Fuli Feng, and Qi Tian. 2023a
Cited in the paper.
Tallrec: An effective and efficient tuning framework to align large language model with recommendation
Keqin Bao et al. 2023b
Cited in the paper.
Recommender systems with generative retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Tran, Jonah Samost, et al. 2024 · 2024
Closest in time.
Towards llm-recsys alignment with textual id learning
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang. 2024 · 2024
Closest in time.
Llmrec: Large language models with graph augmentation for recommendation
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024 · 2024
Closest in time.
Text-like encoding of collaborative information in large language models for recommendation
Yang Zhang, Keqin Bao, Ming Yan, Wenjie Wang, Fuli Feng, and Xiangnan He. 2024b · 2024
Closest in time.