Fetching the paper…
Reading the bibliography…
In recent years, Recommender Systems(RS) have witnessed a transformative shift with the advent of Large Language Models(LLMs) in the field of Natural Language Processing(NLP).
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
Earlier work this paper cites.
Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton · 2011
Earlier work this paper cites.
A collaborative filtering approach to mitigate the new user cold start problem
JesúS Bobadilla, Fernando Ortega, Antonio Hernando, and Jesús Bernal · 2012
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
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer, 2019
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang · 2019
Earlier work this paper cites.
Generate neural template explanations for recommendation
Lei Li, Yongfeng Zhang, and Li Chen · 2020
Earlier work this paper cites.
Gpt-3: What’s it good for?
Robert Dale · 2021
Cited alongside, same era.
Transformers4rec: Bridging the gap between nlp and sequential/session-based recommendation
Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge · 2021
Cited alongside, same era.
LoRA: Low-Rank Adaptation of Large Language Models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang · 2022
Cited alongside, same era.
Towards universal sequence representation learning for recommender systems
Yupeng Hou, Shanlei Mu, Wayne Xin Zhao, Yaliang Li, Bolin Ding, and Ji-Rong Wen · 2022
Cited alongside, same era.
Peng Liu, Lemei Zhang, and Jon Atle Gulla · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron and Louis Martin et al · 2023
Closest in time.
Generative sequential recommendation with gptrec
Aleksandr V Petrov and Craig Macdonald · 2023
Closest in time.
Up5: Unbiased foundation model for fairness-aware recommendation
Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, and Yongfeng Zhang · 2023
Closest in time.
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, et al · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman · 2022
Cited alongside, same era.
Peng Liu, Lemei Zhang, and Jon Atle Gulla · 2023
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
Cited alongside, same era.
Closest in time.
Personalized prompt learning for explainable recommendation
Lei Li, Yongfeng Zhang, and Li Chen · 2023
Closest in time.
Towards personalized cold-start recommendation with prompts
Xuansheng Wu, Huachi Zhou, Wenlin Yao, Xiao Huang, and Ninghao Liu · 2023
Closest in time.
Bias and debias in recommender system: A survey and future directions
Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He · 2023
Closest in time.