Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

Speech Understanding Systems. Summary of Results of the Five-Year Research Effort at Carnegie-Mellon University

CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE. 1977 · 1977

Earlier work this paper cites.

Bleu: a method for automatic evaluation of machine translation

Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002

Earlier work this paper cites.

Meta-recommendation systems: user-controlled integration of diverse recommendations

J Ben Schafer, Joseph A Konstan, and John Riedl. 2002 · 2002

Earlier work this paper cites.

Rouge: A package for automatic evaluation of summaries

Chin-Yew Lin. 2004 · 2004

Earlier work this paper cites.

A tutorial on spectral clustering

Ulrike Von Luxburg. 2007 · 2007

Earlier work this paper cites.

Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014 · 2014

Earlier work this paper cites.

User-controllable personalization: A case study with setfusion

Denis Parra and Peter Brusilovsky. 2015 · 2015

Earlier work this paper cites.

Deep neural networks for youtube recommendations

Paul Covington, Jay Adams, and Emre Sargin. 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.

Conversational recommender system

Yueming Sun and Yi Zhang. 2018 · 2018

Earlier work this paper cites.

Towards conversational search and recommendation: System ask, user respond

Yongfeng Zhang, Xu Chen, Qingyao Ai, Liu Yang, and W Bruce Croft. 2018 · 2018

Earlier work this paper cites.

Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable recommendation

Xu Chen, Hanxiong Chen, Hongteng Xu, Yongfeng Zhang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2019 · 2019

Earlier work this paper cites.

Towards controllable and personalized review generation

Pan Li and Alexander Tuzhilin. 2019 · 2019

Earlier work this paper cites.

Future of in-vehicle recommendation systems @ bosch

Juergen Luettin, Susanne Rothermel, and Mark Andrew. 2019 · 2019

Earlier work this paper cites.

Generate neural template explanations for recommendation

Lei Li, Yongfeng Zhang, and Li Chen. 2020 · 2020

Earlier work this paper cites.

A survey on conversational recommender systems

Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, and Li Chen. 2021 · 2021

Earlier work this paper cites.

Personalized transformer for explainable recommendation

Lei Li, Yongfeng Zhang, and Li Chen. 2021 · 2021

Earlier work this paper cites.

Zero-shot text-to-image generation

Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021

Earlier work this paper cites.

Videogpt: Video generation using vq-vae and transformers

Original

Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas. 2021 · 2021

Earlier work this paper cites.

Soundstream: An end-to-end neural audio codec

Neil Zeghidour, Alejandro Luebs, Ahmed Omran, Jan Skoglund, and Marco Tagliasacchi. 2021 · 2021

Earlier work this paper cites.

Language models as recommender systems: Evaluations and limitations

Yuhui Zhang, Hao Ding, Zeren Shui, Yifei Ma, James Zou, Anoop Deoras, and Hao Wang. 2021 · 2021

Earlier work this paper cites.

M6-rec: Generative pretrained language models are open-ended recommender systems

Original

Zeyu Cui, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. 2022 · 2022

Cited alongside, same era.

Path language modeling over knowledge graphsfor explainable recommendation

Shijie Geng, Zuohui Fu, Juntao Tan, Yingqiang Ge, Gerard De Melo, and Yongfeng Zhang. 2022b · 2022

Cited alongside, same era.

User-controllable recommendation against filter bubbles

Wenjie Wang, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. 2022 · 2022

Cited alongside, same era.

Large language models and the perils of their hallucinations

Razvan Azamfirei, Sapna R Kudchadkar, and James Fackler. 2023 · 2023

Cited alongside, same era.

When large language models meet personalization: Perspectives of challenges and opportunities

Original

Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, et al. 2023 · 2023

User-controllable recommendation via counterfactual retrospective and prospective explanations

Juntao Tan, Yingqiang Ge, Yan Zhu, Yinglong Xia, Jiebo Luo, Jianchao Ji, and Yongfeng Zhang. 2023 · 2023

Closest in time.

Zero-shot next-item recommendation using large pretrained language models

Original

Lei Wang and Ee-Peng Lim. 2023 · 2023

Closest in time.

COFFEE: Counterfactual fairness for personalized text generation in explainable recommendation

Nan Wang, Qifan Wang, Yi-Chia Wang, Maziar Sanjabi, Jingzhou Liu, Hamed Firooz, Hongning Wang, and Shaoliang Nie. 2023b · 2023

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Rethinking the evaluation for conversational recommendation in the era of large language models

Xiaolei Wang, Xinyu Tang, Wayne Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. 2023c · 2023

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A survey on large language models for recommendation

Original

Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al. 2023 · 2023