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Product review generation is an important task in recommender systems, which could provide explanation and persuasiveness for the recommendation.
Generating long and informative reviews with aspect-aware coarse-to-fine decoding
Junyi Li, Wayne Xin Zhao, Ji-Rong Wen, and Yang Song. 2019 · 1979
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and Jure Leskovec. 2013 · 2013
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Context-aware natural language generation with recurrent neural networks
Jian Tang, Yifan Yang, Sam Carton, Ming Zhang, and Qiaozhu Mei. 2016 · 2016
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Learning to generate product reviews from attributes
Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017 · 2017
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Estimating reactions and recommending products with generative models of reviews
Jianmo Ni, Zachary C Lipton, Sharad Vikram, and Julian McAuley. 2017 · 2017
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Towards automatic generation of product reviews from aspect-sentiment scores
Hongyu Zang and Xiaojun Wan. 2017 · 2017
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Why i like it: multi-task learning for recommendation and explanation
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018 · 2018
Cited alongside, same era.
Personalized review generation by expanding phrases and attending on aspect-aware representations
Jianmo Ni and Julian McAuley. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Towards controllable and personalized review generation
Pan Li and Alexander Tuzhilin. 2019 · 2019
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Retrieval-augmented controllable review generation
Dual learning for explainable recommendation: Towards unifying user preference prediction and review generation
Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, and Meng Wang. 2020 · 2020
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Lora: Low-rank adaptation of large language models
Edward J Hu, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. 2021 · 2021
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Peft: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul, and Benjamin Bossan. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Later among the works it cites.
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, et al. 2023 · 2023
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Jihyeok Kim, Seungtaek Choi, Reinald Kim Amplayo, and Seung-won Hwang. 2020 · 2020
Cited alongside, same era.
Knowledge-enhanced personalized review generation with capsule graph neural network
Junyi Li, Siqing Li, Wayne Xin Zhao, Gaole He, Zhicheng Wei, Nicholas Jing Yuan, and Ji-Rong Wen. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Lanling Xu, Junjie Zhang, Bingqian Li, Jinpeng Wang, Mingchen Cai, Wayne Xin Zhao, and Ji-Rong Wen. 2024 · 2024
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