mlsanityData to 14 Oct 2026

Towards Controllable and Personalized Review Generation

arXiv 1910.03506 ↗First posted 30 Sep 2019cs.CL cs.LG stat.MLcited by 24

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Abstract (arXiv)

In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic information. RevGAN utilizes the combination of three novel components, including self-attentive recursive autoencoders, conditional discriminators, and personalized decoders. We test its performance on the several real-world datasets, where our model significantly outperforms state-of-the-art generation models in terms of sentence quality, coherence, personalization and human evaluations. We also empirically show that the generated reviews could not be easily distinguished from the organically produced reviews and that they follow the same statistical linguistics laws.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Sep 2019.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 6; 2025: 3; 2026: 3.

24 papers.

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  1. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  2. 2023Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Mergingcited by 280
  3. 2025Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMscited by 148
  4. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentationcited by 99
  5. 2024LongLaMP: A Benchmark for Personalized Long-form Text Generationcited by 97
  6. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  7. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  8. 2023Teach LLMs to Personalize -- An Approach inspired by Writing Educationcited by 48
  9. 2023Learning to Rewrite Prompts for Personalized Text Generationcited by 40
  10. 2024Exploring Safety-Utility Trade-Offs in Personalized Language Modelscited by 27
  11. 2021Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modelingcited by 16
  12. 2024Review-LLM: Harnessing Large Language Models for Personalized Review Generationcited by 14
12 more
  1. 2021Controllable and Diverse Text Generation in E-commercecited by 13
  2. 2023Automated Evaluation of Personalized Text Generation using Large Language Modelscited by 9
  3. 2022COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendationcited by 6
  4. 2022Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Controlcited by 6
  5. 2026Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Futurecited by 6
  6. 2024Online Personalizing White-box LLMs Generation with Neural Banditscited by 5
  7. 2022On Faithfulness and Coherence of Language Explanations for Recommendation Systemscited by 1
  8. 2023Input-length-shortening and text generation via attention valuescited by 1
  9. 2026User Review Writing via Interview with Dialogue Systemscited by 1
  10. 2025Personalized Image Generation from an Author Writing Stylecited by 0
  11. 2025Few-Shot and Training-Free Review Generation via Conversational Promptingcited by 0
  12. 2026Do Implicit Personalization and Explicit Styles Conflict? PsPLUG: A Lightweight Plug-in for Balancing Personalization and Style in Customized LLMscited by 0