# RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm

arXiv: https://arxiv.org/abs/2404.08675 · HTML: https://mlsanity.com/paper/2404.08675/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 33 references that are in the map.
- First posted on arXiv 6 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Session-based Recommendations with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06939/) (2015)
- [S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization](https://mlsanity.com/paper/2008.07873/) (2020)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Neural Attentive Session-based Recommendation](https://mlsanity.com/paper/1711.04725/) (2017)
- [Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation](https://mlsanity.com/paper/1609.09152/) (2016)
- [Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation](https://mlsanity.com/paper/2110.05730/) (2021)
- [GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation](https://mlsanity.com/paper/2304.03879/) (2023)
- [Filter-enhanced MLP is All You Need for Sequential Recommendation](https://mlsanity.com/paper/2202.13556/) (2022)
- [Controllable Multi-Interest Framework for Recommendation](https://mlsanity.com/paper/2005.09347/) (2020)
- [Zero-Shot Next-Item Recommendation using Large Pretrained Language Models](https://mlsanity.com/paper/2304.03153/) (2023)
- [Will ChatGPT get you caught? Rethinking of Plagiarism Detection](https://mlsanity.com/paper/2302.04335/) (2023)
- [RecBole 2.0: Towards a More Up-to-Date Recommendation Library](https://mlsanity.com/paper/2206.07351/) (2022)
- [Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2010.14395/) (2020)
- [Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability](https://mlsanity.com/paper/2209.06644/) (2022)

## Built on by (most cited first, 8 of 8)

- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://mlsanity.com/paper/2405.03988/) (2024)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [Improving LLM-powered Recommendations with Personalized Information](https://mlsanity.com/paper/2502.13845/) (2025)
- [RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation](https://mlsanity.com/paper/2509.03131/) (2025)
- [AgenticTagger: Structured Item Representation for Recommendation with LLM Agents](https://mlsanity.com/paper/2602.05945/) (2026)
- [PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information](https://mlsanity.com/paper/2412.06308/) (2024)

## Abstract (arXiv)

ChatGPT has achieved remarkable success in natural language understanding. Considering that recommendation is indeed a conversation between users and the system with items as words, which has similar underlying pattern with ChatGPT, we design a new chat framework in item index level for the recommendation task. Our novelty mainly contains three parts: model, training and inference. For the model part, we adopt Generative Pre-training Transformer (GPT) as the sequential recommendation model and design a user modular to capture personalized information. For the training part, we adopt the two-stage paradigm of ChatGPT, including pre-training and fine-tuning. In the pre-training stage, we train GPT model by auto-regression. In the fine-tuning stage, we train the model with prompts, which include both the newly-generated results from the model and the user's feedback. For the inference part, we predict several user interests as user representations in an autoregressive manner. For each interest vector, we recall several items with the highest similarity and merge the items recalled by all interest vectors into the final result. We conduct experiments with both offline public datasets and online A/B test to demonstrate the effectiveness of our proposed method.