mlsanityData to 14 Oct 2026

BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model

arXiv 2305.15673 ↗First posted 25 May 2023cs.IR cs.CLcited by 22

Not on the map. Why?

Abstract (arXiv)

With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks, book rating recommendation, user rating recommendation, and book summary recommendation, and explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 19 of its 48 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

19 of 48 references.

References · 19

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  5. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  6. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  7. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,375
  8. 2023A Survey of Large Language Modelscited by 2,576
  9. 2022Emergent Abilities of Large Language Modelscited by 2,512
  10. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  11. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  12. 2022Can language models learn from explanations in context?cited by 249
7 more
  1. 2015A Critical Review of Recurrent Neural Networks for Sequence Learningcited by 224
  2. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 218
  3. 2013A Theoretical Analysis of NDCG Type Ranking Measurescited by 119
  4. 2021Learning Intents behind Interactions with Knowledge Graph for Recommendationcited by 94
  5. 2023Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Modelscited by 87
  6. 2019Memory Augmented Graph Neural Networks for Sequential Recommendationcited by 46
  7. 2023A Latent Space Theory for Emergent Abilities in Large Language Modelscited by 43

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 11; 2025: 3; 2026: 3.

22 papers.

Sort:

By area:

  1. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  2. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  3. 2024From Matching to Generation: A Survey on Generative Information Retrievalcited by 89
  4. 2023Large Language Models for Generative Recommendation: A Survey and Visionary Discussionscited by 73
  5. 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalizationcited by 60
  6. 2024AI PERSONA: Towards Life-long Personalization of LLMscited by 32
  7. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyondcited by 26
  8. 2023Evaluating ChatGPT as a Recommender System: A Rigorous Approachcited by 20
  9. 2023User Modeling in the Era of Large Language Models: Current Research and Future Directionscited by 18
  10. 2024Federated Recommendation via Hybrid Retrieval Augmented Generationcited by 16
  11. 2025Personalize Your LLM: Fake it then Align itcited by 15
  12. 2025PersonaFeedback: A Large-scale Human-annotated Benchmark For Personalizationcited by 15
10 more
  1. 2024Step-Back Profiling: Distilling User History for Personalized Scientific Writingcited by 10
  2. 2024Text Understanding and Generation Using Transformer Models for Intelligent E-commerce Recommendationscited by 3
  3. 2024Unveiling Large Language Models Generated Texts: A Multi-Level Fine-Grained Detection Frameworkcited by 3
  4. 2024Leveraging Large Language Models for Hybrid Workplace Decision Supportcited by 2
  5. 2026Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalizationcited by 1
  6. 2024Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendationscited by 0
  7. 2024Recommender Algorithm for Supporting Self-Management of CVD Risk Factors in an Adult Population at Homecited by 0
  8. 2025Membership Inference Attacks on In-Context Examples in LLM-based Recommender Systemscited by 0
  9. 2026Exploring Diversity, Novelty, and Popularity Bias in ChatGPT's Recommendationscited by 0
  10. 2026Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinementcited by 0