# Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences

arXiv: https://arxiv.org/abs/2307.14225 · HTML: https://mlsanity.com/paper/2307.14225/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 26 of 26)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)](https://mlsanity.com/paper/2203.13366/) (2022)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Large Language Models are Zero-Shot Rankers for Recommender Systems](https://mlsanity.com/paper/2305.08845/) (2023)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches](https://mlsanity.com/paper/1907.06902/) (2019)
- [A Survey on Conversational Recommender Systems](https://mlsanity.com/paper/2004.00646/) (2020)
- [Towards Universal Sequence Representation Learning for Recommender Systems](https://mlsanity.com/paper/2206.05941/) (2022)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction](https://mlsanity.com/paper/2305.06474/) (2023)
- [Embarrassingly Shallow Autoencoders for Sparse Data](https://mlsanity.com/paper/1905.03375/) (2019)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation](https://mlsanity.com/paper/2007.15356/) (2020)
- [Large Language Model Augmented Narrative Driven Recommendations](https://mlsanity.com/paper/2306.02250/) (2023)
- [On Natural Language User Profiles for Transparent and Scrutable Recommendation](https://mlsanity.com/paper/2205.09403/) (2022)
- [Denoising Self-attentive Sequential Recommendation](https://mlsanity.com/paper/2212.04120/) (2022)
- [On Interpretation and Measurement of Soft Attributes for Recommendation](https://mlsanity.com/paper/2105.09179/) (2021)
- [Editable User Profiles for Controllable Text Recommendation](https://mlsanity.com/paper/2304.04250/) (2023)
- [DOCENT: Learning Self-Supervised Entity Representations from Large Document Collections](https://mlsanity.com/paper/2102.13247/) (2021)
- [RecoBERT: A Catalog Language Model for Text-Based Recommendations](https://mlsanity.com/paper/2009.13292/) (2020)
- [Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset](https://mlsanity.com/paper/2303.06791/) (2023)

## Built on by (most cited first, 30 of 66)

- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [Large Language Models meet Collaborative Filtering: An Efficient All-round LLM-based Recommender System](https://mlsanity.com/paper/2404.11343/) (2024)
- [CoRAL: Collaborative Retrieval-Augmented Large Language Models Improve Long-tail Recommendation](https://mlsanity.com/paper/2403.06447/) (2024)
- [RAH! RecSys-Assistant-Human: A Human-Centered Recommendation Framework with LLM Agents](https://mlsanity.com/paper/2308.09904/) (2023)
- [Large Language Models for Intent-Driven Session Recommendations](https://mlsanity.com/paper/2312.07552/) (2023)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations](https://mlsanity.com/paper/2402.09617/) (2024)
- [Interactive AI Alignment: Specification, Process, and Evaluation Alignment](https://mlsanity.com/paper/2311.00710/) (2023)
- [RDRec: Rationale Distillation for LLM-based Recommendation](https://mlsanity.com/paper/2405.10587/) (2024)
- [LEARN: Knowledge Adaptation from Large Language Model to Recommendation for Practical Industrial Application](https://mlsanity.com/paper/2405.03988/) (2024)
- [Evaluating ChatGPT as a Recommender System: A Rigorous Approach](https://mlsanity.com/paper/2309.03613/) (2023)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Language-Based User Profiles for Recommendation](https://mlsanity.com/paper/2402.15623/) (2024)
- [Unifying Generative and Dense Retrieval for Sequential Recommendation](https://mlsanity.com/paper/2411.18814/) (2024)
- [A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios](https://mlsanity.com/paper/2409.17864/) (2024)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Uncertainty Quantification and Decomposition for LLM-based Recommendation](https://mlsanity.com/paper/2501.17630/) (2025)
- [Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation](https://mlsanity.com/paper/2405.12119/) (2024)
- [End-to-end Training for Recommendation with Language-based User Profiles](https://mlsanity.com/paper/2410.18870/) (2024)
- [Adaptive In-Context Learning with Large Language Models for Bundle Generation](https://mlsanity.com/paper/2312.16262/) (2023)
- [Incorporating External Knowledge and Goal Guidance for LLM-based Conversational Recommender Systems](https://mlsanity.com/paper/2405.01868/) (2024)
- [Enhancing High-order Interaction Awareness in LLM-based Recommender Model](https://mlsanity.com/paper/2409.19979/) (2024)
- [Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?](https://mlsanity.com/paper/2502.13909/) (2025)
- [RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation](https://mlsanity.com/paper/2402.04527/) (2024)
- [Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models](https://mlsanity.com/paper/2509.03036/) (2025)
- [Leveraging Large Language Models for Collective Decision-Making](https://mlsanity.com/paper/2311.04928/) (2023)
- [ChatGPT for Conversational Recommendation: Refining Recommendations by Reprompting with Feedback](https://mlsanity.com/paper/2401.03605/) (2024)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective](https://mlsanity.com/paper/2508.03703/) (2025)
- [Revealing Potential Biases in LLM-Based Recommender Systems in the Cold Start Setting](https://mlsanity.com/paper/2508.20401/) (2025)

## Abstract (arXiv)

Traditional recommender systems leverage users' item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to express language-based preferences offer a fundamentally different modality for preference input. Inspired by recent successes of prompting paradigms for large language models (LLMs), we study their use for making recommendations from both item-based and language-based preferences in comparison to state-of-the-art item-based collaborative filtering (CF) methods. To support this investigation, we collect a new dataset consisting of both item-based and language-based preferences elicited from users along with their ratings on a variety of (biased) recommended items and (unbiased) random items. Among numerous experimental results, we find that LLMs provide competitive recommendation performance for pure language-based preferences (no item preferences) in the near cold-start case in comparison to item-based CF methods, despite having no supervised training for this specific task (zero-shot) or only a few labels (few-shot). This is particularly promising as language-based preference representations are more explainable and scrutable than item-based or vector-based representations.