# Large Language Models as Data Augmenters for Cold-Start Item Recommendation

arXiv: https://arxiv.org/abs/2402.11724 · HTML: https://mlsanity.com/paper/2402.11724/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2024.

## Built on by (most cited first, 19 of 19)

- [HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling](https://mlsanity.com/paper/2409.12740/) (2024)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [LLMs for User Interest Exploration in Large-scale Recommendation Systems](https://mlsanity.com/paper/2405.16363/) (2024)
- [Do LLMs Memorize Recommendation Datasets? A Preliminary Study on MovieLens-1M](https://mlsanity.com/paper/2505.10212/) (2025)
- [Large Language Model Enhanced Recommender Systems: A Survey](https://mlsanity.com/paper/2412.13432/) (2024)
- [Improving LLM-powered Recommendations with Personalized Information](https://mlsanity.com/paper/2502.13845/) (2025)
- [Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge](https://mlsanity.com/paper/2508.08777/) (2025)
- [Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation](https://mlsanity.com/paper/2508.08730/) (2025)
- [Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation](https://mlsanity.com/paper/2411.01457/) (2024)
- [Keyword-driven Retrieval-Augmented Large Language Models for Cold-start User Recommendations](https://mlsanity.com/paper/2405.19612/) (2024)
- [All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction](https://mlsanity.com/paper/2403.17740/) (2024)
- [Large Language Models Enhanced Hyperbolic Space Recommender Systems](https://mlsanity.com/paper/2504.05694/) (2025)
- [Language Models as Semantic Augmenters for Sequential Recommenders](https://mlsanity.com/paper/2510.18046/) (2025)
- [IDProxy: CTR Prediction with Multimodal LLMs for Cold-Start Recommendation at Xiaohongshu](https://mlsanity.com/paper/2603.01590/) (2026)
- [Serendipitous Recommendation with Multimodal LLM](https://mlsanity.com/paper/2506.08283/) (2025)
- [Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation](https://mlsanity.com/paper/2602.05544/) (2026)
- [LLM-I2I: Boost Your Small Item2Item Recommendation Model with Large Language Model](https://mlsanity.com/paper/2512.21595/) (2025)
- [Facet-Aware Multi-Head Mixture-of-Experts Model with Text-Enhanced Pre-training for Sequential Recommendation](https://mlsanity.com/paper/2601.12301/) (2026)
- [Denoising Implicit Feedback for Cold-start Recommendation](https://mlsanity.com/paper/2606.19658/) (2026)

## Abstract (arXiv)

The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant, conventional recommendation systems struggle to recommend cold-start items without historical interactions. To address this, we propose utilizing LLMs as data augmenters to bridge the knowledge gap on cold-start items during training. We employ LLMs to infer user preferences for cold-start items based on textual description of user historical behaviors and new item descriptions. The augmented training signals are then incorporated into learning the downstream recommendation models through an auxiliary pairwise loss. Through experiments on public Amazon datasets, we demonstrate that LLMs can effectively augment the training signals for cold-start items, leading to significant improvements in cold-start item recommendation for various recommendation models.