# NoteLLM: A Retrievable Large Language Model for Note Recommendation

arXiv: https://arxiv.org/abs/2403.01744 · HTML: https://mlsanity.com/paper/2403.01744/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 30 of 33)

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- [Language Representations Can be What Recommenders Need: Findings and Potentials](https://mlsanity.com/paper/2407.05441/) (2024)
- [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)
- [Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization](https://mlsanity.com/paper/2412.13771/) (2024)
- [Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation](https://mlsanity.com/paper/2412.18176/) (2024)
- [AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization](https://mlsanity.com/paper/2405.19668/) (2024)
- [DLRREC: Denoising Latent Representations via Multi-Modal Knowledge Fusion in Deep Recommender Systems](https://mlsanity.com/paper/2512.00596/) (2025)
- [Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions](https://mlsanity.com/paper/2503.00501/) (2025)
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- [KARMA: Knowledge-Action Regularized Multimodal Alignment for Personalized Search at Taobao](https://mlsanity.com/paper/2603.22779/) (2026)
- [Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation](https://mlsanity.com/paper/2605.28175/) (2026)
- [Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation](https://mlsanity.com/paper/2504.10538/) (2025)
- [FOUNDv2: Learning Unified User Quantized Tokenizers for User Representation](https://mlsanity.com/paper/2508.00956/) (2025)
- [Efficient Sequential Recommendation for Long Term User Interest Via Personalization](https://mlsanity.com/paper/2601.03479/) (2026)
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- [Fine-Tuned LLM as a Complementary Predictor Improving Ads System](https://mlsanity.com/paper/2605.27856/) (2026)
- [AuditNet: A Conversational AI-based Security Assistant [DEMO]](https://mlsanity.com/paper/2407.14116/) (2024)
- [GTS-LUM: Reshaping User Behavior Modeling with LLMs in Telecommunications Industry](https://mlsanity.com/paper/2504.06511/) (2025)
- [HSTU-BLaIR: Lightweight Contrastive Text Embedding for Generative Recommender](https://mlsanity.com/paper/2504.10545/) (2025)
- [An item is worth one token in Multimodal Large Language Models-based Sequential Recommendation](https://mlsanity.com/paper/2511.05885/) (2025)
- [AdaRec: Adaptive Recommendation with LLMs via Narrative Profiling and Dual-Channel Reasoning](https://mlsanity.com/paper/2511.07166/) (2025)
- [CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillation](https://mlsanity.com/paper/2511.17041/) (2025)
- [Modular Representation Compression: Adapting LLMs for Efficient and Effective Recommendations](https://mlsanity.com/paper/2604.18146/) (2026)
- [AFMRL: Attribute-Enhanced Fine-Grained Multi-Modal Representation Learning in E-commerce](https://mlsanity.com/paper/2604.20135/) (2026)
- [Towards Sustainable Growth: A Multi-Value-Aware Retrieval Framework for E-Commerce Search](https://mlsanity.com/paper/2605.17994/) (2026)

## Abstract (arXiv)

People enjoy sharing "notes" including their experiences within online communities. Therefore, recommending notes aligned with user interests has become a crucial task. Existing online methods only input notes into BERT-based models to generate note embeddings for assessing similarity. However, they may underutilize some important cues, e.g., hashtags or categories, which represent the key concepts of notes. Indeed, learning to generate hashtags/categories can potentially enhance note embeddings, both of which compress key note information into limited content. Besides, Large Language Models (LLMs) have significantly outperformed BERT in understanding natural languages. It is promising to introduce LLMs into note recommendation. In this paper, we propose a novel unified framework called NoteLLM, which leverages LLMs to address the item-to-item (I2I) note recommendation. Specifically, we utilize Note Compression Prompt to compress a note into a single special token, and further learn the potentially related notes' embeddings via a contrastive learning approach. Moreover, we use NoteLLM to summarize the note and generate the hashtag/category automatically through instruction tuning. Extensive validations on real scenarios demonstrate the effectiveness of our proposed method compared with the online baseline and show major improvements in the recommendation system of Xiaohongshu.