# Large Language Models Enhanced Collaborative Filtering

arXiv: https://arxiv.org/abs/2403.17688 · HTML: https://mlsanity.com/paper/2403.17688/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- First posted on arXiv 26 Mar 2024.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Built on by (most cited first, 27 of 27)

- [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)
- [Graph Retrieval-Augmented LLM for Conversational Recommendation Systems](https://mlsanity.com/paper/2503.06430/) (2025)
- [Inference Computation Scaling for Feature Augmentation in Recommendation Systems](https://mlsanity.com/paper/2502.16040/) (2025)
- [Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction](https://mlsanity.com/paper/2505.14057/) (2025)
- [Bidirectional Knowledge Distillation for Enhancing Sequential Recommendation with Large Language Models](https://mlsanity.com/paper/2505.18120/) (2025)
- [Effective In-Context Example Selection through Data Compression](https://mlsanity.com/paper/2405.11465/) (2024)
- [LLM-KT: A Versatile Framework for Knowledge Transfer from Large Language Models to Collaborative Filtering](https://mlsanity.com/paper/2411.00556/) (2024)
- [Do LLMs Understand Collaborative Signals? Diagnosis and Repair](https://mlsanity.com/paper/2505.20730/) (2025)
- [ITDR: An Instruction Tuning Dataset for Enhancing Large Language Models in Recommendations](https://mlsanity.com/paper/2508.05667/) (2025)
- [DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization](https://mlsanity.com/paper/2512.03607/) (2025)
- [BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models](https://mlsanity.com/paper/2601.22925/) (2026)
- [Reasoning-guided Collaborative Filtering with Language Models for Explainable Recommendation](https://mlsanity.com/paper/2602.05544/) (2026)
- [A Novel Mamba-based Sequential Recommendation Method](https://mlsanity.com/paper/2504.07398/) (2025)
- [Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks](https://mlsanity.com/paper/2506.03391/) (2025)
- [Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation](https://mlsanity.com/paper/2508.10312/) (2025)
- [RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations](https://mlsanity.com/paper/2509.06286/) (2025)
- [Selective LLM-Guided Regularization for Enhancing Recommendation Models](https://mlsanity.com/paper/2512.21526/) (2025)
- [LLM-I2I: Boost Your Small Item2Item Recommendation Model with Large Language Model](https://mlsanity.com/paper/2512.21595/) (2025)
- [GRAB: An LLM-Inspired Sequence-First Click-Through Rate Prediction Modeling Paradigm](https://mlsanity.com/paper/2602.01865/) (2026)
- [Give Users the Wheel: Towards Promptable Recommendation Paradigm](https://mlsanity.com/paper/2602.18929/) (2026)
- [Pre-trained LLMs Meet Sequential Recommenders: Efficient User-Centric Knowledge Distillation](https://mlsanity.com/paper/2604.21536/) (2026)
- [LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation](https://mlsanity.com/paper/2607.04270/) (2026)
- [SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation](https://mlsanity.com/paper/2607.24025/) (2026)
- [TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation](https://mlsanity.com/paper/2608.09605/) (2026)
- [Towards Efficient Reasoning in LLM-Based Recommender Systems via Model Merging](https://mlsanity.com/paper/2608.10447/) (2026)
- [CARA: Cognitive Adaptive Recommendation Agent](https://mlsanity.com/paper/2608.16919/) (2026)

## Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing work predominantly utilizes LLMs to generate knowledge-rich texts or utilizes LLM-derived embeddings as features to improve RSs. Although the extensive world knowledge embedded in LLMs generally benefits RSs, the application can only take limited number of users and items as inputs, without adequately exploiting collaborative filtering information. Considering its crucial role in RSs, one key challenge in enhancing RSs with LLMs lies in providing better collaborative filtering information through LLMs. In this paper, drawing inspiration from the in-context learning and chain of thought reasoning in LLMs, we propose the Large Language Models enhanced Collaborative Filtering (LLM-CF) framework, which distils the world knowledge and reasoning capabilities of LLMs into collaborative filtering. We also explored a concise and efficient instruction-tuning method, which improves the recommendation capabilities of LLMs while preserving their general functionalities (e.g., not decreasing on the LLM benchmark). Comprehensive experiments on three real-world datasets demonstrate that LLM-CF significantly enhances several backbone recommendation models and consistently outperforms competitive baselines, showcasing its effectiveness in distilling the world knowledge and reasoning capabilities of LLM into collaborative filtering.