# Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation

arXiv: https://arxiv.org/abs/2401.13870 · HTML: https://mlsanity.com/paper/2401.13870/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2024.

## Built on by (most cited first, 26 of 26)

- [LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation](https://mlsanity.com/paper/2405.20646/) (2024)
- [Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning](https://mlsanity.com/paper/2503.24289/) (2025)
- [Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond](https://mlsanity.com/paper/2410.19744/) (2024)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [Large Language Model Enhanced Recommender Systems: A Survey](https://mlsanity.com/paper/2412.13432/) (2024)
- [Semantic Convergence: Harmonizing Recommender Systems via Two-Stage Alignment and Behavioral Semantic Tokenization](https://mlsanity.com/paper/2412.13771/) (2024)
- [Preference Discerning with LLM-Enhanced Generative Retrieval](https://mlsanity.com/paper/2412.08604/) (2024)
- [Active Large Language Model-based Knowledge Distillation for Session-based Recommendation](https://mlsanity.com/paper/2502.15685/) (2024)
- [RALLRec+: Retrieval Augmented Large Language Model Recommendation with Reasoning](https://mlsanity.com/paper/2503.20430/) (2025)
- [RecLLM-R1: A Two-Stage Training Paradigm with Reinforcement Learning and Chain-of-Thought v1](https://mlsanity.com/paper/2506.19235/) (2025)
- [Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery](https://mlsanity.com/paper/2403.06097/) (2024)
- [TEARS: Textual Representations for Scrutable Recommendations](https://mlsanity.com/paper/2410.19302/) (2024)
- [RALLRec: Improving Retrieval Augmented Large Language Model Recommendation with Representation Learning](https://mlsanity.com/paper/2502.06101/) (2025)
- [DELRec: Distilling Sequential Pattern to Enhance LLMs-based Sequential Recommendation](https://mlsanity.com/paper/2406.11156/) (2024)
- [A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges](https://mlsanity.com/paper/2507.21117/) (2025)
- [Privacy in LLM-based Recommendation: Recent Advances and Future Directions](https://mlsanity.com/paper/2406.01363/) (2024)
- [Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders](https://mlsanity.com/paper/2505.23053/) (2025)
- [LLM4Rec: Large Language Models for Multimodal Generative Recommendation with Causal Debiasing](https://mlsanity.com/paper/2510.01622/) (2025)
- [Enhance Large Language Models as Recommendation Systems with Collaborative Filtering](https://mlsanity.com/paper/2510.15647/) (2025)
- [SAGE: Sequence-level Adaptive Gradient Evolution for Generative Recommendation](https://mlsanity.com/paper/2601.21452/) (2026)
- [Generative Archetype-Grounded Item Representations for Sequential Recommendation](https://mlsanity.com/paper/2606.11023/) (2026)
- [VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentation](https://mlsanity.com/paper/2507.21563/) (2025)
- [EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation](https://mlsanity.com/paper/2508.16170/) (2025)
- [Action-Aware Generative Sequence Modeling for Short Video Recommendation](https://mlsanity.com/paper/2604.25834/) (2026)
- [Distributionally Robust Set Representation Learning Under Inference-Time Element Corruption](https://mlsanity.com/paper/2605.30089/) (2026)

## Abstract (arXiv)

Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior. Recently, large language models (LLMs) have gained prominence for their capabilities in understanding and reasoning over textual semantics, and have found utility in various domains, including recommendation. Conventional recommendation methods and LLMs each have their strengths and weaknesses. While conventional methods excel at mining collaborative information and modeling sequential behavior, they struggle with data sparsity and the long-tail problem. LLMs, on the other hand, are proficient at utilizing rich textual contexts but face challenges in mining collaborative or sequential information. Despite their individual successes, there is a significant gap in leveraging their combined potential to enhance recommendation performance. In this paper, we introduce a general and model-agnostic framework known as Large language model with mutual augmentation and adaptive aggregation for Recommendation (Llama4Rec). Llama4Rec synergistically combines conventional and LLM-based recommendation models. Llama4Rec proposes data augmentation and prompt augmentation strategies tailored to enhance the conventional model and LLM respectively. An adaptive aggregation module is adopted to combine the predictions of both kinds of models to refine the final recommendation results. Empirical studies on three real-world datasets validate the superiority of Llama4Rec, demonstrating its consistent outperformance of baseline methods and significant improvements in recommendation performance.