# Text-like Encoding of Collaborative Information in Large Language Models for Recommendation

arXiv: https://arxiv.org/abs/2406.03210 · HTML: https://mlsanity.com/paper/2406.03210/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2024.

## Built on by (most cited first, 25 of 25)

- [A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models](https://mlsanity.com/paper/2501.13958/) (2025)
- [Reinforced Latent Reasoning for LLM-based Recommendation](https://mlsanity.com/paper/2505.19092/) (2025)
- [Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization](https://mlsanity.com/paper/2503.02450/) (2025)
- [Latent Inter-User Difference Modeling for LLM Personalization](https://mlsanity.com/paper/2507.20849/) (2025)
- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [Improving LLM-powered Recommendations with Personalized Information](https://mlsanity.com/paper/2502.13845/) (2025)
- [Mitigating Propensity Bias of Large Language Models for Recommender Systems](https://mlsanity.com/paper/2409.20052/) (2024)
- [Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation](https://mlsanity.com/paper/2410.22809/) (2024)
- [CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation](https://mlsanity.com/paper/2408.10645/) (2024)
- [GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion](https://mlsanity.com/paper/2506.01673/) (2025)
- [TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation](https://mlsanity.com/paper/2508.15388/) (2025)
- [Inference Computation Scaling for Feature Augmentation in Recommendation Systems](https://mlsanity.com/paper/2502.16040/) (2025)
- [IGD: Token Decisiveness Modeling via Information Gain in LLMs for Personalized Recommendation](https://mlsanity.com/paper/2506.13229/) (2025)
- [MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation](https://mlsanity.com/paper/2409.06377/) (2024)
- [Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts](https://mlsanity.com/paper/2509.26093/) (2025)
- [Do LLMs Benefit from User and Item Embeddings in Recommendation Tasks?](https://mlsanity.com/paper/2601.04690/) (2026)
- [Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?](https://mlsanity.com/paper/2602.13626/) (2026)
- [UniGRec: Unified Generative Recommendation with Soft Identifiers for End-to-End Optimization](https://mlsanity.com/paper/2601.17438/) (2026)
- [GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation](https://mlsanity.com/paper/2606.07526/) (2026)
- [A Novel Mamba-based Sequential Recommendation Method](https://mlsanity.com/paper/2504.07398/) (2025)
- [Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation](https://mlsanity.com/paper/2508.10312/) (2025)
- [Membership Inference Attack against Large Language Model-based Recommendation Systems: A New Distillation-based Paradigm](https://mlsanity.com/paper/2511.14763/) (2025)
- [Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders](https://mlsanity.com/paper/2604.07825/) (2026)
- [RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation](https://mlsanity.com/paper/2605.07129/) (2026)
- [Enhancing LLMs in Predictive Political QA with Semi-Structured Data](https://mlsanity.com/paper/2608.21218/) (2026)

## Abstract (arXiv)

When adapting Large Language Models for Recommendation (LLMRec), it is crucial to integrate collaborative information. Existing methods achieve this by learning collaborative embeddings in LLMs' latent space from scratch or by mapping from external models. However, they fail to represent the information in a text-like format, which may not align optimally with LLMs. To bridge this gap, we introduce BinLLM, a novel LLMRec method that seamlessly integrates collaborative information through text-like encoding. BinLLM converts collaborative embeddings from external models into binary sequences -- a specific text format that LLMs can understand and operate on directly, facilitating the direct usage of collaborative information in text-like format by LLMs. Additionally, BinLLM provides options to compress the binary sequence using dot-decimal notation to avoid excessively long lengths. Extensive experiments validate that BinLLM introduces collaborative information in a manner better aligned with LLMs, resulting in enhanced performance. We release our code at https://github.com/zyang1580/BinLLM.