# Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation

arXiv: https://arxiv.org/abs/2310.06491 · HTML: https://mlsanity.com/paper/2310.06491/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2023.

## Built on by (most cited first, 29 of 29)

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- [Agentic Feedback Loop Modeling Improves Recommendation and User Simulation](https://mlsanity.com/paper/2410.20027/) (2024)
- [A Survey of Generative Search and Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2404.16924/) (2024)
- [Collaboration of Large Language Models and Small Recommendation Models for Device-Cloud Recommendation](https://mlsanity.com/paper/2501.05647/) (2025)
- [Efficient Inference for Large Language Model-based Generative Recommendation](https://mlsanity.com/paper/2410.05165/) (2024)
- [ThinkRec: Thinking-based recommendation via LLM](https://mlsanity.com/paper/2505.15091/) (2025)
- [CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation](https://mlsanity.com/paper/2511.22707/) (2025)
- [Beyond Retrieval: Generating Narratives in Conversational Recommender Systems](https://mlsanity.com/paper/2410.16780/) (2024)
- [Causality-Enhanced Behavior Sequence Modeling in LLMs for Personalized Recommendation](https://mlsanity.com/paper/2410.22809/) (2024)
- [GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion](https://mlsanity.com/paper/2506.01673/) (2025)
- [Integrating Large Language Models with Graphical Session-Based Recommendation](https://mlsanity.com/paper/2402.16539/) (2024)
- [Beyond Unimodal Boundaries: Generative Recommendation with Multimodal Semantics](https://mlsanity.com/paper/2503.23333/) (2025)
- [Tuning Language Models for Robust Prediction of Diverse User Behaviors](https://mlsanity.com/paper/2505.17682/) (2025)
- [Enhancing Time Awareness in Generative Recommendation](https://mlsanity.com/paper/2509.13957/) (2025)
- [Verifiable Reasoning for LLM-based Generative Recommendation](https://mlsanity.com/paper/2603.07725/) (2026)
- [Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation](https://mlsanity.com/paper/2605.28175/) (2026)
- [Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders](https://mlsanity.com/paper/2505.23053/) (2025)
- [Cloud-Device Collaborative Agents for Sequential Recommendation](https://mlsanity.com/paper/2509.01551/) (2025)
- [Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation](https://mlsanity.com/paper/2511.12922/) (2025)
- [Sharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2607.25366/) (2026)
- [Token-Efficient Item Representation via Images for LLM Recommender Systems](https://mlsanity.com/paper/2503.06238/) (2025)
- [Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation](https://mlsanity.com/paper/2504.11658/) (2025)
- [Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning](https://mlsanity.com/paper/2507.03047/) (2025)
- [CLLMRec: LLM-powered Cognitive-Aware Concept Recommendation via Semantic Alignment and Prerequisite Knowledge Distillation](https://mlsanity.com/paper/2511.17041/) (2025)
- [Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders](https://mlsanity.com/paper/2604.07825/) (2026)
- [Dialogue to Discovery: Attribute-Aware Preference Elicitation for Conversational Product Search Assistants](https://mlsanity.com/paper/2606.24194/) (2026)

## Abstract (arXiv)

Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However, previous methods exhibit inherent limitations in the two steps. Existing ID-based identifiers (e.g., numeric IDs) and description-based identifiers (e.g., titles) either lose semantics or lack adequate distinctiveness. Moreover, prior generation grounding methods might generate invalid identifiers, thus misaligning with in-corpus items. To address these issues, we propose a novel Transition paradigm for LLM-based Recommender (named TransRec) to bridge items and language. Specifically, TransRec presents multi-facet identifiers, which simultaneously incorporate ID, title, and attribute for item indexing to pursue both distinctiveness and semantics. Additionally, we introduce a specialized data structure for TransRec to ensure generating valid identifiers only and utilize substring indexing to encourage LLMs to generate from any position of identifiers. Lastly, TransRec presents an aggregated grounding module to leverage generated multi-facet identifiers to rank in-corpus items efficiently. We instantiate TransRec on two backbone models, BART-large and LLaMA-7B. Extensive results on three real-world datasets under diverse settings validate the superiority of TransRec.