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Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

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arxiv 2309.01157 v2 pith:WNSN6WOH submitted 2023-09-03 cs.IR cs.AIcs.CL

Lei Li , Yongfeng Zhang , Dugang Liu , Li Chen This is my paper
classification cs.IRcs.AIcs.CL
keywords recommendationgenerativelanguagesurveylargellm-basedmodelsprocess
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Large language models (LLM) not only have revolutionized the field of natural language processing (NLP) but also have the potential to reshape many other fields, e.g., recommender systems (RS). However, most of the related work treats an LLM as a component of the conventional recommendation pipeline (e.g., as a feature extractor), which may not be able to fully leverage the generative power of LLM. Instead of separating the recommendation process into multiple stages, such as score computation and re-ranking, this process can be simplified to one stage with LLM: directly generating recommendations from the complete pool of items. This survey reviews the progress, methods, and future directions of LLM-based generative recommendation by examining three questions: 1) What generative recommendation is, 2) Why RS should advance to generative recommendation, and 3) How to implement LLM-based generative recommendation for various RS tasks. We hope that this survey can provide the context and guidance needed to explore this interesting and emerging topic.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation

    cs.IR 2026-07 accept novelty 6.5 of 10

    LBR removes length bias in LLM recommenders via length-aware attention offsets and Trie-branching information-length normalization, improving accuracy and fairness with negligible cost.

  2. Beyond Fixed Depths and Widths: Optimizing Textual Decoding Tries in LLM-based Generative Recommendation

    cs.IR 2026-07 conditional novelty 6.0 of 10

    BONSAI constructs variable-depth, low-branching decoding tries for LLM-based generative recommendation and reports 16–22% relative gains over state-of-the-art baselines.

  3. KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation

    cs.IR 2025-07 conditional novelty 6.0 of 10

    KERAG_R improves LLM-based top-k recommendation by using a GAT to select relevant KG triples and incorporating them into instruction-tuned prompts, reporting gains over ten baselines on three datasets.

  4. Tokenizing Numerical and Embedding Features for LLM RecSys

    cs.IR 2026-07 conditional novelty 5.0 of 10

    Interaction-based soft-token fusion of numerical and embedding features improves LLM two-tower retrieval over text-only and direct-concatenation baselines on three Amazon datasets.

  5. Benchmark Leakage Trap: Can We Trust LLM-based Recommendation?

    cs.LG 2026-02 conditional novelty 5.0 of 10

    Fine-tuning an LLM recommender on a slice of the benchmark inflates AUC/UAUC for in-domain leakage and degrades it for out-of-domain leakage, showing benchmark contamination can distort LLM-based recommendation evaluation.

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