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CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation

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arxiv 2310.19488 v3 pith:SVSUKEZO submitted 2023-10-30 cs.IR

Yang Zhang , Fuli Feng , Jizhi Zhang , Keqin Bao , Qifan Wang , Xiangnan He This is my paper
classification cs.IR
keywords collaborativeinformationcollmllmrecmodelingrecommendationapproachesembeddings
verification ladder T0 review T1 audit T2 compute T3 formal
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Leveraging Large Language Models as Recommenders (LLMRec) has gained significant attention and introduced fresh perspectives in user preference modeling. Existing LLMRec approaches prioritize text semantics, usually neglecting the valuable collaborative information from user-item interactions in recommendations. While these text-emphasizing approaches excel in cold-start scenarios, they may yield sub-optimal performance in warm-start situations. In pursuit of superior recommendations for both cold and warm start scenarios, we introduce CoLLM, an innovative LLMRec methodology that seamlessly incorporates collaborative information into LLMs for recommendation. CoLLM captures collaborative information through an external traditional model and maps it to the input token embedding space of LLM, forming collaborative embeddings for LLM usage. Through this external integration of collaborative information, CoLLM ensures effective modeling of collaborative information without modifying the LLM itself, providing the flexibility to employ various collaborative information modeling techniques. Extensive experiments validate that CoLLM adeptly integrates collaborative information into LLMs, resulting in enhanced recommendation performance. We release the code and data at https://github.com/zyang1580/CoLLM.

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Cited by 10 Pith papers

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

  1. GrocLM: Grocery Category Recommendation in E-Commerce with Large Language Models

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    A LoRA-tuned LLM with trie-constrained decoding improves grocery category recommendation and yields a 7.5% cart-add lift in production.

  2. LLM4MEA: Data-free Model Extraction Attacks on Sequential Recommenders via Large Language Models

    cs.IR 2025-07 conditional novelty 6.0 of 10

    An LLM-driven agent generates synthetic interaction sequences that, when queried against a target sequential recommender, produce surrogate models with higher agreement to the target than random or autoregressive data...

  3. Heterogeneous User Modeling for LLM-based Recommendation

    cs.IR 2025-07 conditional novelty 6.0 of 10

    HUM uses a compression prompt, a dedicated user token, input masking, and domain-reweighted training to improve LLM-based multi-domain recommendation.

  4. GORACS: Group-level Optimal Transport-guided Coreset Selection for LLM-based Recommender Systems

    cs.IR 2025-06 conditional novelty 6.0 of 10

    GORACS selects small groups of fine-tuning examples via an optimal-transport and gradient-norm proxy objective, outperforming prior coreset methods for LLM-based recommendation.

  5. GRAM: Generative Recommendation via Semantic-aware Multi-granular Late Fusion

    cs.IR 2025-06 conditional novelty 6.0 of 10

    GRAM improves LLM-based generative recommendation by translating item relationships into hierarchical textual IDs and fusing multi-granular prompts at the decoder, outperforming prior generative recommenders on four b...

  6. Hierarchical Tree Search-based User Lifelong Behavior Modeling on Large Language Model

    cs.IR 2025-05 conditional novelty 6.0 of 10

    HiT-LBM chunks lifelong user behavior, uses process-rating-guided tree search to select interest summaries, and fuses them temporally to improve recommendation performance.

  7. LLMs as Better Recommenders with Natural Language Collaborative Signals: A Self-Assessing Retrieval Approach

    cs.IR 2025-05 conditional novelty 6.0 of 10

    SCORE retrieves similar users' behaviors, uses an LLM self-assessment to rerank them, and injects the top ones in natural language into an LLM prompt, improving recommendation accuracy on two datasets.

  8. TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation

    cs.IR 2025-08 conditional novelty 5.0 of 10

    TrackRec trains a small LLM to generate user-preference summaries and a validator to score them, alternating the training so each improves the other, and reports gains on public and industrial recommendation benchmarks.

  9. When Transformers Meet Recommenders: Integrating Self-Attentive Sequential Recommendation with Fine-Tuned LLMs

    cs.IR 2025-07 reject novelty 3.0 of 10

    SASRecLLM, a hybrid of SASRec and a LoRA-fine-tuned LLM, reports modest gains over TALLRec on MovieLens but shows inconsistent and contradictory results on Amazon and in warm-start settings.

  10. A Survey on LLM-powered Agents for Recommender Systems

    cs.IR 2025-02 conditional novelty 3.0 of 10

    The paper categorizes LLM-powered agents for recommender systems into recommender-oriented, interaction-oriented, and simulation-oriented paradigms and describes a common four-module agent architecture.

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