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

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arxiv 2406.03210 v1 pith:LZRFQLZV submitted 2024-06-05 cs.IR

Yang Zhang , Keqin Bao , Ming Yan , Wenjie Wang , Fuli Feng , Xiangnan He This is my paper
classification cs.IR
keywords collaborativeinformationbinllmllmsmodelstext-likeformatbinary
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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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. CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models

    cs.IR 2025-06 conditional novelty 6.0 of 10

    CORONA uses LLM-generated preference and intent queries to prune the interaction graph in two stages, then applies a GNN to the remaining subgraph, achieving state-of-the-art recommendation accuracy.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

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