Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation

• URL: http://arxiv.org/abs/2608.12184v2
• Date: Mon, 17 Aug 2026 07:05:15 GMT
• Title: Making Collaborative Signals Count: Graph-Aware Large Language Models for Sequential Recommendation
• Abstract summary: We propose GALLM, a graph-aware LLM framework for sequential recommendation.<n>GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations.<n> Experiments on four real-world benchmarks show that GALLM achieves the best performance among the compared baselines.
• Score: 14.98615627827995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have been widely adopted as backbones for recommender systems. However, their language-centric pretraining makes it difficult to capture collaborative signals implicit in user-item interactions, which are crucial for personalized recommendation. Existing methods either inject collaborative representations produced by external recommenders or model only intra-sequence dependencies, limiting their ability to exploit global collaborative patterns. To address this limitation, we propose GALLM, a graph-aware LLM framework for sequential recommendation. GALLM constructs a collaborative graph over text tokens and item tokens, and models three types of relations: Text--Text relations for preserving semantic dependencies, Item--Text relations for aligning item tokens with their textual descriptions, and Item--Item relations derived from global item co-occurrence patterns. These relations are transformed into lightweight learnable attention biases and incorporated into the LLM attention mechanism, enabling collaborative-aware token interactions without introducing an additional graph encoder. Experiments on four real-world benchmarks show that GALLM achieves the best performance among the compared baselines, improving over the strongest baseline by 9.76\% on average in HR@5.

Related papers

• A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation [13.705515718089123]
  Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models with external knowledge.<n>Existing graph-based methods operate on representations anchored to original text without true knowledge fusion.<n>We propose HyGRAG, a hierarchical graph RAG framework that transcends source documents by addressing three core challenges.
  arXiv  Detail & Related papers  (2026-06-16T15:44:10Z)
• From Token to Item: Enhancing Large Language Models for Recommendation via Item-aware Attention Mechanism [17.92505712532828]
  Large Language Models (LLMs) have recently gained increasing attention in the field of recommendation.<n>We revisit the role of tokens in recommendation and categorize their relations into two types: intra-item token relations and inter-item token relations.<n>We propose a novel framework with an item-aware attention mechanism (IAM) to enhance LLMs for recommendation.
  arXiv  Detail & Related papers  (2026-03-20T06:56:59Z)
• Large Language Models are Good Relational Learners [55.40941576497973]
  We introduce Rel-LLM, a novel architecture that utilizes a graph neural network (GNN)- based encoder to generate structured relational prompts for large language models (LLMs)<n>Unlike traditional text-based serialization approaches, our method preserves the inherent relational structure of databases while enabling LLMs to process and reason over complex entity relationships.
  arXiv  Detail & Related papers  (2025-06-06T04:07:55Z)
• Knowledge Graph Completion with Relation-Aware Anchor Enhancement [50.50944396454757]
  We propose a relation-aware anchor enhanced knowledge graph completion method (RAA-KGC)<n>We first generate anchor entities within the relation-aware neighborhood of the head entity.<n>Then, by pulling the query embedding towards the neighborhoods of the anchors, it is tuned to be more discriminative for target entity matching.
  arXiv  Detail & Related papers  (2025-04-08T15:22:08Z)
• Bridging Textual-Collaborative Gap through Semantic Codes for Sequential Recommendation [91.13055384151897]
  CCFRec is a novel Code-based textual and Collaborative semantic Fusion method for sequential Recommendation.<n>We generate fine-grained semantic codes from multi-view text embeddings through vector quantization techniques.<n>In order to further enhance the fusion of textual and collaborative semantics, we introduce an optimization strategy.
  arXiv  Detail & Related papers  (2025-03-15T15:54:44Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Semi-automatic Data Enhancement for Document-Level Relation Extraction with Distant Supervision from Large Language Models [26.523153535336725]
  Document-level Relation Extraction (DocRE) aims to extract relations from a long context. We propose a method integrating a large language model (LLM) and a natural language inference (NLI) module to generate relation triples. We demonstrate the effectiveness of our approach by introducing an enhanced dataset known as DocGNRE.
  arXiv  Detail & Related papers  (2023-11-13T13:10:44Z)
• Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization [77.21951145754065]
  We propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph. Our CSMGAN is able to effectively capture high-order interactions between two modalities, thus enabling a further precise localization.
  arXiv  Detail & Related papers  (2020-08-04T08:25:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.