LAGCL 4 R ec: When LLM s Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025)

Abstract: A core barrier preventing recommender systems from reaching their full potential lies in the inherent limitations of user-item interaction data: (1) Sparse user-item interactions, making it difficult to learn reliable user preferences; (2) Traditional contrastive learning methods often treat negative samples as equally hard or easy, ignoring the informative semantic difficulty during training. (3) Modern LLM-based recommender systems, on the other hand, discard all negative feedback, leading to unbalanced preference modeling. To address these issues, we propose LAGCL4Rec, a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation. Our approach operates through three stages: (i) Data-Level: augmenting sparse interactions with balanced positive and negative samples using LLM-enriched profiles; (ii) Rank-Level: assessing semantic difficulty of negative samples through LLM-based grouping for fine-grained contrastive learning; and (iii) Rerank-Level: reasoning over augmented historical interactions for personalized recommendations. Theoretical analysis proves that LAGCL4Rec achieves effective information utilization with minimal computational overhead. Experiments across multiple benchmarks confirm our method consistently outperforms state-of-the-art baselines.

## introduction large language models (llms) have demonstrated remarkable capabilities in natural language processing (nlp). the success of llms in nlp has encouraged researchers to explore their potential in recommendation tasks @xcite . current llm-based recommendation approaches generally fall into three categories: (a) semantic augmentation using llms: using llms to enhance textual descriptions of users and items to provide contextual information @xcite ; (b) feature extractions using llms: using llms as feature extractors to generate item and user embeddings @xcite for downstream models; and (c) decision making using llms: directly produce recommendation lists through prompting or finetuning on foundation models @xcite . despite these advances, existing approaches fail to fully unleash llms' potential for recommendation due to three critical limitations: (1) sparse user-item interactions: while (a) approaches augment positive interactions, they neglect the latent potential in negative interactions, leading to an imbalanced activation of interaction data @xcite . (2) ignorance of difficulty of negative samples in contrastive learning: (b) approaches face significant efficiency bottlenecks, while traditional graph contrastive learning (gcl) methods fail to activate the semantic potential in interactions by treating all negative samples uniformly @xcite . (3) neglect of informative negative feedback in llm prompting: by discarding negative interactions, current (c) methods fail to harness the complete spectrum of user preference signals @xcite . meanwhile, most of these research using prompting on general foundation models, ignoring the domain-specific nature of recommendation. this leads us to a question: how can we systematically unlock and activate the full potential of both positive and negative interactions throughout the entire recommendation pipeline to maximize preference modeling capabilities and recommendation performance? as an answer to this question, we introduce lagcl4rec, a novel approach that leverages llms to activate interaction potential in graph contrastive learning through a progressive pipeline operating at each level of the recommenand lagcl4rec (right). data level: traditional methods rely on positive feedback only, while our approach activates potential in both positive and negative interactions. rank level: conventional methods treat all negatives uniformly, whereas lagcl4rec implements semantic-aware grouping to differentiate hard/easy negatives, unlocking their potential for contrastive learning. rerank level: existing approaches ignore negative interaction potential, while our method activates both positive and negative profiles through structured reasoning, enabling full-spectrum personalization. ## methodology we introduce lagcl4rec, a novel framework that leverages llms to activate graph contrastive learning through a progressive activation pipeline operating at multiple levels of the recommendation process, as illustrated in figure 2 . our approach systematically unleashes interaction potential via three complementary mechanisms: (1) data-level activation (section 2.1), (2) rank-level activation (section 2.2) and (3) rerank-level activation (section 2.3). ## theoretical analysis this section provides a theoretical analysis of our pipeline, focusing on the information gain guaranteed by hard negative samples. ## related work recommender systems have long been recognized as a canonical research problem and have attracted sustained attention from the academic community @xcite @xcite , and the advent of large language models (llms) @xcite @xcite has infused new vitality into the field, opening up fresh opportunities for innovation in recommendation @xcite @xcite @xcite @xcite @xcite @xcite @xcite . while llm-based approaches have shown promise, they typically overlook dormant negative signals. graph neural networks (gnns) have emerged as a highly active research frontier and are being applied across a wide spectrum of domains @xcite @xcite , which have transformed recommender systems through their ability to model complex useritem relationships @xcite , but these methods treat all negative interactions uniformly rather than differentiating by difficulty level. our work addresses these limitations through a progressive activation pipeline operating at multiple levels. unlike existing sign-aware systems @xcite @xcite @xcite that lack systematic activation mechanisms, we explicitly awaken dormant signals in raw data, differentially amplify them based on semantic difficulty, and leverage historical interactions through structured reasoning. while recent reranking approaches @xcite @xcite have explored llm integration, they fail to provide the comprehensive activation strategy we introduce. ## conclusion we introduced lagcl4rec, a framework that activates the untapped potential in user-item interactions through our progressive activation pipeline. by integrating llms with graph contrastive learning via data-level, rank-level, and rerank-level mechanisms, we addressed key limitations in existing approaches. experiments across diverse datasets demonstrated significant performance improvements, highlighting the value of systematically unlocking and leveraging interaction potential.