Dual-Tree LLM-Enhanced Negative Sampling for Implicit Collaborative Filtering

Abstract: Negative sampling is a pivotal technique in implicit collaborative filtering (CF) recommendation, enabling efficient and effective training by contrasting observed interactions with sampled unobserved ones. Recently, LLMs have shown promise in recommender systems; however, research on LLM-empowered negative sampling remains underexplored. Existing methods heavily rely on textual information and task-specific fine-tuning, limiting practical applicability. To address this limitation, we propose a text-free and fine-tuning-free Dual-Tree LLM-enhanced Negative Sampling method (DTL-NS). It consists of two modules: (i) an offline false negative identification module that leverages hierarchical index trees to transform collaborative structural and latent semantic information into structured item-ID encodings for LLM inference, enabling accurate identification of false negatives; and (ii) a multi-view hard negative sampling module that combines user-item preference scores with item-item hierarchical similarities from these encodings to mine high-quality hard negatives, thus improving models' discriminative ability. Extensive experiments demonstrate the effectiveness of DTL-NS. For example, on the Amazon-sports dataset, DTL-NS outperforms the strongest baseline by 10.64% and 19.12% in Recall@20 and NDCG@20, respectively. Moreover, DTL-NS can be integrated into various implicit CF models and negative sampling methods, consistently enhancing their performance.