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

TL;DR

This paper introduces a novel, text-free, and fine-tuning-free negative sampling method for implicit collaborative filtering that leverages hierarchical index trees and large language models to improve recommendation accuracy.

Contribution

It proposes a dual-tree, LLM-enhanced negative sampling approach that accurately identifies false negatives and mines high-quality negatives without textual data or fine-tuning.

Findings

Demonstrates significant improvement in recommendation performance across various models.

Shows broad applicability of the method with different LLMs and negative sampling techniques.

Validates effectiveness through extensive experiments.

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, large language models (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 this end, 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 negatives, thus improving the discriminative ability of recommender models. Extensive experiments demonstrate the effectiveness of DTL-NS. Moreover, DTL-NS shows broad applicability across different implicit CF models, negative sampling methods, and LLMs, consistently enhancing recommendation performance.