SAHC-NS: Structure-Aware and Hardness-Calibrated Negative Sampling for Implicit Collaborative Filtering

2026-08-17

AI Summary

This study addresses the limitations of existing negative sampling strategies in implicit collaborative filtering, specifically their failure to account for candidate pool hardness variations and multi-layered structural information. To overcome these issues, we propose a Structure-Aware and Hardness-Calibrated Negative Sampling method. This approach leverages inter-layer matching scores to capture cross-layer structural discrepancies for effective negative sample selection, while incorporating a candidate pool-aware hardness calibration module to enable dynamic control over sample difficulty. Extensive experiments across multiple datasets demonstrate that the proposed method significantly outperforms state-of-the-art negative sampling strategies, yielding substantial improvements in recommendation performance. Consequently, this work offers a novel structural perspective for mitigating the insufficient training value of negative samples in recommender systems.

Abstract

Negative sampling is a key component of implicit collaborative filtering (CF), as it enables recommenders to effectively learn user preferences. Existing negative sampling methods mostly follow a two-stage paradigm: they first construct a candidate negative pool for each user and then select negative samples from the pool according to predefined sampling rules. However, these methods usually overlook the hardness variation of candidate negative pools across users, making it difficult to adaptively adjust the hardness and informativeness of negative samples according to candidate-pool conditions. In addition, most existing samplers evaluate candidate negatives mainly through a matching score computed from the final aggregated user and item embeddings, while ignoring the structural differences captured by multi-hop neighborhood aggregation. As a result, the training value of negatives may be insufficiently characterized. To address these issues, we propose SAHC-NS, a Structure-Aware and Hardness-Calibrated Negative Sampling method. Specifically, SAHC-NS uses the mean and standard deviation of layer-wise matching scores to capture the overall matching strength and cross-layer structural discrepancy of candidate negatives, respectively. This enables SAHC-NS to select informative negatives by taking cross-layer structural discrepancy into account, rather than relying solely on final matching scores. Moreover, SAHC-NS introduces a candidate-pool-aware hardness calibration module to dynamically adjust negative augmentation strength according to candidate-pool hardness, producing hardness-controllable negatives. Extensive experiments demonstrate the superiority of SAHC-NS over existing negative sampling methods.

Problem

• Implicit Collaborative Filtering
• Negative Sampling
• Hardness Calibration
• Structural Discrepancy

Innovation

• Negative Sampling
• Structure-Aware
• Hardness Calibration
• Implicit Collaborative Filtering
• Cross-layer Structural Discrepancy