Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

TL;DR

This paper introduces DINS, a novel area-wise negative sampling method for collaborative filtering that improves training effectiveness by exploring hard sampling areas more comprehensively.

Contribution

It presents the first Area-wise sampling approach, DINS, which enhances negative sampling in collaborative filtering models by addressing limitations of existing point-wise and line-wise methods.

Findings

DINS outperforms existing negative sampling methods on real-world datasets.

DINS achieves state-of-the-art performance in collaborative filtering tasks.

The approach is effective for both matrix factorization and graph-based models.

Abstract

Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch.