Addressing Class-Imbalance Problem in Personalized Ranking

TL;DR

This paper identifies class-imbalance issues in pairwise ranking models for recommendation, analyzes their impact, and proposes VINS, a bias sampling method that improves training efficiency and ranking quality.

Contribution

It introduces VINS, a novel bias sampling method that alleviates class-imbalance in pairwise ranking models, enhancing training speed and ranking accuracy.

Findings

Training speed increased by 30-50%.

Ranking quality maintained or improved.

Effective for both shallow and deep models.

Abstract

Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into \emph{positive} samples if user-item interactions exist, and \emph{negative} samples otherwise. Due to the limited number of observable interactions, pairwise ranking models face serious \emph{class-imbalance} issues. Our theoretical analysis shows that current sampling-based methods cause the vertex-level imbalance problem, which makes the norm of learned item embeddings towards infinite after a certain training iterations, and consequently results in vanishing gradient and affects the model inference results. We thus propose an efficient \emph{\underline{Vi}tal \underline{N}egative \underline{S}ampler} (VINS) to alleviate the class-imbalance issue for pairwise ranking model, in particular for deep learning models optimized by gradient methods. The core of VINS is a bias sampler with reject probability that will tend to accept a negative candidate with a larger degree weight than the given positive item. Evaluation results on several real datasets demonstrate that the proposed sampling method speeds up the training procedure 30\% to 50\% for ranking models ranging from shallow to deep, while maintaining and even improving the quality of ranking results in top-N item recommendation.