Parallel and Mini-Batch Stable Matching for Large-Scale Reciprocal Recommender Systems

TL;DR

This paper introduces parallel and mini-batch algorithms for stable reciprocal matching in large-scale recommender systems, significantly improving computational efficiency and scalability to handle up to one million users without sacrificing match quality.

Contribution

It presents novel parallel and mini-batch methods that enhance the efficiency of stable matching algorithms for large-scale reciprocal recommender systems.

Findings

Increased computation speed for large datasets

Scalable to one million samples on a single GPU

Maintained match count while improving efficiency

Abstract

Reciprocal recommender systems (RRSs) are crucial in online two-sided matching platforms, such as online job or dating markets, as they need to consider the preferences of both sides of the match. The concentration of recommendations to a subset of users on these platforms undermines their match opportunities and reduces the total number of matches. To maximize the total number of expected matches among market participants, stable matching theory with transferable utility has been applied to RRSs. However, computational complexity and memory efficiency quadratically increase with the number of users, making it difficult to implement stable matching algorithms for several users. In this study, we propose novel methods using parallel and mini-batch computations for reciprocal recommendation models to improve the computational time and space efficiency of the optimization process for stable matching. Experiments on both real and synthetic data confirmed that our stable matching theory-based RRS increased the computation speed and enabled tractable large-scale data processing of up to one million samples with a single graphics processing unit graphics board, without losing the match count.