用并行与小批量计算提升大规模双向推荐系统匹配效率
Parallel and Mini-Batch Stable Matching for Large-Scale Reciprocal Recommender Systems
- 采用并行与小批量计算优化稳定匹配算法
- 单块显卡处理百万级数据，速度提升显著
- 适合需要高效匹配的在线招聘/约会平台
双向推荐系统（RRS）在在线双面匹配平台（如求职或婚恋网站）中至关重要，需同时考虑双方偏好。现有推荐集中于少数用户，降低整体匹配机会。为最大化预期匹配总数，已有研究引入可转移效用的稳定匹配理论。然而，传统算法随用户数增加，计算复杂度和内存消耗呈二次增长，难以应用于大规模场景。本文提出基于并行与小批量计算的新方法，显著提升优化过程的计算与空间效率。在真实与合成数据上的实验表明，该方法使基于稳定匹配理论的RRS实现计算加速，可在单张显卡上处理高达一百万样本的数据，且不损失匹配数量。
原文摘要 · Abstract (English)
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.
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