针对互惠求职推荐中匹配标签稀疏及双向独立模型误差传播问题，提出融合真实标签与稠密预测的 BoB 伪匹配分数及元模型，并支持个性化权重以提升潜在匹配发现效果。
摘要：
将具有相互偏好的用户进行匹配，是求职等由互惠推荐驱动的服务的关键环节。要在此类场景中生成推荐，可以预测匹配概率，并基于这些预测构建排序。然而，由于匹配标签极其稀疏，这种直接匹配预测方法往往表现不佳。因此，大多数现有方法分别预测每个方向的偏好（例如求职者对雇主，以及雇主对求职者），随后聚合这些预测以生成总体匹配分数并产生推荐。但这种典型方法通常会带来实际问题，例如两个模型之间存在偏差的误差传播。本文介绍并展示了一种新颖且实用的解决方案，通过利用伪匹配分数来改进生产环境中的互惠推荐。具体而言，我们的方法将准确但稀疏的真实匹配标签，与相对不准确但稠密的匹配预测相结合，生成稠密且更直接相关的伪匹配分数。随后，我们训练一个元模型，通过最小化相对于伪匹配分数的预测损失来输出最终匹配预测。我们的方法可被视为一种两全其美（BoB）方法，因为它结合了直接匹配预测和两个独立模型方法的高层思想。该方法还允许使用用户特定权重来构建个性化伪匹配分数，并通过适当调整权重实现更优的匹配性能。在真实求职数据上的离线实验表明，相较于现有方法，我们的 BoB 方法在发现潜在匹配方面表现更优，尤其是在使用个性化伪匹配分数时。
英文摘要：
Matching users with mutual preferences is a critical aspect of services driven by reciprocal recommendations, such as job search. To produce recommendations in such scenarios, one can predict match probabilities and construct rankings based on these predictions. However, this direct match prediction approach often underperforms due to the extreme sparsity of match labels. Therefore, most existing methods predict preferences separately for each direction (e.g., job seeker to employer and employer to job seeker) and then aggregate the predictions to generate overall matching scores and produce recommendations. However, this typical approach often leads to practical issues, such as biased error propagation between the two models. This paper introduces and demonstrates a novel and practical solution to improve reciprocal recommendations in production by leveraging pseudo-match scores. Specifically, our approach generates dense and more directly relevant pseudo-match scores by combining the true match labels, which are accurate but sparse, with relatively inaccurate but dense match predictions. We then train a meta-model to output the final match predictions by minimizing the prediction loss against the pseudo-match scores. Our method can be seen as a best-of-both (BoB) approach, as it combines the high-level ideas of both direct match prediction and the two separate models approach. It also allows for user-specific weights to construct personalized pseudo-match scores, achieving even better matching performance through appropriate tuning of the weights. Offline experiments on real-world job search data demonstrate the superior performance of our BoB method, particularly with personalized pseudo-match scores, compared to existing approaches in terms of finding potential matches.
Learning to Match Jobs with Resumes from Sparse Interaction Data using Multi-View Co-Teaching Network
School of Information, Renmin University of China（中国人民大学信息学院）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Key Laboratory of Big Data Management and Analysis Methods（北京市大数据管理与分析方法重点实验室）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）；BOSS Zhipin NLP Center（BOSS直聘自然语言处理中心）
Generative Job Recommendations with Large Language Model
Career Science Lab, BOSS Zhipin（BOSS直聘职业科学实验室）；University of Science and Technology of China（中国科学技术大学）；The Hong Kong University of Science and Technology（香港科技大学）
DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Science and Technology of China（中国科学技术大学）；Institutes of Physical Science and Information Technology, Anhui University（安徽大学物理与信息技术研究院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Revisiting Reciprocal Recommender Systems: Metrics, Formulation, and Method
Nanbeige Lab（南戈实验室）；BOSS Zhipin（BOSS直聘）；Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Career Science Lab, BOSS Zhipin（BOSS直聘职业科学实验室）
