为抢手工作模板设计公平推荐机制，提升匹配效率
Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform
- 按岗位发布频率和空缺数重分配推荐曝光，避免热门模板过度集中
- 模拟显示每轮求职成功率从57.6%提升至70.0%
- 真实实验验证可提高匹配量、降低低曝光模板比例
当推荐系统影响稀缺且短暂的工作机会获取时，如何设计更合理的推荐机制？我们在日本最大零工平台Timee展开研究：工人收藏工作模板后，会收到对应模板的新岗位通知。单纯追求高预测收藏率会导致资源错配——热门模板虽被频繁推荐，但实际岗位少；而有真实用工需求的模板却曝光不足。为此，我们提出基于阈值的可选性控制（TEC）机制，根据岗位发布活跃度与空缺容量动态调整推荐优先级。该方法完全并行，适用于大规模平台。在基于Timee数据的模拟中，TEC将每轮求职成功概率从57.6%提升至70.0%。一次县级范围的随机对照实验表明，该机制显著提升了实际匹配数与每个活跃模板的曝光量，减少了低曝光模板占比，并改善了点击收藏率及后续匹配效果。
原文摘要 · Abstract (English)
How should recommender systems be designed when recommendations shape access to scarce, short-lived opportunities? We study this question in a production setting: Timee, Japan's largest platform for spot work, where workers favorite job templates and receive notifications when firms post shifts from those templates. Maximizing predicted favoriting can generate misdirected concentration: recommendations accumulate on popular templates that create few viable job openings, while templates with unmet labor demand receive too little exposure. We design exposure-control mechanisms for favorite-list management, reallocating template exposure based on posting activity and unfilled capacity. The proposed recommender, thresholded eligibility control (TEC), is fully parallelizable and suitable for large-scale digital platforms. In simulations calibrated to Timee data, TEC raises the per-round job-finding rate from 57.6% to 70.0%. A prefecture-level randomized field experiment increases realized matches and exposure per active template, reduces the share of low-exposure templates, and improves impression-level favoriting and downstream matching.
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