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arXiv · 2606.17397

Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform

Kazuki Sekiya·Suguru Otani·Yuki Komatsu·Yuki Fujii·Shunsuke Ozeki·Shunya Noda
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Abstract

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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Kazuki Sekiya, Suguru Otani, Yuki Komatsu, Yuki Fujii, Shunsuke Ozeki, Shunya Noda. 2026-06-16. Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform. https://arxiv.org/abs/2606.17397

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