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

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Listed:
  • Kazuki Sekiya
  • Suguru Otani
  • Yuki Komatsu
  • Yuki Fujii
  • Shunsuke Ozeki
  • Shunya Noda

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.

Suggested Citation

  • Kazuki Sekiya & Suguru Otani & Yuki Komatsu & Yuki Fujii & Shunsuke Ozeki & Shunya Noda, 2026. "Designing Recommendation Exposure and Favorite Lists: A Field Experiment in a Spot-Work Platform," Papers 2606.17397, arXiv.org, revised Jun 2026.
    • Handle: RePEc:arx:papers:2606.17397
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    References listed on IDEAS

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    1. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    2. Benjamin Knight & Dmitry Mitrofanov, 2026. "Disclosing Low Product Availability: An Online Platform’s Strategy for Mitigating Stockout Risk," Management Science, INFORMS, vol. 72(2), pages 1560-1578, February.
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