mlsanityData to 14 Oct 2026

DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage

arXiv 2610.15696 ↗First posted 11 Oct 2026cs.LGcited by 0

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Abstract (arXiv)

We present DRanker (Disney+ Ranker), a transformer-based multitask ranking model serving personalized recommendations on the Disney+ homepage. We describe the encoder-only architecture over enriched engagement-history sequences paired with a lightweight causal debiasing mechanism, and the inference stack that enables transformer-based ranking at scale. Through iterative development, we systematically evaluated more complex alternatives and found that simpler designs consistently outperformed them on offline metrics, qualitative behavioral evaluation, and in production. Deployed globally, DRanker drives over 1% lift in our key engagement metric and 0.5% lift in daily active users across all experiences powered by the ranker. We share practical lessons on data engineering, debiasing, and architectural choices that shaped our ranking system at scale.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 11 Oct 2026.

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References · 18

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6 more
  1. 2020Feedback Loop and Bias Amplification in Recommender Systemscited by 90
  2. 2019Managing Popularity Bias in Recommender Systems with Personalized Re-rankingcited by 82
  3. 2023Multi-Task Deep Recommender Systems: A Surveycited by 30
  4. 2022OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with Strangerscited by 9
  5. 2022Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencingcited by 4
  6. 2023CAM2: Conformity-Aware Multi-Task Ranking Model for Large-Scale Recommender Systemscited by 1

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