arXiv Machine Learning
Content Exploration Beyond the Feed: Creator Supply and the Shared Corpus
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arXiv Machine Learning
Aug 6
The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing
arXiv:2608. 04432v1 Announce Type: cross Abstract: On two-sided content platforms, symmetric two-sided isolation (assigning matched fractions of creators and viewers to isolated treatment and control submarkets) is widely used for creator-side and cold-start experiments because it removes cross-arm marketplace interference.
arXiv AI
4d ago
ChatGPT Images 2.5 in the Wild: A Launch-Period Dataset and Detector Evaluation
arXiv:2609.15100v1 Announce Type: cross Abstract: An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study t...
arXiv AI
Aug 20
The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
The paper introduces a lifecycle framework for LLM-as-a-Judge systems used to evaluate recommendation explanations at Netflix. It outlines four phases—Birth, Training, Deployment, and Monitoring—detailing how each stage addresses specific technical and operational challenges. The authors report that after five weeks of A/B testing, judge-aligned explanations increased novel content viewing and successful browse-to-play sessions without quality takedowns.
arXiv Machine Learning
Aug 28
Incremental Recommendation via Causal Models
The paper proposes an incremental recommendation system that uses causal modeling to avoid delivering redundant recommendations. By leveraging existing holdback data and a dual‑threshold targeting policy, the authors reduce recommendation impressions by 7% without harming overall content consumption. Joint training with holdback data also improves the calibration of the treated model, suggesting better generalisable representations than purely observational models.
arXiv Computer Vision
Sep 10
MotionBlind: Probing the Illusion of Motion Understanding in Video-LLMs
arXiv:2609.09528v1 Announce Type: new Abstract: Video large language models (Video-LLMs) are increasingly used as the perceptual front end of world models, a role that assumes they can read motion: h...
Hugging Face Trending Papers
Aug 27
Incremental Recommendation via Causal Models
The paper proposes an incremental recommendation approach that uses a causal model built from existing holdback data to avoid delivering redundant recommendations. By applying a dual‑threshold targeting policy, the system only recommends content when the likelihood of a treated stream is high and the likelihood of an organic stream is low, thereby reducing recommendation impressions by 7% without hurting overall consumption. Joint training with holdback data also improves the calibration of the treated head, suggesting that causal models capture more generalisable representations than purely observational models.