arXiv Machine Learning

From Live to Recording: Consumer Demand and Response to Price Across the Livestreaming Lifecycle

arXiv:2107. 01629v3 Announce Type: replace-cross Abstract: Livestreaming has evolved into a thriving industry where creators can directly monetize and engage with their audiences and followers.

arXiv Machine Learning
Aug 6

Multi-Objective Ranking for Live-Streaming: Balancing Fresh and Delayed Signals with Segment-Aware Targeting

arXiv:2608. 04455v1 Announce Type: cross Abstract: One of the most challenging problems entertainment live-streaming services face in recommendation systems is that user behaviors are sparse and delayed, and interaction data exhibits bias for different user segments.

By Xiaoyi Gu, Julia Tavares, Eder Santana, Carlos Mendoza-Cardenas, Nikita Mishra, Saad Ali
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.

By Yuanyuan Shen, Yiren Yan, Wenjie Li, Chunhui Zhu
arXiv Machine Learning
Jun 15

High-Frequency Pricing at Scale for E-Commerce

arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.

By Stefan Birr, Tobias Huelden, Mones Raslan, Adele Gouttes, Andreas Schmitt, Mateusz Koren, Johannes Stephan, Robert Streek, Manuel Kunz, Tim Januschowski
arXiv Machine Learning
Jun 9

The Value of Personalized Recommendations: Evidence from Netflix

arXiv:2511. 07280v5 Announce Type: replace-cross Abstract: Personalized recommendation systems shape much of user choice online, yet their targeted nature makes separating out the value of recommendation and the underlying goods challenging.

By Kevin Zielnicki, Guy Aridor, Aur\'elien Bibaut, Allen Tran, Winston Chou, Nathan Kallus
arXiv Machine Learning
Jun 9

Benchmark Datasets for Lead-Lag Forecasting on Social Platforms

arXiv:2511. 03877v2 Announce Type: replace Abstract: Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are followed, sometimes months or years later, by higher impact like citations, sales, or reviews.

By Kimia Kazemian (Department of Computer Science, Cornell University), Zhenzhen Liu (Department of Computer Science, Cornell University), Yangfanyu Yang (Department of Information Science, Cornell University), Katie Luo (Department of Computer Science, Stanford University), Shuhan Gu (Department of Computer Science, Cornell University), Audrey Du (Department of Computer Science, Cornell University), Xinyu Yang (Department of Information Science, Cornell University), Jack Jansons (Department of Computer Science, Cornell University), Kilian Q. Weinberger (Department of Computer Science, Cornell University), John Thickstun (Department of Computer Science, Cornell University), Yian Yin (Department of Information Science, Cornell University), Sarah Dean (Department of Computer Science, Cornell University)