all AI news
Interpretable Personalization via Policy Learning with Linear Decision Boundaries. (arXiv:2003.07545v3 [cs.LG] UPDATED)
May 23, 2022, 1:11 a.m. | Zhaonan Qu, Isabella Qian, Zhengyuan Zhou
stat.ML updates on arXiv.org arxiv.org
With the rise of the digital economy and an explosion of available
information on consumers, effective personalization of offers, goods, and
services has become a core business focus for companies to improve revenues and
maintain competitive edge. This paper studies the personalization problem
through the lens of policy learning, where the goal is to learn a
decision-making rule (a policy) that maps from consumer and product
characteristics (features) to recommendations (actions) in order to optimize
outcomes (rewards). We focus on …
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Software Engineer, Data Platforms
@ Whatnot | San Francisco, CA, Los Angeles, CA, New York City, Phoenix, AZ, Seattle, WA, Denver, CO
Staff Data Engineer, Data Platform
@ Lilt | Indianapolis
Business Data Analyst - New Division
@ Breakthru Beverage Group | Toronto, ON, Canada
Data Operations Associate
@ iCapital | New York City, United States
Senior Data Scientist, R&D
@ Plusgrade | Toronto, Ontario