DMMの検索に機械学習を導入して、A/B テストで圧勝した考え方 - DMM inside｜DMM inside
ML Platform Meetup: Infra for Contextual Bandits and Reinforcement Learning
Infrastructure for Contextual Bandits and Reinforcement Learning — theme of the ML Platform meetup hosted at Netflix, Los Gatos on Sep 12, 2019. Contextual and Multi-armed Bandits enable faster and adaptive alternatives to traditional A/B Testing. They enable rapid learning and better decision-making for product rollouts. Broadly speaking, these approaches can be seen as a stepping stone to full-o
Under the Hood of Uber’s Experimentation Platform
Data / MLUnder the Hood of Uber’s Experimentation PlatformAugust 28, 2018 / Global Experimentation is at the core of how Uber improves the customer experience. Uber applies several experimental methodologies to use cases as diverse as testing out a new feature to enhancing our app design. Uber’s Experimentation Platform (XP) plays an important role in this process, enabling us to launch, debug, me
Building an Intelligent Experimentation Platform with Uber Engineering
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more During our inaugural Uber Technology Day, data scientist Eva Feng delivered a presentation on Uber’s experimentation platform (XP). In this article, she and colleague Zhenyu Zhao detail how Uber engineered an XP capable of rolling out new features stably and quickly at
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Discover opportunities at Pinterest
Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal,
Netflix Recommendations: Beyond the 5 stars (Part 2)
by Xavier Amatriain and Justin Basilico (Personalization Science and Engineering) In part one of this blog post, we detailed the different components of Netflix personalization. We also explained how Netflix personalization, and the service as a whole, have changed from the time we announced the Netflix Prize. The $1M Prize delivered a great return on investment for us, not only in algorithmic inn
Netflix Recommendations: Beyond the 5 stars (Part 1)
by Xavier Amatriain and Justin Basilico (Personalization Science and Engineering) In this two-part blog post, we will open the doors of one of the most valued Netflix assets: our recommendation system. In Part 1, we will relate the Netflix Prize to the broader recommendation challenge, outline the external components of our personalized service, and highlight how our task has evolved with the busi
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