NDLI: Hypothesis Testing: How to Eliminate Ideas as Soon as Possible

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Hypothesis Testing: How to Eliminate Ideas as Soon as Possible

Content Provider	ACM Digital Library
Author	Zykov, Roman
Abstract	Retail Rocket helps web shoppers make better shopping decisions by providing personalized real-time recommendations through multiple channels with over 100MM unique monthly users and 1000+ retail partners. The rapid improvement of the product is important to win on the high-concurrency market of real-time personalization platforms. The necessity of introducing constant innovations and improvements of algorithms for recommendation systems requires correct tools and a process of rapid testing of hypotheses. It's not a secret that 9 out of 10 hypotheses actually do not improve the performance at least. We had the task stated as follows: How to detect and eliminate the idea that doesn't improve as early as possible, to spend a minimum of resources on that process. In the report we will talk about: How we make our process of hypotheses testing faster. One programming language for R&D. Enmity and friendship of offline and online metrics. Why it is difficult to predict the impact of changing diversity of algorithms. What is the benefit of AA/BB online tests. Bayesian statistics for the evaluation of online tests. Roman Zykov is the Chief Data Scientist at the Retail Rocket. In Retail Rocket is responsible for algorithms of personalized and non-personalized recommendations. Previous to Retail Rocket, Roman was the Head of analytics at the biggest e-commerce companies for almost ten years. He received Ms.Sc. in applied mathematics and physics from the MIPhT in 2004.
Starting Page	387
Ending Page	387
Page Count	1
File Format	PDF
ISBN	9781450340359
DOI	10.1145/2959100.2959127
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Evaluation Aa/bb testing Bayesian statistics Hypotheses testing A/b testing Diversification Recommender systems
Content Type	Text
Resource Type	Article

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