[图书][B] Trustworthy online controlled experiments: A practical guide to a/b testing
R Kohavi, D Tang, Y Xu - 2020 - books.google.com
Getting numbers is easy; getting numbers you can trust is hard. This practical guide by
experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate …
experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate …
The netflix recommender system: Algorithms, business value, and innovation
CA Gomez-Uribe, N Hunt - ACM Transactions on Management …, 2015 - dl.acm.org
This article discusses the various algorithms that make up the Netflix recommender system,
and describes its business purpose. We also describe the role of search and related …
and describes its business purpose. We also describe the role of search and related …
Unbiased learning-to-rank with biased feedback
Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …
[图书][B] Click models for web search
With the rapid growth of web search in recent years the problem of modeling its users has
started to attract more and more attention of the information retrieval community. This has …
started to attract more and more attention of the information retrieval community. This has …
User preference optimization for control of ankle exoskeletons using sample efficient active learning
One challenge to achieving widespread success of augmentative exoskeletons is accurately
adjusting the controller to provide cooperative assistance with their wearer. Often, the …
adjusting the controller to provide cooperative assistance with their wearer. Often, the …
Measuring the business value of recommender systems
Recommender Systems are nowadays successfully used by all major web sites—from e-
commerce to social media—to filter content and make suggestions in a personalized way …
commerce to social media—to filter content and make suggestions in a personalized way …
Unbiased learning to rank with unbiased propensity estimation
Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …
been explored to debias click data for learning to rank such as click models, result …
Counterfactual estimation and optimization of click metrics in search engines: A case study
Optimizing an interactive system against a predefined online metric is particularly
challenging, especially when the metric is computed from user feedback such as clicks and …
challenging, especially when the metric is computed from user feedback such as clicks and …
Estimating position bias without intrusive interventions
Presentation bias is one of the key challenges when learning from implicit feedback in
search engines, as it confounds the relevance signal. While it was recently shown how …
search engines, as it confounds the relevance signal. While it was recently shown how …
Efficient and effective tree-based and neural learning to rank
As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …