English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Off-policy evaluation (OPE) is the method that attempts to estimate theperformance of decision making policies using historical data generated bydifferent policies without conducting costly online A/B tests. Accurate OPE isessential in domains such as healthcare, marketing or recommender systems toavoid deploying poor performing policies, as such policies may hart human livesor destroy the user experience. Thus, many OPE methods with theoreticalbackgrounds have been proposed. One emerging challenge with this trend is thata suitable estimator can be different for each application setting. It is oftenunknown for practitioners which estimator to use for their specificapplications and purposes. To find out a suitable estimator among manycandidates, we use a data-driven estimator selection procedure for off-policypolicy performance estimators as a practical solution. As proof of concept, weuse our procedure to select the best estimator to evaluate coupon treatmentpolicies on a real-world online content delivery service. In the experiment, wefirst observe that a suitable estimator might change with different definitionsof the outcome variable, and thus the accurate estimator selection is criticalin real-world applications of OPE. Then, we demonstrate that, by utilizing theestimator selection procedure, we can easily find out suitable estimators foreach purpose.

Data-Driven Off-Policy Estimator Selection: An Application in User Marketing on An Online Content Delivery Service