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

Personalization enables businesses to learn customer preferences from pastinteractions and thus to target individual customers with more relevantcontent. We consider the problem of predicting the optimal promotional offerfor a given customer out of several options as a contextual bandit problem.Identifying information for the customer and/or the campaign can be used todeduce unknown customer/campaign features that improve optimal offerprediction. Using a generated synthetic email promo dataset, we demonstratesimilar prediction accuracies for (a) a wide and deep network that takesidentifying information (or other categorical features) as input to the widepart and (b) a deep-only neural network that includes embeddings of categoricalfeatures in the input. Improvements in accuracy from including categoricalfeatures depends on the variability of the unknown numerical features for eachcategory. We also show that selecting options using upper confidence bound orThompson sampling, approximated via Monte Carlo dropout layers in the wide anddeep models, slightly improves model performance.

Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo Recommendations