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

We introduce Probabilistic Rank and Reward (PRR), a scalable probabilisticmodel for personalized slate recommendation. Our approach allowsstate-of-the-art estimation of the user interests in the ubiquitous scenariowhere the user interacts with at most one item from a slate of K items. We showthat the probability of a slate being successful can be learned efficiently bycombining the reward, whether the user successfully interacted with the slate,and the rank, the item that was selected within the slate. PRR outperformscompeting approaches that use one signal or the other and is far more scalableto large action spaces. Moreover, PRR allows fast delivery of recommendationspowered by maximum inner product search (MIPS), making it suitable in lowlatency domains such as computational advertising.

Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation