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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Despite the development of ranking optimization techniques, pointwise lossremains the dominating approach for click-through rate prediction. It can beattributed to the calibration ability of the pointwise loss since theprediction can be viewed as the click probability. In practice, a CTRprediction model is also commonly assessed with the ranking ability. Tooptimize the ranking ability, ranking loss (e.g., pairwise or listwise loss)can be adopted as they usually achieve better rankings than pointwise loss.Previous studies have experimented with a direct combination of the two lossesto obtain the benefit from both losses and observed an improved performance.However, previous studies break the meaning of output logit as theclick-through rate, which may lead to sub-optimal solutions. To address thisissue, we propose an approach that can Jointly optimize the Ranking andCalibration abilities (JRC for short). JRC improves the ranking ability bycontrasting the logit value for the sample with different labels and constrainsthe predicted probability to be a function of the logit subtraction. We furthershow that JRC consolidates the interpretation of logits, where the logits modelthe joint distribution. With such an interpretation, we prove that JRCapproximately optimizes the contextualized hybrid discriminative-generativeobjective. Experiments on public and industrial datasets and online A/B testingshow that our approach improves both ranking and calibration abilities. SinceMay 2022, JRC has been deployed on the display advertising platform of Alibabaand has obtained significant performance improvements.

Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model