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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Recently, industrial recommendation services have been boosted by thecontinual upgrade of deep learning methods. However, they still face de-biasingchallenges such as exposure bias and cold-start problem, where circulations ofmachine learning training on human interaction history leads algorithms torepeatedly suggest exposed items while ignoring less-active ones. Additionalproblems exist in multi-scenario platforms, e.g. appropriate data fusion fromsubsidiary scenarios, which we observe could be alleviated through graphstructured data integration via message passing.  In this paper, we present a multi-graph structured multi-scenariorecommendation solution, which encapsulates interaction data across scenarioswith multi-graph and obtains representation via graph learning. Extensiveoffline and online experiments on real-world datasets are conducted where theproposed method demonstrates an increase of 0.63% and 0.71% in CTR and VideoViews per capita on new users over deployed set of baselines and outperformsregular method in increasing the number of outer-scenario videos by 25% andvideo watches by 116%, validating its superiority in activating cold videos andenriching target recommendation.

Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services