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

Online display advertising platforms service numerous advertisers byproviding real-time bidding (RTB) for the scale of billions of ad requestsevery day. The bidding strategy handles ad requests cross multiple channels tomaximize the number of clicks under the set financial constraints, i.e., totalbudget and cost-per-click (CPC), etc. Different from existing works mainlyfocusing on single channel bidding, we explicitly consider cross-channelconstrained bidding with budget allocation. Specifically, we propose ahierarchical offline deep reinforcement learning (DRL) framework called``HiBid'', consisted of a high-level planner equipped with auxiliary loss fornon-competitive budget allocation, and a data augmentation enhanced low-levelexecutor for adaptive bidding strategy in response to allocated budgets.Additionally, a CPC-guided action selection mechanism is introduced to satisfythe cross-channel CPC constraint. Through extensive experiments on both thelarge-scale log data and online A/B testing, we confirm that HiBid outperformssix baselines in terms of the number of clicks, CPC satisfactory ratio, andreturn-on-investment (ROI). We also deploy HiBid on Meituan advertisingplatform to already service tens of thousands of advertisers every day.

HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning