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 study a strategic variant of the multi-armed bandit problem, which we cointhe strategic click-bandit. This model is motivated by applications in onlinerecommendation where the choice of recommended items depends on both theclick-through rates and the post-click rewards. Like in classical bandits,rewards follow a fixed unknown distribution. However, we assume that theclick-rate of each arm is chosen strategically by the arm (e.g., a host onAirbnb) in order to maximize the number of times it gets clicked. The algorithmdesigner does not know the post-click rewards nor the arms' actions (i.e.,strategically chosen click-rates) in advance, and must learn both values overtime. To solve this problem, we design an incentive-aware learning algorithm,UCB-S, which achieves two goals simultaneously: (a) incentivizing desirable armbehavior under uncertainty; (b) minimizing regret by learning unknownparameters. We characterize all approximate Nash equilibria among arms underUCB-S and show a $\tilde{\mathcal{O}} (\sqrt{KT})$ regret bound uniformly inevery equilibrium. We also show that incentive-unaware algorithms generallyfail to achieve low regret in the strategic click-bandit. Finally, we supportour theoretical results by simulations of strategic arm behavior which confirmthe effectiveness and robustness of our proposed incentive design.

Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation