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 investigate the problem of fair recommendation in the context of two-sidedonline platforms, comprising customers on one side and producers on the other.Traditionally, recommendation services in these platforms have focused onmaximizing customer satisfaction by tailoring the results according to thepersonalized preferences of individual customers. However, our investigationreveals that such customer-centric design may lead to unfair distribution ofexposure among the producers, which may adversely impact their well-being. Onthe other hand, a producer-centric design might become unfair to the customers.Thus, we consider fairness issues that span both customers and producers. Ourapproach involves a novel mapping of the fair recommendation problem to aconstrained version of the problem of fairly allocating indivisible goods. Ourproposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposurefor most of the producers and Envy-Free up to One item (EF1) fairness for everycustomer. Extensive evaluations over multiple real-world datasets show theeffectiveness of FairRec in ensuring two-sided fairness while incurring amarginal loss in the overall recommendation quality.

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms