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

An excessive number of customers often leads to a degradation in servicequality. However, the capacity constraints of services are ignored byrecommender systems, which may lead to unsatisfactory recommendation. Thisproblem can be solved by limiting the number of users who receive therecommendation for a service, but this may be viewed as unfair. In this paper,we propose a novel metric Top-N Fairness to measure the individual fairness ofmulti-round recommendations of services with capacity constraints. Byconsidering the fact that users are often only affected by top-ranked items ina recommendation, Top-N Fairness only considers a sub-list consisting of top Nservices. Based on the metric, we design FAST, a Fairness Assured servicerecommendation STrategy. FAST adjusts the original recommendation list toprovide users with recommendation results that guarantee the long-term fairnessof multi-round recommendations. We prove the convergence property of thevariance of Top-N Fairness of FAST theoretically. FAST is tested on the Yelpdataset and synthetic datasets. The experimental results show that FASTachieves better recommendation fairness while still maintaining highrecommendation quality.

FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint