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

The proliferation of personalized recommendation technologies has raisedconcerns about discrepancies in their recommendation performance acrossdifferent genders, age groups, and racial or ethnic populations. This varyingdegree of performance could impact users' trust in the system and may poselegal and ethical issues in domains where fairness and equity are criticalconcerns, like job recommendation. In this paper, we investigate severalpotential factors that could be associated with discriminatory performance of arecommendation algorithm for women versus men. We specifically study severalcharacteristics of user profiles and analyze their possible associations withdisparate behavior of the system towards different genders. Thesecharacteristics include the anomaly in rating behavior, the entropy of users'profiles, and the users' profile size. Our experimental results on a publicdataset using four recommendation algorithms show that, based on all the threementioned factors, women get less accurate recommendations than men indicatingan unfair nature of recommendation algorithms across genders.

Investigating Potential Factors Associated with Gender Discrimination in Collaborative Recommender Systems