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 analyze a general problem in a crowd-sourced setting where one user asks aquestion (also called item) and other users return answers (also called labels)for this question. Different from existing crowd sourcing work which focuses onfinding the most appropriate label for the question (the "truth"), our problemis to determine a ranking of the users based on their ability to answerquestions. We call this problem "ability discovery" to emphasize the connectionto and duality with the more well-studied problem of "truth discovery".  To model items and their labels in a principled way, we draw upon ItemResponse Theory (IRT) which is the widely accepted theory behind standardizedtests such as SAT and GRE. We start from an idealized setting where therelative performance of users is consistent across items and better userschoose better fitting labels for each item. We posit that a principledalgorithmic solution to our more general problem should solve this idealsetting correctly and observe that the response matrices in this setting obeythe Consecutive Ones Property (C1P). While C1P is well understoodalgorithmically with various discrete algorithms, we devise a novel variant ofthe HITS algorithm which we call "HITSNDIFFS" (or HND), and prove that it canrecover the ideal C1P-permutation in case it exists. Unlike fast combinatorialalgorithms for finding the consecutive ones permutation (if it exists), HNDalso returns an ordering when such a permutation does not exist. Thus itprovides a principled heuristic for our problem that is guaranteed to returnthe correct answer in the ideal setting. Our experiments show that HND producesuser rankings with robustly high accuracy compared to state-of-the-art truthdiscovery methods. We also show that our novel variant of HITS scales better inthe number of users than ABH, the only prior spectral C1P reconstructionalgorithm.

HITSnDIFFs: From Truth Discovery to Ability Discovery by Recovering Matrices with the Consecutive Ones Property