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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

In this paper, we propose a mechanism to deal with dishonest opinions inrecommendation-based trust models, at both the collection and processinglevels. We consider a scenario in which an agent requests recommendations frommultiple parties to build trust toward another agent. At the collection level,we propose to allow agents to self-assess the accuracy of their recommendationsand autonomously decide on whether they would participate in the recommendationprocess or not. At the processing level, we propose a recommendationsaggregation technique that is resilient to collusion attacks, followed by acredibility update mechanism for the participating agents. The originality ofour work stems from its consideration of dishonest opinions at both thecollection and processing levels, which allows for better and more persistentprotection against dishonest recommenders. Experiments conducted on theEpinions dataset show that our solution yields better performance in protectingthe recommendation process against Sybil attacks, in comparison with acompeting model that derives the optimal network of advisors based on theagents' trust values.

A two-level solution to fight against dishonest opinions in recommendation-based trust systems