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

News Recommendation System(NRS) has become a fundamental technology to manyonline news services. Meanwhile, several studies show that recommendationsystems(RS) are vulnerable to data poisoning attacks, and the attackers havethe ability to mislead the system to perform as their desires. A widely studiedattack approach, injecting fake users, can be applied on the NRS when the NRSis treated the same as the other systems whose items are fixed. However, in theNRS, as each item (i.e. news) is more informative, we propose a novel approachto poison the NRS, which is to perturb contents of some browsed news thatresults in the manipulation of the rank of the target news. Intuitively, anattack is useless if it is highly likely to be caught, i.e., exposed. Toaddress this, we introduce a notion of the exposure risk and propose a novelproblem of attacking a history news dataset by means of perturbations where thegoal is to maximize the manipulation of the target news rank while keeping therisk of exposure under a given budget. We design a reinforcement learningframework, called TDP-CP, which contains a two-stage hierarchical model toreduce the searching space. Meanwhile, influence estimation is also applied tosave the time on retraining the NRS for rewards. We test the performance ofTDP-CP under three NRSs and on different target news. Our experiments show thatTDP-CP can increase the rank of the target news successfully with a limitedexposure budget.

Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation