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

User interest modeling is critical for personalized news recommendation.Existing news recommendation methods usually learn a single user embedding foreach user from their previous behaviors to represent their overall interest.However, user interest is usually diverse and multi-grained, which is difficultto be accurately modeled by a single user embedding. In this paper, we proposea news recommendation method with hierarchical user interest modeling, namedHieRec. Instead of a single user embedding, in our method each user isrepresented in a hierarchical interest tree to better capture their diverse andmulti-grained interest in news. We use a three-level hierarchy to represent 1)overall user interest; 2) user interest in coarse-grained topics like sports;and 3) user interest in fine-grained topics like football. Moreover, we proposea hierarchical user interest matching framework to match candidate news withdifferent levels of user interest for more accurate user interest targeting.Extensive experiments on two real-world datasets validate our method caneffectively improve the performance of user modeling for personalized newsrecommendation.

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation