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

Personalized news recommendation aims to provide attractive articles forreaders by predicting their likelihood of clicking on a certain article. Toaccurately predict this probability, plenty of studies have been proposed thatactively utilize content features of articles, such as words, categories, orentities. However, we observed that the articles' contextual features, such asCTR (click-through-rate), popularity, or freshness, were either neglected orunderutilized recently. To prove that this is the case, we conducted anextensive comparison between recent deep-learning models and naive contextualmodels that we devised and surprisingly discovered that the latter easilyoutperforms the former. Furthermore, our analysis showed that the recenttendency to apply overly sophisticated deep-learning operations to contextualfeatures was actually hindering the recommendation performance. From thisknowledge, we design a purposefully simple contextual module that can boost theprevious news recommendation models by a large margin.

On the Overlooked Significance of Underutilized Contextual Features in Recent News Recommendation Models