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

Recommenders personalize the web content by typically using collaborativefiltering to relate users (or items) based on explicit feedback, e.g., ratings.The difficulty of collecting this feedback has recently motivated to considerimplicit feedback (e.g., item consumption along with the corresponding time).  In this paper, we introduce the notion of consumed item pack (CIP) whichenables to link users (or items) based on their implicit analogous consumptionbehavior. Our proposal is generic, and we show that it captures three novelimplicit recommenders: a user-based (CIP-U), an item-based (CIP-I), and a wordembedding-based (DEEPCIP), as well as a state-of-the-art technique usingimplicit feedback (FISM). We show that our recommenders handle incrementalupdates incorporating freshly consumed items. We demonstrate that all threerecommenders provide a recommendation quality that is competitive withstate-of-the-art ones, including one incorporating both explicit and implicitfeedback.

Sequences, Items And Latent Links: Recommendation With Consumed Item Packs