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

The conversational recommender systems (CRSs) have received extensiveattention in recent years. However, most of the existing works focus on variousdeep learning models, which are largely limited by the requirement oflarge-scale human-annotated datasets. Such methods are not able to deal withthe cold-start scenarios in industrial products. To alleviate the problem, wepropose FORCE, a Framework Of Rule-based Conversational Recommender system thathelps developers to quickly build CRS bots by simple configuration. We conductexperiments on two datasets in different languages and domains to verify itseffectiveness and usability.

FORCE: A Framework of Rule-Based Conversational Recommender System