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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the rating for unseen items with high accuracy. However, they fail to justify their output. The main objective of this paper is to present a novel approach that employs Semantic Web technologies to generate explanations for the output of black box recommender systems. The proposed model significantly outperforms state-of-the-art baseline models in terms of the error rate. Moreover, it produces more explainable items than all baseline approaches.

Augmented Semantic Explanations for Collaborative Filtering Recommendations