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

Recommender systems act as decision support systems in supporting users in selecting the right choice of items or services from a high number of choices in an overloaded search space. However, such systems have difficulty dealing with sparse rating data. One way to deal with this issue is to incorporate additional explicit information, also known as side information, to the rating information. However, this side information requires some explicit action from the users and often not always available. Accordingly, this study presents a hybrid multi-criteria collaborative filtering model. The proposed model exploits the multi-criteria ratings, implicit similarity, similarity transitivity and global reputation concepts to expand the space of potential recommenders. This expansion will enhance the prediction accuracy and coverage of the proposed model when applied to sparse data situations. To show effectiveness of the proposed model, a set of experiments are conducted on two real-world multi-criteria datasets, Yahoo! Movies and TripAdvisor. The experimental results demonstrate the superiority of the proposed model compared to a number of existing collaborative filtering-based recommendation methods under a variety of evaluation metrics.

A Hybrid Multi-Criteria Collaborative Filtering Model for Effective Personalized Recommendations