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

With the rapid growth of the Internet and overwhelming amount of informationand choices that people are confronted with, recommender systems have beendeveloped to effectively support users' decision-making process in the onlinesystems. However, many recommendation algorithms suffer from the data sparsityproblem, i.e. the user-object bipartite networks are so sparse that algorithmscannot accurately recommend objects for users. This data sparsity problem makesmany well-known recommendation algorithms perform poorly. To solve the problem,we propose a recommendation algorithm based on the semi-local diffusion processon a user-object bipartite network. The numerical simulation on two sparsedatasets, Amazon and Bookcross, show that our method significantly outperformsthe state-of-the-art methods especially for those small-degree users. Twopersonalized semi-local diffusion methods are proposed which further improvethe recommendation accuracy. Finally, our work indicates that sparse onlinesystems are essentially different from the dense online systems, all thealgorithms and conclusions based on dense data should be rechecked again insparse data.

Information filtering in sparse online systems: recommendation via semi-local diffusion