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

Recommendation system has been widely used in different areas. Collaborativefiltering focuses on rating, ignoring the features of items itself. In order toeffectively evaluate customers preferences on books, taking into considerationof the characteristics of offline book retail, we use LDA model to calculatecustomers preference on book topics and use word2vec to calculate customerspreference on book types. When forecasting rating on books, we take two factorsinto consideration: similarity of customers and correlation between customersand books. The experiment shows that our hybrid recommendation method based onfeatures performances better than single recommendation method in offline bookretail data.

A Hybrid Recommendation Method Based on Feature for Offline Book Personalization