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

With the development of e-commerce, most of the existing catering management system lags consumers and managers^ need. An effective approach is to apply recommendation systems to catering management, and to provide or­ dering recommendations according to consumers, behavior data. As for cold start problems that may arise in the recom­ mending process, the ordering recommender system based on consumers^ behavior was proposed, containing three re­ commendation engines ? which are frequency statistics ? association rules and Markov chain. Experiments on ordering data of real restaurants achieve a satisfactory result, and get a weight combination of three recommendation engine: (0. 2167, 0. 5167,0. 2666) ,and the best recommending length under that weight: 3.

基于消费者行为的点餐推荐算法 (Ordering Recommender Algorithm Based on Consumers' Behavior).