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

Embedding is a useful technique to project a high-dimensional feature into alow-dimensional space, and it has many successful applications including linkprediction, node classification and natural language processing. Currentapproaches mainly focus on static data, which usually lead to unsatisfactoryperformance in applications involving large changes over time. How todynamically characterize the variation of the embedded features is stilllargely unexplored. In this paper, we introduce a dynamic variational embedding(DVE) approach for sequence-aware data based on recent advances in recurrentneural networks. DVE can model the node's intrinsic nature and temporalvariation explicitly and simultaneously, which are crucial for exploration. Wefurther apply DVE to sequence-aware recommender systems, and develop anend-to-end neural architecture for link prediction.

DVE: Dynamic Variational Embeddings with Applications in Recommender Systems