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

Deep learning based recommender systems (DLRSs) often have embedding layers,which are utilized to lessen the dimensionality of categorical variables (e.g.user/item identifiers) and meaningfully transform them in the low-dimensionalspace. The majority of existing DLRSs empirically pre-define a fixed andunified dimension for all user/item embeddings. It is evident from recentresearches that different embedding sizes are highly desired for differentusers/items according to their popularity. However, manually selectingembedding sizes in recommender systems can be very challenging due to the largenumber of users/items and the dynamic nature of their popularity. Thus, in thispaper, we propose an AutoML based end-to-end framework (AutoEmb), which canenable various embedding dimensions according to the popularity in an automatedand dynamic manner. To be specific, we first enhance a typical DLRS to allowvarious embedding dimensions; then we propose an end-to-end differentiableframework that can automatically select different embedding dimensionsaccording to user/item popularity; finally we propose an AutoML basedoptimization algorithm in a streaming recommendation setting. The experimentalresults based on widely used benchmark datasets demonstrate the effectivenessof the AutoEmb framework.

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations