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

Using embeddings as representations of products is quite commonplace inrecommender systems, either by extracting the semantic embeddings of textdescriptions, user sessions, collaborative relationships, or product images. Inthis paper, we present an approach to extract style embeddings for using infashion recommender systems, with a special focus on style information such astextures, prints, material, etc. The main issue of using such a type ofembeddings is its high dimensionality. So, we propose feature reductionsolutions alongside the investigation of its influence in the overall task ofrecommending products of the same style based on their main image. The featurereduction we propose allows for reducing the embedding vector from 600kfeatures to 512, leading to a memory reduction of 99.91\% without criticallycompromising the quality of the recommendations.

How big can style be? Addressing high dimensionality for recommending with style