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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The substitute-based recommendation is widely used in E-commerce to providebetter alternatives to customers. However, existing research typically uses thecustomer behavior signals like co-view and view-but-purchase-another to capturethe substitute relationship. Despite its intuitive soundness, we find that suchan approach might ignore the functionality and characteristics of products. Inthis paper, we adapt substitute recommendation into language matching problemby taking product title description as model input to consider productfunctionality. We design a new transformation method to de-noise the signalsderived from production data. In addition, we consider multilingual supportfrom the engineering point of view. Our proposed end-to-end transformer-basedmodel achieves both successes from offline and online experiments. The proposedmodel has been deployed in a large-scale E-commerce website for 11 marketplacesin 6 languages. Our proposed model is demonstrated to increase revenue by 19%based on an online A/B experiment.

A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data