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

Modeling customer shopping intentions is a crucial task for e-commerce, as itdirectly impacts user experience and engagement. Thus, accurately understandingcustomer preferences is essential for providing personalized recommendations.Session-based recommendation, which utilizes customer session data to predicttheir next interaction, has become increasingly popular. However, existingsession datasets have limitations in terms of item attributes, user diversity,and dataset scale. As a result, they cannot comprehensively capture thespectrum of user behaviors and preferences. To bridge this gap, we present theAmazon Multilingual Multi-locale Shopping Session Dataset, namely Amazon-M2. Itis the first multilingual dataset consisting of millions of user sessions fromsix different locales, where the major languages of products are English,German, Japanese, French, Italian, and Spanish. Remarkably, the dataset canhelp us enhance personalization and understanding of user preferences, whichcan benefit various existing tasks as well as enable new tasks. To test thepotential of the dataset, we introduce three tasks in this work: (1)next-product recommendation, (2) next-product recommendation with domainshifts, and (3) next-product title generation. With the above tasks, webenchmark a range of algorithms on our proposed dataset, drawing new insightsfor further research and practice. In addition, based on the proposed datasetand tasks, we hosted a competition in the KDD CUP 2023 and have attractedthousands of users and submissions. The winning solutions and the associatedworkshop can be accessed at our website https://kddcup23.github.io/.

Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation