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

With information systems becoming larger scale, recommendation systems are atopic of growing interest in machine learning research and industry. Eventhough progress on improving model design has been rapid in research, we arguethat many advances fail to translate into practice because of two limitingassumptions. First, most approaches focus on a transductive learning settingwhich cannot handle unseen users or items and second, many existing methods aredeveloped for static settings that cannot incorporate new data as it becomesavailable. We argue that these are largely impractical assumptions onreal-world platforms where new user interactions happen in real time. In thissurvey paper, we formalize both concepts and contextualize recommender systemswork from the last six years. We then discuss why and how future work shouldmove towards inductive learning and incremental updates for recommendationmodel design and evaluation. In addition, we present best practices andfundamental open challenges for future research.

Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates