A Scalable Recommendation Engine for New Users and Items

no code implementations • 6 Sep 2022 • Boya Xu, Yiting Deng, Carl Mela

In many digital contexts such as online news and e-tailing with many new users and items, recommendation systems face several challenges: i) how to make initial recommendations to users with little or no response history (i. e., cold-start problem), ii) how to learn user preferences on items (test and learn), and iii) how to scale across many users and items with myriad demographics and attributes.

Collaborative Filtering Recommendation Systems