Talk by: Shlomi Babluki
Today almost every website and app collect data about the interactions (clicks, likes, views…) between users and items. The most common use case for these sparse “user-item” matrices is to train and improve different recommendation systems. In my presentation I will introduce how we can use exactly the same matrices together with additional datasets to generate valuable features that can be used to train different regression and classification models.
I will start with describing how it was implemented at SimilarWeb, in order to accurately estimate different website metrics like demographics (age and gender) and category and continue with explaining how we can expand the algorithm to solve similar problems in different domains.
About: Databricks provides a unified data analytics platform, powered by Apache Spark™, that accelerates innovation by unifying data science, engineering and business.
Read more here: [ Ссылка ]
Connect with us:
Website: [ Ссылка ]
Facebook: [ Ссылка ]
Twitter: [ Ссылка ]
LinkedIn: [ Ссылка ]
Instagram: [ Ссылка ] Databricks is proud to announce that Gartner has named us a Leader in both the 2021 Magic Quadrant for Cloud Database Management Systems and the 2021 Magic Quadrant for Data Science and Machine Learning Platforms. Download the reports here. [ Ссылка ]
