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Interaction Based Feature Extraction: How to Convert Your Users’ Activity into Valuable Features

889 views · Published 10 October 2018 · 29:47 · Indexed 27 September 2026

Channel: Databricks · 2018 · Science & Technology

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.

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Interaction Based Feature Extraction: How to Convert Your Users’ Activity into Valuable Features

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