Attention Span For Personalisation
This work addresses the need for more effective personalization in recommender systems by leveraging richer user engagement data, though it appears incremental as it builds on existing event-based methods.
The paper tackled the problem of limited user engagement data in recommender systems by introducing a framework to collect and store event stream data and extract features like attention span, which resulted in a 340% higher click-through-rate compared to using clicks alone.
A click on an item is arguably the most widely used feature in recommender systems. However, a click is one out of 174 events a browser can trigger. This paper presents a framework to effectively collect and store data from event streams. A set of mining methods is provided to extract user engagement features such as: attention span, scrolling depth and visible impressions. In this work, we present an experiment where recommendations based on attention span drove 340% higher click-through-rate than clicks.