Attention Span For Personalisation

2016/07/30 by Hurtado, Joan Figuerola
#FOS: Computer and information sciences #Information Retrieval (cs.IR)

paper · doi:10.48550/arxiv.1608.00147

Abstract

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.

Related

• Retrieval Augmentation via User Interest Clustering
• PinFM: Foundation Model for User Activity Sequences at a Billion-scale Visual Discovery Platform
• AIM: Automatic Interaction Machine for Click-Through Rate Prediction
• CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
• TRACE: Transformer-based user Representations from Attributed Clickstream Event sequences