These are the slides of the tutorial Liangjie Hong and I gave at The Web Conference in San Francisco, 2019. Full details of the tutorial and previous instances can be found at https://onlineuserengagement.github.io/.
User engagement plays a central role in companies operating online services, such as search engines, news portals, e commerce sites, entertainment services, and social networks. A main challenge is to leverage collected knowledge about the daily online behavior of millions of users to understand what engage them short-term and more importantly long-term. Two critical steps of improving user engagement are metrics and their optimization. The most common way that engagement is measured is through various online metrics, acting as proxy measures of user engagement. This tutorial will review these metrics, their advantages and drawbacks, and their appropriateness to various types of online services. Once metrics are defined, how to optimize them will become the key issue. We will survey methodologies including machine learning models and experimental designs that are utilized to optimize these metrics via directly or indirect ways. As case studies, we will focus on four types of services, news, search, entertainment, and e-commerce.
We will end with lessons learned and a discussion on the most promising research directions.
Liangjie Hong, Director of Engineering, Data Science and Machine Learning at Etsy Inc.
Mounia Lalmas, Director of Research at Spotify, and Head of Tech Research in Personalization.