Real-time user modeling and prediction: examples from youtube

ABSTRACT

Real-time analysis and modeling of users for improving engagement, and interaction is a burgeoning area of interest with applications to web sites, social networks and mobile applications. Apart from scalability issues, this domain poses a number of modeling and algorithmic challenges. In this talk, as an illustrative example, we present DAL, a system that leverages real-time user activity/signals for dynamic ad loads, and designed to improve the overall user experience on YouTube. This system uses machine learning to optimize for user activity during a visit and helps decide on real-time advertising policies dynamically for the user. We conclude the talk with challenges and opportunities in this important area of real-time user analysis and social modeling.

Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

    • Machine learning approaches

      • Markov decision processes

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory

      • Markov decision processes