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Challenges in User Modeling and Personalization

Personalization has a long history, dating back to the “master-apprentice” approach of individual tutoring that sought to pass on knowledge and skills from one generation to the next. Through user modeling and adaptation, we try to capture the tutor’s human intelligence and turn it into artificial intelligence. Over the last decades, this research has evolved from an expert-driven approach toward a data-driven approach. This evolution comes with an interesting challenge: How can we continue to understand what an automated tutor is doing when the process of collecting and interpreting data about users is fully automated and the adaptation and recommendation decisions are “deduced” from individual users’ behavior as well as the behavior of all users combined? This article discusses the challenges of scrutability, repeatability, and meta-adaptation (aka adaptation of the adaptation), important research issues for the coming years.