Recommender systems are one of the most visible and successful applications of AI/ Machine Learning today. They are not only helpful for end users to discover things they might be interested in, they can also lead to a substantial value for business, e.g., by stimulating more sales or by increasing customer engagement. In this talk, we will first discuss the various types of value recommenders can have for different stakeholders in practice. We will review case studies of recommender systems deployments and discuss how their effects can be measured. We will then move on to the academic perspective, where we describe common problem abstractions, the main technical approaches, as well as evaluation procedures. The talk ends with a discussion of open challenges in our field and an outlook on possible future directions.

Short Bio

Dietmar Jannach is a full professor of Information Systems at AAU Klagenfurt, Austria. Before joining AAU in 2017, he was a full professor of Computer Science at TU Dortmund, Germany. In his research, he focuses on the application of intelligent system technology to practical problems and the development of methods for building knowledge-intensive software applications. In the last years, Dietmar Jannach worked on various practical aspects of recommender systems. He is the main author of the first textbook on the topic published by Cambridge University Press in 2010 and was the co-founder of a tech startup that created an award-winning product for interactive advisory solutions.