Abstract. On-line recommender systems are designed to address a number of different recommendation scenarios in which traditional
systems fail primarily, but not only, due to scalability issues.
The goal of this talk is to give participants an overview on the design requirements for on-line recommender systems, with a focus on their quality evaluation, and to provide pragmatic guidelines to perform these activities more effectively avoiding commons pitfalls.
The talk is structured into two parts. In the first part, after a general overview of on-line recommender systems, we will analyze different application scenarios. In the second part we will analyze possible functional and non-functional evaluation problems. We will present some of our works on evaluating presentation biases, problems which affect click-based on-line recommender systems. We will later present some of our recent work towards comparing the scalability of different Top-N recommender systems, understanding their fundamental limitations and characteristics for some of the application scenarios identified in the first part.
Paolo Cremonesi is professor of Recommender Systems at the Computer Science Department of Politecnico di Milano. Paolo is also the co-founder of Moviri, the first and most successful start-up from Politecnico di Milano, now a holding company with more than 200 employees and offices in Europe, US and Asia. Paolo holds an MSc in Aerospace Engineering and a PhD in Computer Science. His research interests include high-performance computing and recommender systems. He has published more than 200 papers in refereed journals, conferences and book chapters, and holds 5 patents.