Recommender Systems: from Preference Learning to Useful Recommendations
Francesco Ricci
Free University of Bozen-Bolzano
Recommender systems are information search and filtering tools that provide suggestions for items to be of use to a user. They have become common in a large number of Internet applications (YouTube, Amazon, LinkedIn), helping users to make better choices while browsing and searching for news, music, vacations, or financial investments. State of the art recommender systems exploit data mining and information retrieval techniques to predict to what extent an item fits the user needs and wants, but often they end up in making obvious and uninteresting suggestions. In the talk, recommender systems ideas and techniques will be introduced and criticised. We will illustrate the fundamental steps that are required to build and use a recommender system and its underlying assumptions. But, we will also discuss some limitations and open challenges for recommender systems research, such as preference modelling, choice modelling and the dynamics of the data generated and consumed by recommender systems. [Slides available online]