Keynote: Computing useful recommendations: still requires knowledge


Recommender systems have been introduced as information search and filtering tools for providing suggestions for items to be of use to a user. State of the art recommender systems mostly focus on the usage of data mining and information retrieval techniques to predict to what extent an item fits user needs and wants. But often they end up in making uninteresting suggestions especially in complex domains, such as tourism. In this talk, classical recommender systems ideas will be introduced and critically scrutinised in the attempt to better understand the role of observed and predicted choices and preferences. We will discuss some of the key ingredients necessary to build a useful recommender system. Hence, we will point out some limitations and open challenges for recommender systems research.
Chair: Rosina Weber
Speaker: Francesco Ricci

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