Nowadays, most recommender systems provide recommendations
by either exploiting feedback given by similar users, referred to as
collaborative filtering, or by identifying items with similar properties,
referred to as content-based recommendation. Focusing on the
latter, this keynote presents various examples and case studies that
illustrate both strengths and weaknesses of content-based recommendatio
