View article

As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF). As IF techniques usually perform a progressive removal of non-relevant content according to the information stored in a user profile, recommendation algorithms process information about user interests-acquired in an explicit (eg, letting users express their opinion about items) or implicit (eg, studying some behavioral features) way-and exploit these data to generate a list of recommended items. Although each type of filtering method has its own weaknesses and strengths, preference handling is one of the core issues in the design of every recommender system: since these systems aim to guide users in a personalized way to interesting or useful objects in a large space of possible options, it is important for them to accurately catch and model user preferences. The paper provides a general overview of the approaches to learning preference models in the context of recommender systems.