Preference Learning in Recommender Systems

As proved by the continuous growth of the number of websites which embody recommender systems as a way of personalizing theexperience of users with their content, recommender systems representone of the most popular applications of principles and techniques com-ing from Information Filtering (IF). As IF techniques usually perform aprogressive removal of non-relevant content according to the informationstored in a user profile, recommendation algorithms process informationabout user interests - acquired in an explicit (e.g., letting users expresstheir opinion about items) or implicit (e.g., studying some behavioralfeatures) way - and exploit these data to generate a list of recommendeditems. Although each type of filtering method has its own weaknessesand strengths, preference handling is one of the core issues in the designof every recommender system: since these systems aim to guide users in apersonalized way to interesting or useful objects in a large space of possi-ble options, it is important for them to accurately catch and model userpreferences. The paper provides a general overview of the approaches tolearning preference models in the context of recommender systems