Learning User Preferences by Observing User-Items Interactions in an IoT Augmented Space
David Massimo, Mehdi Elahi, Francesco Ricci⋆ · 2017
Recommender systems generate recommendations by analysing which items the user consumes or likes. Moreover, in many scenarios, e.g., when a user is visiting an exhibition or a city, users are faced with a sequence of decisions, and the recommender should therefore suggest, at each decision step, a set of viable recommendations (attractions). In these scenarios the order and the context of the past user choices is a valuable source of data, and the recommender has to effectively exploit this information for understanding the user preferences in order to recommend compelling items.