Contextual Information Elicitation in Travel Recommender Systems

Context-Aware Recommender Systems are advisory applications that exploit users’ preference knowledge contained in datasets of context-dependent user ratings, i.e., ratings augmented with the description of the contextual situation detected when the user experienced the item and rated it. Since the space of context-dependent ratings increases exponentially in size with the number of contextual factors, and because certain contextual information is still hard to acquire automatically (e.g., the user’s mood or the travellers’ group composition), it is fundamental to identify and acquire only those factors that truly influence the user preferences and consequently the ratings and the recommendations. In this paper, we propose a novel method that estimates the impact of a contextual factor on rating predictions and adaptively elicits from the users only the relevant ones. Our experimental evaluation, on two travel-related datasets, shows that our method compares favorably to other state-of-the-art context selection methods.