Madrid Live: a context-aware recommender system of leisure plans

Classical recommender systems focus on recommending the most relevant items to users. An active area of research proposes to complete the recommendation process by considering additional contextual information, such as time, location, budget, weather or social position. Researchers and practitioners in different domains have already recognized the great impact of contextual information in decision-making processes. In this paper, we focus on recommenders for tourism and leisure activities where contextual information plays a central role to modify the initial user preferences. We present Madrid Live, a context-aware recommender system (CARS) to recommend leisure activities in Madrid. In Madrid Live, users state their own restrictions and preferences to their plans. The system recommends the set of activities that satisfies these preferences together with the contextual knowledge. The main contributions of our approach are the contextual recommendation and the system explanation interface that allows the user to understand the recommendation process.