Learning user preferences in matching for ridesharing

Sharing car journeys can be very beneficial, since it can save travel costs, as well as reducing traffic congestion and pollution. The process of matching riders and drivers automatically at short notice, is referred to as dynamic ridesharing, which has attracted a lot of attention in recent years. In this paper, amongst the wide range of challenges in dynamic ridesharing, we consider the problem of ride-matching. While existing studies mainly consider fixed assignments of participants in the matching process, our main contribution is focused on the learning of the user preferences regarding the desirability of a choice of matching; this could then form an important component of a system that can generate robust matchings that maintain high user satisfaction, thus encouraging repeat usage of the system. An SVM inspired method is exploited which is able to learn a scoring function from a set of preferences; this function measures the predicted satisfaction degree of the user regarding specific matches. To the best of our knowledge, we are the first to present a model that is able to implicitly learn individual preferences of participants. Our experimental results, which are conducted on a real ridesharing data set, show the effectiveness of our approach.