A Machine-Learned Ranking Algorithm for Dynamic and Personalised Car Pooling Services
This work addresses the challenge of improving match success rates in car pooling services by incorporating non-monetary and social factors, which is incremental as it applies existing learning-to-rank methods to a specific domain.
The authors tackled the problem of predicting user preferences in car pooling by proposing GoTogether, a recommender system that uses learning-to-rank techniques to personalize ride recommendations based on user choice history, achieving accurate predictions in both static and dynamic conditions.
Car pooling is expected to significantly help in reducing traffic congestion and pollution in cities by enabling drivers to share their cars with travellers with similar itineraries and time schedules. A number of car pooling matching services have been designed in order to efficiently find successful ride matches in a given pool of drivers and potential passengers. However, it is now recognised that many non-monetary aspects and social considerations, besides simple mobility needs, may influence the individual willingness of sharing a ride, which are difficult to predict. To address this problem, in this study we propose GoTogether, a recommender system for car pooling services that leverages on learning-to-rank techniques to automatically derive the personalised ranking model of each user from the history of her choices (i.e., the type of accepted or rejected shared rides). Then, GoTogether builds the list of recommended rides in order to maximise the success rate of the offered matches. To test the performance of our scheme we use real data from Twitter and Foursquare sources in order to generate a dataset of plausible mobility patterns and ride requests in a metropolitan area. The results show that the proposed solution quickly obtain an accurate prediction of the personalised user's choice model both in static and dynamic conditions.