Personalization of Routing Services: User Classification as a Method to Overcome the Cold Start Problem for Personalized Models

Abstract

Mobile technologies are improving constantly, and there are plenty of online routing services around. Although users are willing to try new services and apps, it is hard to retain them as users unless the service meets their expectations straightaway. In multimodal routing, using a generic model might not give appropriate routes to both an environmentally conscious traveler who uses mostly public transport and a car enthusiast. Following a trend in online retailing, in which personalized services are already the norm, research in the personalization of online routing services has shown promising results. However, because users tend to use a new service only a few times if the results are not satisfying right from the start, the initialization of the personalized model is of core importance. The authors propose a methodology that is based on a small initial data collection within the service and combined with two models that classify users into classes, first a latent class model and second a mixed logit approach with a prior clustering step. The authors tested the modeling approaches for their performance in the initialization stage and found that the classification approach can improve the correct detection of users’ chosen routes compared with state-of-the-art methodologies by several percentage points. Furthermore, user reactions to personalized routes included in a routing application were collected. The results show that the personalization of routes is well received by the users.