A Recommendation System for Shared-Use Mobility Service

Nowadays, shared mobility service is a trend in many countries. It tends to grow even more because of its low cost, the mitigation of both traffic and pollution, and due to the spreading of several shared-use mobility applications on mobile devices. As seen in other services, the success is based on the satisfaction level attained by users. Hence, if a ride is shared between people with similar preferences, users will feel more comfortable and safer. However, finding users with similar preferences is still a challenge in shared-use mobility services. In this context, this research shows that using some basic user information, such as gender, age, and relationship, extracted from Online Social Networks(OSN), and also some preferences, it is possible to determine if the user wants to share a vehicle with people with specific characteristics. Thus, as contribution, it is possible to classify users of similar preferences, automatically, to improve their ride experience. The classification was performed through machine learning algorithms, in which a Discriminative Restricted Boltzmann Machine(DRBM) algorithm reaches a correct classified instance of 94.5% and F-Measure of 0.93 for the option of sharing a ride with a person with similar hobby. Then, a Recommendation System(RS) is proposed, which efficiency is compared with a basic RS; they reached a Pearson Correlation Coefficient of 0.96 and 0.79, respectively; highlighting the importance of considering user preferences. Also, it is important to note that this study can be extended for other sharing services.