Customized recommendation based on the analysis of passengers' mobility profiles - Mike Santos Pinto (2017)
The usage of public transportation service by the general public has been decreasing in the last few years. This is a matter of big concern as it affects the environment and traffic logistics, so there is a need to find new ways to attract users towards this kind of transportation. Previous studies show that an effective method of promotion is giving users something to look forward to, in this case, a reward for their transportation choice. A promising rewarding process consists is establishing partnerships between transportation services and business services to reward users who opt for public transports with prize offers, i.e. vouchers. With the purpose of rewarding users of public transportation services, this project consists on developing a recommender system. The system is capable of recommending business establishments and prize offers based on the user's mobility and social profile, being the main objective of the project to allow measuring the system's recommendation precision and which method is best. The Mobility Aware Recommender System (MARS) model and previous projects, such as Voyager, are used as starting point for this project. With the integration of social network services, namely Facebook and Foursquare, we aim at creating more detailed and complex user profiles. This allows defining a base profile for each user, increasing the system's efficiency right from the start, as it solves the cold start problem. This solution shows a way to reward the three parties: transportation service entities have a new tool to attract new users, business services have a way of promoting their services to targeted users and passengers are rewarded by the system in a customized way. Beyond the scope of this project are several other areas to explore. With social networks, the integration of achievement system with social sharing is one possibility; integrating new social services such as Twitter and LinkedIn to enrich the recommender system is another. Another extension to the system would be recommending other locations, for tourism, or events, like concerts or shows.