Exploring a large-scale multi-modal transportation recommendation system

The emergence of navigation applications with multi-modal trip planning services has brought about the demand for the multi-modal transportation recommendation systems. In this paper, we explore the problem of large-scale multi-modal transportation recommendation and propose a novel travel mode recommendation system for a multi-modal transportation system. In the proposed model, the feature engineering focuses on the application scenario of the multi-modal transportation recommendation, and is designed from multiple perspectives of users, travel modes, locations, and time. To learn a better representation of the co-occurrence, we construct a bipartite graph for the Origin-Destination (OD) pair and the User-OD pair of all the query records then transformed nodes in the bipartite graph to feature vectors using a graph-embedding technique. Finally, we propose a post-processing technique to handle the inconsistency between the objective function and evaluation metric. Experimental results from a city-wide multi-modal transportation recommendation indicate that our proposed model is superior to the existing method of navigation service providers.