RVTN: Recommender system for vehicle routing in transportation network

The recommender system predicts the future paths based on the vehicle interactions in a vehicular environment. As the data is ubiquitous, we have a number of options available to make a selection for finding the path in the vehicular network. This is the scenario where the recommender system plays its role by narrowing the available options and make the selection process easy and effective. In this paper, we have proposed a sequential recommender system that considers the vehicle’s interactions along with the paths in sequential order and map the sequences dynamically in which the weight-age is given to current preferences and recent choices as explored by the vehicle of the network. A detailed comparison of the proposed sequential recommender system with the commonly used matrix factorization model has also provided. The proposed recommender system is based on sequential dependencies and responds equally well to the vehicle routing problem. The obtained results using vehicle routing dataset reveals that the proposed recommender system is much faster with higher Mean reciprocal rank (MRR) value.