A Route Recommender System Based on Current and Historical Crowdsourcing

Abstract

Due to the rise of the social networks it's possible to use techniques based on crowdsourcing to easily gather real-time information directly from citizens in order to create recommendation systems capable to employ knowledge that is shared from the crowd. Particularly, in Twitter, the users publish a big amount of short messages; however, to automatically extract useful information from Twitter is a complex task. In order to provide an informed recommendation of the current best route between two city points, this chapter introduces a workflow that integrates natural language techniques to build an vector of features for training two linear classifiers which obtain current information from Twitter, and integrates that information with historical information about possible routes using exponential smoothing; current and historical data to feed a route selection algorithm based on Dijkstra. The effectiveness of the proposed workflow is shown with routes between two interest points in Caracas (Venezuela).