Creating a model for providing personal travel recommendations : Predicting the destination from the commuter search history

Abstract: Following the recent surge of implementations of various recommender systems, this study applied the technique of collaborative filtering to the area of commuting in the system of public transportation. This study processed and analyzed commuter search patterns collected from the travel planning mobile application MobiTime. The purpose of this study was to see how collaborative filtering can be applied on search history to predict the commuter non-routine destination. By creating and providing a model with varying sizes of routine routes (history from the commuters), the accuracy of the model was measured based on that history. The study found that the accuracy is varying with the history provided by the commuter and that the method described in the study outperformed the baseline. The result of this study could make the commuting experience more efficient.