Learning urban users´ choices to improve trip recommendations

We analyze the work of urban trip planners and the relevance of trips they recommend upon user queries. We propose to improve the planner recommendations by learning from choices made by travelers who use the transportation network on the daily basis. We analyze a large collection of individual travelers´ trips collected from the automated fare collection systems; we convert the trips into pair-wise preferences for traveling from a given origin to a destination at a given time point. We model passenger preferences with a number of smoothed time-dependent latent variables which are used to learn a ranking function for trips. This function can be used to re-rank the top planner´s recommendations. Results of tests for cities of Nancy, France and Adelaide, Australia show a considerable increase of the recommendation relevance.