Solving the data sparsity problem in destination prediction

摘要

This paper was first presented in 2013 at the International Conference on Data Engineering (ICDE) in Brisbane; its core ideas are still available as a slide presentation online. Since then, work has gone on following the continuing interest of the Melbourne School of Information in the research area of destination prediction, or the statistical guess of successive locations along the course of a trip. This, beyond the obvious academic interest, is important in many real-world situations, among others the location-based applications currently popular on portable devices. The field is not new, of course, but existing methods rely on proprietary data sources, are difficult and costly to obtain, or depend on series of historical data, which can be severely incomplete.