We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recommendation problem. The system starts from a map-based overview, taking an interactive query as starting point. It then breaks down contributions from different geographical and user behavior features, and those from individual points-of-interest versus pairs of consecutive points on a route. The system also supports detailed quantitative interrogation by comparing a large number of features for multiple points. Effective trajectory visualisations can potentially benefit a large cohort of online map users and assist their decision-making. More broadly, the design of this system can inform visualisations of other structured prediction tasks, such as for sequences or trees.
@article{arxiv.1707.01627,
title = {PathRec: Visual Analysis of Travel Route Recommendations},
author = {Dawei Chen and Dongwoo Kim and Lexing Xie and Minjeong Shin and Aditya Krishna Menon and Cheng Soon Ong and Iman Avazpour and John Grundy},
journal= {arXiv preprint arXiv:1707.01627},
year = {2017}
}