PathRec: Visual Analysis of Travel Route Recommendations
July 06, 2017 Β· Declared Dead Β· π ACM Conference on Recommender Systems
"No code URL or promise found in abstract"
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Authors
Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy
arXiv ID
1707.01627
Category
cs.HC: Human-Computer Interaction
Citations
11
Venue
ACM Conference on Recommender Systems
Last Checked
4 months ago
Abstract
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.
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