PathRec: visual analysis of travel route recommendations

Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the Eleventh ACM Conference on Recommender Systems
EditorsShlomo Berkovsky, Alexander Tuzhilin
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages364-365
Number of pages2
ISBN (Electronic)9781450346528
DOIs
Publication statusPublished - 2017
Externally publishedYes
EventACM International Conference on Recommender Systems 2017 - Como, Italy
Duration: 27 Aug 201731 Aug 2017
Conference number: 11th
https://recsys.acm.org/recsys17/

Conference

ConferenceACM International Conference on Recommender Systems 2017
Abbreviated titleRecSys 2017
Country/TerritoryItaly
CityComo
Period27/08/1731/08/17
Internet address

Keywords

  • Learning to rank
  • Route visualization
  • Travel recommendation

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