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Constructing travel itineraries from tagged geo-temporal breadcrumbs

Published: 26 April 2010 Publication History

Abstract

Vacation planning is a frequent laborious task which requires skilled interaction with a multitude of resources. This paper develops an end-to-end approach for constructing intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. In particular, the popular rich media sharing site, Flickr, allows photos to be stamped by the date and time of when they were taken, and be mapped to Points Of Interest (POIs) by latitude-longitude information as well as semantic metadata (e.g., tags) that describe them.
Our extensive user study on a "crowd-sourcing" marketplace (Amazon Mechanical Turk), indicates that high quality itineraries can be automatically constructed from Flickr data, when compared against popular professionally generated bus tours.

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  • (2024)Including the Temporal Dimension in the Generation of Personalized Itinerary RecommendationsIEEE Access10.1109/ACCESS.2024.344171012(112794-112809)Online publication date: 2024
  • (2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
  • (2023)How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young TravellersIEEE Access10.1109/ACCESS.2023.328525811(61968-61985)Online publication date: 2023
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  1. Constructing travel itineraries from tagged geo-temporal breadcrumbs

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      cover image ACM Other conferences
      WWW '10: Proceedings of the 19th international conference on World wide web
      April 2010
      1407 pages
      ISBN:9781605587998
      DOI:10.1145/1772690

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 April 2010

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      Author Tags

      1. flickr
      2. geo-tags
      3. mechanical turk
      4. media applications
      5. orienteering problem
      6. rich media
      7. travel itinerary

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      WWW '10
      WWW '10: The 19th International World Wide Web Conference
      April 26 - 30, 2010
      North Carolina, Raleigh, USA

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      Cited By

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      • (2024)Including the Temporal Dimension in the Generation of Personalized Itinerary RecommendationsIEEE Access10.1109/ACCESS.2024.344171012(112794-112809)Online publication date: 2024
      • (2024)A survey on personalized itinerary recommendation: From optimisation to deep learningApplied Soft Computing10.1016/j.asoc.2023.111200152(111200)Online publication date: Feb-2024
      • (2023)How Personality Traits can be Used to Shape Itinerary Factors in Recommender Systems for Young TravellersIEEE Access10.1109/ACCESS.2023.328525811(61968-61985)Online publication date: 2023
      • (2023)GC-TripRec: Graph contextualized generative network with adversarial learning for trip recommendationWorld Wide Web10.1007/s11280-022-01127-x26:5(2291-2310)Online publication date: 13-Feb-2023
      • (2023)Combining Genetic Algorithms and Temporal Constraint Satisfaction for Recommending Personalized Tourist ItinerariesAIxIA 2023 – Advances in Artificial Intelligence10.1007/978-3-031-47546-7_30(441-452)Online publication date: 2-Nov-2023
      • (2022)Efficient itinerary recommendation via personalized POI selection and pruningKnowledge and Information Systems10.1007/s10115-021-01648-364:4(963-993)Online publication date: 2-Mar-2022
      • (2021)Contrastive Trajectory Learning for Tour RecommendationACM Transactions on Intelligent Systems and Technology10.1145/346233113:1(1-25)Online publication date: 29-Nov-2021
      • (2020)Timeliness-Aware On-Site Planning Method for Tour NavigationSmart Cities10.3390/smartcities30400663:4(1383-1404)Online publication date: 21-Nov-2020
      • (2020)Smartphones for public transport planning and recommendation in developing countries—A reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.139711:2Online publication date: 13-Nov-2020
      • (2019)Sentiment-Aware and Personalized Tour Recommendation2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006442(900-909)Online publication date: Dec-2019
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