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Travel Attractions Recommendation with Knowledge Graphs

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Knowledge Engineering and Knowledge Management (EKAW 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10024))

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Abstract

Selecting relevant travel attractions for a given user is a real and important problem from both a traveller’s and a travel supplier’s perspectives. Knowledge graphs have been used to conduct recommendations of music artists, movies and books. In this paper, we identify how knowledge graphs might be efficiently leveraged to recommend travel attractions. We improve two main drawbacks in existing systems where semantic information is exploited: semantic poorness and city-agnostic user profiling strategy. Accordingly, we constructed a rich world scale travel knowledge graph from existing large knowledge graphs namely Geonames, DBpedia and Wikidata. The underlying ontology contains more than 1200 classes to describe attractions. We applied a city-dependent user profiling strategy that makes use of the fine semantics encoded in the constructed graph. Our evaluation on YFCC100M dataset showed that our approach achieves a 5.3 % improvement in terms of F1-score, a 4.3 % improvement in terms of nDCG compared with the state-of-the-art approach.

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Notes

  1. 1.

    https://www.expedia.com/.

  2. 2.

    https://www.tripadvisor.com/.

  3. 3.

    https://www.peek.com/.

  4. 4.

    https://www.musement.com/.

  5. 5.

    https://www.tnooz.com/article/real-time-destination-action-tickets-on-a-mobile-as-you-approach-an-attraction/.

  6. 6.

    https://www.tnooz.com/article/expedia-bets-big-on-tours-and-activities-will-the-industry-win/.

  7. 7.

    http://www.geonames.org/.

  8. 8.

    http://wiki.dbpedia.org/.

  9. 9.

    https://www.wikidata.org/.

  10. 10.

    http://dbpedia.org/resource/Louvre.

  11. 11.

    http://dbpedia.org/ontology/Museum.

  12. 12.

    http://dbpedia.org/page/Category:Museums.

  13. 13.

    https://developer-tripadvisor.com/content-api/business-content/categories-subcategories-and-types/.

  14. 14.

    https://developer-tripadvisor.com/content-api/request-api-access/.

  15. 15.

    https://developer.foursquare.com/categorytree.

  16. 16.

    https://foursquare.com/v/volvo-museum/4b9f9e2df964a520432f37e3.

  17. 17.

    https://www.wikidata.org/wiki/Q3329393.

  18. 18.

    http://lod-cloud.net/.

  19. 19.

    http://wiki.dbpedia.org/Downloads2015-10#dbpedia-ontology.

  20. 20.

    http://neo4j.com/.

  21. 21.

    http://download.geonames.org/export/dump/.

  22. 22.

    https://en.wikipedia.org/wiki/Tf-idf.

  23. 23.

    https://www.wikidata.org/wiki/Q19675.

  24. 24.

    Yahoo Webscope: http://webscope.sandbox.yahoo.com

    .

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Authors and Affiliations

  1. Sépage, 27 rue du Chemin Vert, 75011, Paris, France

    Chun Lu & Milan Stankovic

  2. STIH, Université Paris-Sorbonne, 28 rue Serpente, 75006, Paris, France

    Chun Lu, Philippe Laublet & Milan Stankovic

Authors
  1. Chun Lu
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  2. Philippe Laublet
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  3. Milan Stankovic
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Correspondence to Chun Lu .

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Editors and Affiliations

  1. Linköping University, Linköping, Sweden

    Eva Blomqvist

  2. University of Bologna, Bologna, Italy

    Paolo Ciancarini

  3. University of Bologna, Bologna, Italy

    Francesco Poggi

  4. University of Bologna, Bologna, Italy

    Fabio Vitali

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Lu, C., Laublet, P., Stankovic, M. (2016). Travel Attractions Recommendation with Knowledge Graphs. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10024. Springer, Cham. https://doi.org/10.1007/978-3-319-49004-5_27

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  • DOI: https://doi.org/10.1007/978-3-319-49004-5_27

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