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International Conference on Database and Expert Systems Applications

DEXA 2017: Database and Expert Systems Applications pp 325–339Cite as

Recommending Diverse and Personalized Travel Packages

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10439))

Abstract

The success of recommender systems has made them the focus of a massive research effort in both industry and academia. The aim of most recommender systems is to identify a ranked list of items that are likely to be of interest to users. However, there are several applications such as trip planning, where users are interested in package recommendations as collections of items. In this paper, we consider the problem of recommending the top-k packages to the active user, where each package is constituted with a set of points of interest (POIs) and is under user-specified constraints (time, price, etc.). We formally define the problem of top-k composite recommendations and present our approach which is inspired from composite retrieval. We introduce a scoring function and propose a ranking algorithm for solving the top-k packages problem, taking into account the preferences of the user, the diversity of recommended packages as well as the popularity of POIs. An experimental evaluation of our proposal, using data crawled from Tripadvisor demonstrates its quality and its ability to provide relevant and diverse package recommendations.

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Notes

  1. 1.

    http://www.yourtour.com/.

  2. 2.

    https://www.nileguide.com/.

References

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Author information

Authors and Affiliations

  1. Sorbonne Universités, Université de Technologie de Compiègne, CNRS, Heudiasyc UMR 7253, Compiègne, France

    Idir Benouaret & Dominique Lenne

Authors
  1. Idir Benouaret
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  2. Dominique Lenne
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Corresponding authors

Correspondence to Idir Benouaret or Dominique Lenne .

Editor information

Editors and Affiliations

  1. University of Lyon, Villeurbanne, France

    Djamal Benslimane

  2. University of Milan, Milan, Italy

    Ernesto Damiani

  3. University of Michigan, Dearborn, Michigan, USA

    William I. Grosky

  4. Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  5. Wright State University, Dayton, Ohio, USA

    Amit Sheth

  6. Johannes Kepler University, Linz, Austria

    Roland R. Wagner

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© 2017 Springer International Publishing AG

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Benouaret, I., Lenne, D. (2017). Recommending Diverse and Personalized Travel Packages. In: Benslimane, D., Damiani, E., Grosky, W., Hameurlain, A., Sheth, A., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2017. Lecture Notes in Computer Science(), vol 10439. Springer, Cham. https://doi.org/10.1007/978-3-319-64471-4_26

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  • DOI: https://doi.org/10.1007/978-3-319-64471-4_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64470-7

  • Online ISBN: 978-3-319-64471-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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