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International Conference on Web Engineering

ICWE 2021: Web Engineering pp 175–182Cite as

A Probabilistic Approach to Personalize Type-Based Facet Ranking for POI Suggestion

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12706))

Abstract

Faceted Search Systems (FSS) have become one of the main search interfaces used in vertical search systems, offering users meaningful facets to refine their search query and narrow down the results quickly to find the intended search target. This work focuses on the problem of ranking type-based facets. In a structured information space, type-based facets (t-facets) indicate the category to which each object belongs. When they belong to a large multi-level taxonomy, it is desirable to rank them separately before ranking other facet groups. This helps the searcher in filtering the results according to their type first. This also makes it easier to rank the rest of the facets once the type of the intended search target is selected. Existing research employs the same ranking methods for different facet groups. In this research, we propose a two-step approach to personalize t-facet ranking. The first step assigns a relevance score to each individual leaf-node t-facet. The score is generated using probabilistic models and it reflects t-facet relevance to the query and the user profile. In the second step, this score is used to re-order and select the sub-tree to present to the user. We investigate the usefulness of the proposed method to a Point Of Interest (POI) suggestion task. Our evaluation aims at capturing the user effort required to fulfil her search needs by using the ranked facets. The proposed approach achieved better results than other existing personalized baselines.

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Notes

  1. 1.

    In the scope of this paper, we refer to resources (or information objects) being searched as venues or POIs.

  2. 2.

    How the venue ranking is performed is outside scope of this research.

  3. 3.

    Although we acknowledge that other HCI factors may influence the decision of what portion of the tree should be displayed to the users, in this work we focus on studying how the tree building approach affects the user from a pure metric perspective.

  4. 4.

    https://developer.foursquare.com/docs/resources/categories, version: 20180323.

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Acknowledgements

This research was conducted with the financial support of Science Foundation Ireland (SFI) under Grant Agreement No. 13/RC/2106 at the ADAPT SFI Research Centre at Trinity College Dublin. The ADAPT Centre for Digital Media Technology is funded by SFI through the SFI Research Centres Programme and is co-funded under the European Regional Development Fund (ERDF) Grant No. 13/RC/2106_P2.

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Authors and Affiliations

  1. ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

    Esraa Ali, Séamus Lawless & Owen Conlan

  2. ADAPT Centre, School of Computing, Dublin City University, Dublin, Ireland

    Annalina Caputo

Authors
  1. Esraa Ali
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  2. Annalina Caputo
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  3. Séamus Lawless
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  4. Owen Conlan
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Corresponding authors

Correspondence to Esraa Ali or Annalina Caputo .

Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy

    Marco Brambilla

  2. E2S UPPA, LIUPPA, Université de Pau et des Pays de l’Adour, Anglet, France

    Richard Chbeir

  3. Econometric Institute, Erasmus University Rotterdam, Rotterdam, The Netherlands

    Flavius Frasincar

  4. Inria Saclay-Île-de-France, Institut Polytechnique de Paris, Palaiseau, France

    Ioana Manolescu

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Ali, E., Caputo, A., Lawless, S., Conlan, O. (2021). A Probabilistic Approach to Personalize Type-Based Facet Ranking for POI Suggestion. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-74296-6_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74295-9

  • Online ISBN: 978-3-030-74296-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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