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Recommending needles in a haystack: the SURGE approach.

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Title: Recommending needles in a haystack: the SURGE approach.
Authors: Hermoso, Ramon1 (AUTHOR) rhermoso@unizar.es, Ilarri, Sergio2 (AUTHOR), Trillo-Lado, Raquel2 (AUTHOR), Marzo, Cristina2 (AUTHOR)
Source: International Journal of Geographical Information Science. Jul2026, Vol. 40 Issue 7, p2023-2060. 38p.
Subjects: Recommender systems, Semantics methodology, Geographic spatial analysis, Spatial data structures, Tourism
Abstract: Collective Spatial Keyword Querying (CoSKQ) was proposed over a decade ago as a model to retrieve sets of objects in spatial databases given a specific query. The rationale behind is that the retrieved solution sets must cover query keywords as well as minimise the geographic distances between the query and the solution elements. However, in most real scenarios, the exact matching of query keywords and object descriptions is rare or not possible. In this paper, we extend the notion of CoSKQ for recommendation problems and present SURGE (Spatial User Recommendations using Geographical metrics and sEmantics), an approach that puts forward a recommender system able to return solution sets even when query keywords and object descriptions do not match exactly. In order to do that, semantic techniques are used. A tourism domain has been used throughout the paper to explain the model. Furthermore, an exhaustive set of experiments has been carried out to validate the approach. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Jul2026, Vol. 40 Issue 7, p2023-2060. 38p.
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  Data: <searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics+methodology%22">Semantics methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+spatial+analysis%22">Geographic spatial analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+data+structures%22">Spatial data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Tourism%22">Tourism</searchLink>
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  Data: Collective Spatial Keyword Querying (CoSKQ) was proposed over a decade ago as a model to retrieve sets of objects in spatial databases given a specific query. The rationale behind is that the retrieved solution sets must cover query keywords as well as minimise the geographic distances between the query and the solution elements. However, in most real scenarios, the exact matching of query keywords and object descriptions is rare or not possible. In this paper, we extend the notion of CoSKQ for recommendation problems and present SURGE (Spatial User Recommendations using Geographical metrics and sEmantics), an approach that puts forward a recommender system able to return solution sets even when query keywords and object descriptions do not match exactly. In order to do that, semantic techniques are used. A tourism domain has been used throughout the paper to explain the model. Furthermore, an exhaustive set of experiments has been carried out to validate the approach. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13658816.2025.2582692
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      – Code: eng
        Text: English
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        PageCount: 38
        StartPage: 2023
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        Type: general
      – SubjectFull: Semantics methodology
        Type: general
      – SubjectFull: Geographic spatial analysis
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      – SubjectFull: Spatial data structures
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      – SubjectFull: Tourism
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              M: 07
              Text: Jul2026
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              Y: 2026
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