IDEAS home Printed from https://ideas.repec.org/a/spr/jcsosc/v8y2025i4d10.1007_s42001-025-00425-4.html
   My bibliography  Save this article

Improving homeless service assignment outcomes

Author

Listed:
  • Khandker Sadia Rahman

    (University at Albany, SUNY)

  • Charalampos Chelmis

    (University at Albany, SUNY)

Abstract

Over the years, homeless service providers have offered services to assist homeless individuals. Administrative data recorded by service providers have been previously used to infer the underlying network of homeless services that individuals navigate with the goal of securing stable housing. Methods have been developed to recommend service assignments based on this network so that the dynamics of the existing system can be replicated. In contrast to prior art, which neglects the real-life impact of service assignments to individuals, we propose a method that, given the history of individuals in the homeless system, recommends the next service assignment that is expected to best improve the exit and reentry outcomes for each individual. To the best of our knowledge, this is the first time that exit and non-reentry outcomes are considered in algorithmic recommendation of homeless service assignments. Extensive experimental evaluation shows that the proposed method significantly outperforms the state of the art.

Suggested Citation

  • Khandker Sadia Rahman & Charalampos Chelmis, 2025. "Improving homeless service assignment outcomes," Journal of Computational Social Science, Springer, vol. 8(4), pages 1-23, November.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:4:d:10.1007_s42001-025-00425-4
    DOI: 10.1007/s42001-025-00425-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s42001-025-00425-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s42001-025-00425-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:v:8:y:2025:i:4:d:10.1007_s42001-025-00425-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.