Multi-Perspective Learning to Rank to Support Children's Information Seeking in the Classroom

Garrett Allen, Katherine Landau Wright, Jerry Alan Fails, Casey Kennington, Maria Soledad Pera

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce a re-ranking model that augments the functionality of standard search engines to aid classroom search activities for children (ages 6-11). This model extends the known listwise learning-to-rank framework by balancing risk and reward. Doing so enables the model to prioritize Web resources of high educational alignment, appropriateness, and adequate readability by analyzing the URLs, snippets, and page titles of Web resources retrieved by a mainstream search engine. Experimental results demonstrate the value of considering multiple perspectives inherent to the classroom when designing algorithms that can better support children's information discovery.

Original languageEnglish
Title of host publicationProceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages311-317
Number of pages7
ISBN (Electronic)9798350309188
DOIs
StatePublished - 2023
Event22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023 - Hybrid, Venice, Italy
Duration: 26 Oct 202329 Oct 2023

Publication series

NameProceedings - 2023 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023

Conference

Conference22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2023
Country/TerritoryItaly
CityHybrid, Venice
Period26/10/2329/10/23

Keywords

  • children's web search
  • ranking

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