Computer Science > Information Retrieval
[Submitted on 29 Aug 2023]
Title:A Multi-Perspective Learning to Rank Approach to Support Children's Information Seeking in the Classroom
View PDFAbstract:We introduce a novel re-ranking model that aims to augment the functionality of standard search engines to support classroom search activities for children (ages 6 to 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 given mainstream search engine. Experimental results, including an ablation study and comparisons with existing baselines, showcase the correctness of the proposed model. The outcomes of this work demonstrate the value of considering multiple perspectives inherent to the classroom setting, e.g., educational alignment, readability, and objectionability, when applied to the design of algorithms that can better support children's information discovery.
Submission history
From: Maria Soledad Pera [view email][v1] Tue, 29 Aug 2023 12:50:21 UTC (107 KB)
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