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. 2024. “Multi-Perspective Learning to Rank to Support Children’s Information Seeking in the Classroom”. In IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, pp. 311-317. DOI:10.1109/WI-IAT59888.2023.00050
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.