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This poster presents our preliminary research on how information can be extracted from user browsing behavior to identify understudied works that are relevant but have too few viewers. We investigate how to apply two types of analysis—a formula called Effective Collection Size and ‘multi-armed bandit’ analysis—to extracted user data to develop alternative methods of retrieving materials from collection that are collated by richer factors of relevancy. We anticipate that these analyses will enable the development of an information retrieval system that presents a broad range of content in a user’s search results.
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