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As information professionals, we know simple database searches are imperfect. With rich and expansive digital collections, patrons may not find content that is buried in a long list of results. So, how do we improve discovery of pertinent materials and offer serendipitous experience? Following the example of recommendation functionality in online applications like Netflix, we have developed a recommendation function for our digital library system that provides relevant content beyond the narrow scope of patrons' original search parameters. This session will outline the reasoning, methodology, and design of the recommendation system as well as preliminary results from implementation.
Guan, Boyuan and Rogers, Jamie, "Improving Discovery and Patron Experience Through Data Mining" (2017). Works of the FIU Libraries. 64.
Databases and Information Systems Commons, Library and Information Science Commons, Systems Architecture Commons, Theory and Algorithms Commons
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