USER: user-sensitive expert recommendations for knowledge-dense environments

Authors:
Colin DeLong;Prasanna Desikan;Jaideep Srivastava
Affiliations:
College of Liberal Arts, University of Minnesota, Minneapolis, MN, United States of America;Department of Computer Science, University of Minnesota, Minneapolis, MN, United States of America;Department of Computer Science, University of Minnesota, Minneapolis, MN, United States of America
Venue:
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Year:
2005

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Abstract

Traditional recommender systems tend to focus on e-commerce applications, recommending products to users from a large catalog of available items. The goal has been to increase sales by tapping into the user's interests by utilizing information from various data sources to make relevant recommendations. Education, government, and policy websites face parallel challenges, except the product is information and their users may not be aware of what is relevant and what isn't. Given a large, knowledge-dense website and a nonexpert user searching for information, making relevant recommendations becomes a significant challenge. This paper addresses the problem of providing recommendations to non-experts, helping them understand what they need to know, as opposed to what is popular among other users. The approach is usersensitive in that it adopts a ‘model of learning' whereby the user's context is dynamically interpreted as they browse and then leveraging that information to improve our recommendations.