USER: User-Sensitive Expert Recommendations for Knowledge-Dense Environments

  • Colin DeLong
  • Prasanna Desikan
  • Jaideep Srivastava
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4198)


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.


Recommender System Collaborative Filter Concept Graph Content Management System Anchor Text 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Colin DeLong
    • 1
  • Prasanna Desikan
    • 2
  • Jaideep Srivastava
    • 2
  1. 1.College of Liberal ArtsUniversity of MinnesotaMinneapolisUnited States of America
  2. 2.Department of Computer ScienceUniversity of MinnesotaMinneapolisUnited States of America

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