Techlens: a researcher's desktop

Authors:
Nishikant Kapoor;Jilin Chen;John T. Butler;Gary C. Fouty;James A. Stemper;John Riedl;Joseph A. Konstan
Affiliations:
University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN;University of Minnesota, Minneapolis, MN
Venue:
Proceedings of the 2007 ACM conference on Recommender systems
Year:
2007

Citing 3
Cited 1

On the recommending of citations for research papers

CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Enhancing digital libraries with TechLens+

Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
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ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries

Recommender systems: from algorithms to user experience

User Modeling and User-Adapted Interaction

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Abstract

Rapid and continuous growth of digital libraries, coupled with brisk advancements in technology, has driven users to seek tools and services that are not only customized to their specific needs, but are also helpful in keeping them stay abreast with the latest developments in their field. TechLens is a recommender system that learns about its users through implicit feedback, builds correlations among them, and uses that information to generate recommendations that match the user's profile. It gives users control over which parts of their profile of known citations are used in forming recommendations for new articles. This demonstration is a prototype that showcases some of the tools and services that TechLens offers to the users of digital libraries.