TechLens - A researcher's desktop

Nishikant Kapoor, Juin Chen, John T. Butler, Gary C. Fouty, James A Stemper, John Riedl, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationRecSys'07
Subtitle of host publicationProceedings of the 2007 ACM Conference on Recommender Systems
Number of pages2
StatePublished - Dec 1 2007
EventRecSys'07: 2007 1st ACM Conference on Recommender Systems - Minneapolis, MN, United States
Duration: Oct 19 2007Oct 20 2007

Publication series

NameRecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems


OtherRecSys'07: 2007 1st ACM Conference on Recommender Systems
Country/TerritoryUnited States
CityMinneapolis, MN


  • Digital libraries
  • Recommenders
  • Resolvability


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