@inproceedings{62e0c1e28e0c408db4112bc610c42b5f,
title = "TechLens - A researcher's desktop",
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.",
keywords = "Digital libraries, Recommenders, Resolvability",
author = "Nishikant Kapoor and Juin Chen and Butler, {John T.} and Fouty, {Gary C.} and Stemper, {James A} and John Riedl and Konstan, {Joseph A.}",
year = "2007",
doi = "10.1145/1297231.1297268",
language = "English (US)",
isbn = "9781595937308",
series = "RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems",
pages = "183--184",
booktitle = "RecSys'07",
note = "RecSys'07: 2007 1st ACM Conference on Recommender Systems ; Conference date: 19-10-2007 Through 20-10-2007",
}