Enhancing Digital Libraries with TechLens+

@article{citeulike:375907, abstract = {The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102,000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently. These results can be applied to develop recommender systems for other types of digital libraries.}, address = {Tuscon, AZ}, author = {Torres, Roberto and Mcnee, Sean M. and Abel, Mara and Konstan, Joseph A. and Riedl, John }, citeulike-article-id = {375907}, journal = {International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries}, keywords = {collaborative\_filtering, content\_based\_filtering, digital\_library, hybrid\_recommender\_systems, papers, publications, recommender\_systems}, pages = {228--236}, posted-at = {2006-06-15 20:22:03}, priority = {0}, title = {Enhancing Digital Libraries with TechLens+}, url = {http://delivery.acm.org/10.1145/1000000/996402/p228-torres.pdf?key1=996402\&\#38;key2=4160880311\&\#38;coll=GUIDE\&\#38;dl=GUIDE\&\#38;CFID=57065195\&\#38;CFTOKEN=9858647}, year = {2004} }

TY - JOUR ID - citeulike:375907 L3 - citeulike-article-id:375907 N2 - The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102,000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently. These results can be applied to develop recommender systems for other types of digital libraries. JF - International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries AD - Tuscon, AZ EP - 236 TI - Enhancing Digital Libraries with TechLens+ SP - 228 KW - collaborative_filtering KW - content_based_filtering KW - digital_library KW - hybrid_recommender_systems KW - papers KW - publications KW - recommender_systems AU - Torres, Roberto AU - Mcnee, Sean AU - Abel, Mara AU - Konstan, Joseph AU - Riedl, John PY - 2004/// UR - http://delivery.acm.org/10.1145/1000000/996402/p228-torres.pdf?key1=996402&key2=4160880311&coll=GUIDE&dl=GUIDE&CFID=57065195&CFTOKEN=9858647 ER -