Digital Libraries, Joint Conference on (2004)
Tuscon, AZ, USA
June 7, 2004 to June 11, 2004
Roberto Torres , Universidade Federal do Rio Grande do Sul, Brazil; University of Minnesota, Minneapolis, MN
Sean M. McNee , University of Minnesota, Minneapolis, MN
Mara Abel , Universidade Federal do Rio Grande do Sul, Brazil
Joseph A. Konstan , University of Minnesota, Minneapolis, MN
John Riedl , University of Minnesota, Minneapolis, MN
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.
Collaborative Filtering, Content-Based Filtering, Digital Libraries, Hybrid Recommender Systems
S. M. McNee, R. Torres, J. A. Konstan, M. Abel and J. Riedl, "Enhancing Digital Libraries with TechLens," Digital Libraries, Joint Conference on(JCDL), Tuscon, AZ, USA, 2004, pp. 228-236.