Enhancing digital libraries with TechLens+

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 successful...

• Type: Conference
• Year: 2004
• Where: JCDL
• Authors: Roberto Torres, Sean M. McNee, Mara Abel, Joseph A. Konstan, John Riedl
• Keywords: Hybrid Algorithms, Hybrid Recommender Algorithms, JCDL 2004, Research Papers