Enhancing digital libraries with TechLens

• Published in: International Conference on Digital Libraries: Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries; 07-11 June 2004 pp. 228 - 236
• Main Authors: Torres, Roberto, McNee, Sean M., Abel, Mara, Konstan, Joseph A., Riedl, John
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 07.06.2004; IEEE; ACM Press
• Series: ACM Conferences
• Subjects: Applied computing > Computers in other domains > Digital libraries and archives; Applied sciences; Collaboration; Computer science; Computer science; control theory; systems; Digital filters; Exact sciences and technology; Filtering algorithms; Information filtering; Information filters; Information retrieval; Information systems > Information retrieval; Information systems > Information retrieval > Information retrieval query processing; Information systems > Information retrieval > Retrieval models and ranking; Information systems > Information retrieval > Retrieval tasks and goals > Document filtering; Information systems > Information retrieval > Retrieval tasks and goals > Information extraction; Information systems > Information systems applications > Digital libraries and archives; Information systems. Data bases; Memory organisation. Data processing; Recommender systems; Search engines; Software; Software libraries; hybrid recommender systems; collaborative filtering; digital libraries; content-based filtering; Database; Filtering; Portuguese; Electronic library; Recommendation
• ISBN: 1581138326; 9781581138320
• DOI: 10.1145/996350.996402

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.