Conceptual recommender system for CiteSeerX

Short search engine queries do not provide contextual information, making it difficult for traditional search engines to understand what users are really requesting. One approach to this problem is to use recommender systems that identify user interests through various methods in order to provide information specific to the user’s needs. However, many current recommender systems use a collaborative model based on a network of users to provide the recommendations, leading to problems in environments where network relationships are sparse or unknown. Content-based recommenders can avoid the sparsity problem but they may be inefficient for large document collections. In this paper, we propose a concept-based recommender system that recommends papers to general users of the CiteSeerx1 digital library of Computer Science research publications. We also represent a novel way of classifying documents and creating user profiles based on the ACM (Association for Computer Machinery) classifica...

• Type: Conference
• Year: 2009
• Where: RECSYS
• Authors: Ajith Kodakateri Pudhiyaveetil, Susan Gauch, Hiep Phuc Luong, Josh Eno
• Keywords: Control Systems, Current Recommender Systems, Recommender System, RECSYS 2009, User Profile