Conceptual recommender system for CiteSeerX

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Conceptual recommender system for CiteSeerX

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ACM Conference On Recommender Systems archive
Proceedings of the third ACM conference on Recommender systems table of contents

New York, New York, USA

SESSION: Short papers table of contents

Pages 241-244

Year of Publication: 2009

ISBN:978-1-60558-435-5

Authors

Ajith Kodakateri Pudhiyaveetil	University Of Arkansas, Fayetteville, AR, USA
Susan Gauch	University Of Arkansas, Fayetteville, AR, USA
Hiep Luong	University Of Arkansas, Fayetteville, AR, USA
Josh Eno	University Of Arkansas, Fayetteville, AR, USA

Sponsor

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 17, Downloads (12 Months): 17, Citation Count: 0

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ABSTRACT

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 CiteSeerx 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) classification tree. Based on these user profiles which are built using past click histories, relevant papers in the domain are recommended to users. Experiments with a set of users on the CiteSeerX database show that our concept-based method provides accurate recommendations even with limited user profile histories.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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