Conceptual recommender system for CiteSeerX

Authors:
Ajith Kodakateri Pudhiyaveetil;Susan Gauch;Hiep Luong;Josh Eno
Affiliations:
University Of Arkansas, Fayetteville, AR, USA;University Of Arkansas, Fayetteville, AR, USA;University Of Arkansas, Fayetteville, AR, USA;University Of Arkansas, Fayetteville, AR, USA
Venue:
Proceedings of the third ACM conference on Recommender systems
Year:
2009

Citing 10
Cited 4

Term-weighting approaches in automatic text retrieval

Information Processing and Management: an International Journal
Fab: content-based, collaborative recommendation

Communications of the ACM
CiteSeer: an autonomous Web agent for automatic retrieval and identification of interesting publications

AGENTS '98 Proceedings of the second international conference on Autonomous agents
Developing recommendation services for a digital library with uncertain and changing data

Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

IEEE Transactions on Knowledge and Data Engineering
Technical paper recommendation: a study in combining multiple information sources

Journal of Artificial Intelligence Research
User profiles for personalized information access

The adaptive web
Hybrid web recommender systems

The adaptive web
Towards next generation citeseer: a flexible architecture for digital library deployment

ECDL'06 Proceedings of the 10th European conference on Research and Advanced Technology for Digital Libraries

A source independent framework for research paper recommendation

Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Recommending academic papers via users' reading purposes

Proceedings of the sixth ACM conference on Recommender systems
Position-Aligned translation model for citation recommendation

SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Research paper recommender system evaluation: a quantitative literature survey

Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation

Quantified Score

Hi-index	0.00

Visualization

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.