Communications of the ACM
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic personalization based on Web usage mining
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Information Theoretic Clustering of Sparse Co-Occurrence Data
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Enhancing Product Recommender Systems on Sparse Binary Data
Data Mining and Knowledge Discovery
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Applications of wavelet data reduction in a recommender system
Expert Systems with Applications: An International Journal
A source independent framework for research paper recommendation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Leveraging the linkedin social network data for extracting content-based user profiles
Proceedings of the fifth ACM conference on Recommender systems
Link analysis in mind maps: a new approach to determining document relatedness
Proceedings of the 4th International Conference on Uniquitous Information Management and Communication
Recommending academic papers via users' reading purposes
Proceedings of the sixth ACM conference on Recommender systems
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Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBA) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.