PhD in Computer Science
Research, Industrial, and Educational Background
I completed my dissertation in 2002 under the direction of
Haym Hirsh in
the computer science department
at Rutgers University.
My thesis investigated how to formulate algorithms for recommender systems
using classification techniques from machine learning as well as information
retrieval techniques to do text matching. Both of these approaches
considered how to combine information from multiple, often disparate
sources, such as content-based data and data used for collaborative filtering.
In addition to machine learning/data mining and information retrieval
I have worked in areas such as natural language processing, ontologies, voice
access and retrieval, personalization for call centers,
systems/geospatial information filtering,
and Web access to legacy data in the Software Systems and Applied
Research divisions of
Telcordia Technologies (formerly Bell Communications
Research) and at Sarnoff Corporation,
for government agencies and commercial
clients, including NASA,
ARDA, and SAIC.
I received my undergraduate degree in engineering (magna cum laude) from
Princeton University, where I
majored in computer science.
R. Alonso, J. A. Bloom, H. Li, and C. Basu. An Adaptive Nearest Neighbor Search for a Parts Acquisition ePortal, to appear in the Proceedings of the Ninth ACM International Conference on Knowledge Discovery and Data Mining, Washington, DC, August, 2003.
C. Basu. Recommendation as Classification and Recommendation as Matching: Two Information-Centered Approaches to Recommendation. PhD Thesis. Rutgers University, May, 2002.
C. Basu, H. Hirsh, W. W. Cohen, C. Nevill-Manning. Technical Paper Recommendation: A Study in Combining Multiple Information Sources. Journal of Artificial Intelligence Research , 14, 2001.
H. Hirsh, C. Basu, and B. D. Davison. Learning to Personalize. Communications of the ACM , 43(8), 2000.
C. Basu and H. Hirsh. Learning User Models for Recommendation. Proceedings of the Workshop on Machine Learning for User Modeling , held at the International Conference on User Modeling (UM99), Banff, Canada, June, 1999.
C. Basu, H. Hirsh, W. W. Cohen, and C. Nevill-Manning. Recommending Papers by Mining the Web. Proceedings of the Workshops on Learning about Users and Machine Learning for Information Filtering , held at the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden, July, 1999.
C. Basu, H. Hirsh, and W. Cohen.
Recommendation as Classification: Using Social and Content-Based Information in Recommendation,
Proceedings of the Fifteenth National Conference on Artificial Intelligence ,
Madison, WI. 1998. (also presented at the Workshop on Recommender Systems,
C. Basu, H. Hirsh, and W. Cohen.
Learning to Recommend.
Poster presented at the Fifteenth International Conference on Artificial Intelligence,
Nagoya, Japan. 1997.
L. Shklar, C. Behrens, C. Basu, and E. Au.
New Approaches to Cataloguing, Querying, and Browsing Geospatial Metadata,
Proceedings of the Second IEEE Metadata Conference,
Silver Spring, Maryland. 1997.
K. Ng, D. Loewenstern, C. Basu, H. Hirsh, and P. Kantor. Data Fusion of Machine-Learning Methods for the TREC-5 Routing Task (and other works). Proceedings of the Fifth Text Retrieval Conference (TREC), Gaithersburg, MD, November, 1996.
C. Basu. Legacy Data Access Using IRDL.
Workshop on Web Access to Legacy Data, at the
Fourth International WWW Conference. Boston, MA. 1995.
C. Basu and P. Kantor.
When Just One is Enough,
CIKM Workshop on Intelligent Information Agents,
Baltimore, MD. 1995.
L. Shklar, K. Shah, and C. Basu.
Putting Legacy Data on the Web: A Repository Definition Language,
Special Issue of ISDN and Computer Networks,
Proceedings of the Third International WWW Conference,
L. Shklar, K. Shah, C. Basu, and V. Kashyap. Modelling Heterogeneous
Proceedings of the Second International Workshop on Next Generation
Information Technologies , 1995.