Using NLP for Machine Learning of User Profiles^☆

Article type: Research Article

Authors: Bloedorn, Eric^{a; *} | Mani, Inderjeet^{b; 1}

Affiliations: [a] Machine Learning and Inference Laboratory, George Mason University, Fairfax, VA 22030, USA | [b] Artificial Intelligence Technical Center, The MITRE Corporation, W640, 1820 Dolley Madison Boulevard, McLean, VA 22102, USA

Correspondence: [*] Corresponding author. E-mail: bloedorn@mitre.org.

Note: [☆] This work was funded by MITRE under the MITRE Sponsored Research program.

Note: [1] E-mail: limani@mitre.org.

Abstract: As more information becomes available electronically, tools for finding information of interest to users becomes increasingly important. The goal of the research described here is to build a system for generating comprehensible user profiles that accurately capture user interest with minimum user interaction, The research focuses on the importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could be drawn from a thesaurus. Our experiments, conducted in the context of a content-based profiling system for on-line newspapers on the World Wide Web (the IDD News Browser), demonstrate the importance of a generalization hierarchy and the promise of combining natural language processing techniques with machine learning (ML) to address an information retrieval (IR) problem.

Keywords: Information filtering, Machine learning, Natural language processing, Generalization hierarchy

DOI: 10.3233/IDA-1998-2102

Journal: Intelligent Data Analysis, vol. 2, no. 1, pp. 3-18, 1998