SBIR Phase II: Urban Interactions, Inc.

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$500,000.00
Award Year:
2010
Program:
SBIR
Phase:
Phase II
Contract:
0956817
Award Id:
90902
Agency Tracking Number:
0839290
Solicitation Year:
n/a
Solicitation Topic Code:
B3e
Solicitation Number:
n/a
Small Business Information
1056 Cambridge St, Cambridge, MA, 02139
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
800752441
Principal Investigator:
Paul Nemirovsky
(617) 642-7163
paul.nemirovsky@gmail.com
Business Contact:
Paul Nemirovsky
(617) 642-7163
paul.nemirovsky@gmail.com
Research Institution:
n/a
Abstract
This Small Business Innovation Research (SBIR) Phase II project aims to improve the quality of on-demand job matching by applying data mining and machine learning techniques to natural language descriptions of job requests, worker reviews, and transaction history. The project will enable lasting job matches by predicting the needs, preferences and constraints of workers and human resource managers. Currently available methods of job matching rely primarily on keyword search, corporate personality assessment tests, or fixed ontologies. Such systems lack comprehensive learning and therefore have difficulty matching workers with jobs. This project approaches job matching with a bias-free learning model that learns from hiring successes, trains on real-world data, and adapts to new job verticals. The broader/commercial impact of the project is a matching technology that optimizes workers' and employers' strengths, discovering matching opportunities overlooked by traditional search technologies. Online reputation-building through performance reviews can improve workers' ability to market themselves. The global matching technology permits nearly every skill to become marketable by matching workers with all features from every available job request. Natural language processing techniques, developed in the course of this project, have the potential to broaden the appeal of cell phone text-messaging as a comprehensive job-searching tool. Furthermore, the contextual approach to learning about workers and employers enables trends to be identified among users, and has far-reaching commercial implications in fields as diverse as medical research and e-commerce.

* information listed above is at the time of submission.

Agency Micro-sites


SBA logo

Department of Agriculture logo

Department of Commerce logo

Department of Defense logo

Department of Education logo

Department of Energy logo

Department of Health and Human Services logo

Department of Homeland Security logo

Department of Transportation logo

Enviromental Protection Agency logo

National Aeronautics and Space Administration logo

National Science Foundation logo
US Flag An Official Website of the United States Government