SBIR Phase II: Urban Interactions, Inc.

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 0956817
Agency Tracking Number: 0839290
Amount: $500,000.00
Phase: Phase II
Program: SBIR
Awards Year: 2010
Solicitation Year: 2010
Solicitation Topic Code: SS
Solicitation Number: NSF 08-548
Small Business Information
1056 Cambridge St, Cambridge, MA, 02139
DUNS: 800752441
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Paul Nemirovsky
 (617) 642-7163
 paul.nemirovsky@gmail.com
Business Contact
 Paul Nemirovsky
Phone: (617) 642-7163
Email: 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. *

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