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SBIR Phase II: Matching Algorithms and Talent Acquisition System to Improve Start-Up Staffing

SBIR Phase II: Matching Algorithms and Talent Acquisition System to Improve Start-Up Staffing
SBIR第二阶段:匹配算法和人才获取系统以改善初创企业人员配置
批准号:
1127357
负责人:
Stephen Roberson
金额:
$50.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-15 至 2014-01-31

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中文摘要
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英文摘要
This Small Business Innovation Research (SBIR) Phase II project aims to create core algorithms for a Talent Acquisition System to pragmatically match candidates to startup job opportunities. Startup hiring needs are unique, and the market lacks an effective platform to accelerate and improve this core competency for company building. Generic search of a resume database does not sufficiently capture the unique fit requirements of startup employment nor return acceptable results. This research aims to incorporate (a) limited employer input of search criteria using a simple interface with (b) a broad range of normalized inputs, each individually scored for startup fit, to create a self-tuning algorithm for the search, discovery, and pairing of candidates to the unique needs of startups. The innovation in this approach is to create a system inherently weighted to both the hard and soft attributes of startup work/life. If successful, this effort will remove much of the guesswork by pointing employers to those most likely to excel in these opportunities. Data extraction, scoring techniques, and full text search will be applied to resumes, questionnaires, job search histories, social networking maps and search terms to feed the algorithm.The broader impact of this project will be to improve the success rate for young companies by accelerating and improving the staffing of strong teams at every level in the organization. StartUpHire believes there is significant commercial potential for a startup centric career resource in the $6 billion annual U.S. online recruitment industry. Competitive approaches treat startup recruiting as identical to large company recruiting, yet experience indicates there is tremendous demand for an approach built around the unique needs of this community. Companies benefit by (a) focusing on talent which self-selects into this ecosystem and (b) algorithmically filtering these candidates using startup-specific success criteria. This research will create the first platform of its kind specific to startups, something employers have repeatedly requested. The proposed system will deliver both quality and speed biased to the needs of emerging growth companies.
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