APPLICANT RECIPROCAL RECOMMENDER
Our client, a referral based recruitment portal, wanted to improve their ranking algorithm for candidates applying for a job. The current ranking algorithm was based on matching candidate skills with the job description. These were found to be sub optimal.
In order to arrive at better rank for a candidate for a job application, merely matching the candidate skills and job description is not enough. There also needs to be a cultural match of the candidate and the quality of referrer should also be factored in.
- Develop spiders to collect data from multiple sites on the Internet to
- quantify the quality of candidate skills
- characterize companies and educational institutes to predict the cultural match between the candidates employment history and prospective employer
- Extract information from spidered text to develop an influence network to measure the “network worth” of a referrer using social network analysis.
- Use Genetic Algorithms to optimize a hybrid model using skills, cultural match and referrer quality
TECHNIQUES, TECHNOLOGIES, TOOLS
Text Analysis, Topic Models, Genetic Algorithm, Conditional Random Fields, R, jsoup
There was a significant reduction in the number of candidates that needed to be interviewed as compared to prior to the use of the applicant recommender hence, saving a lot of time and money for the client.