Patent: A Job Search Engine Based on Occupation Vectors and a Job Identification Model

Bill Slawski breaks down a newly granted Google patent related to Job Search Engine that sheds some light on how Google will use job identification model to enhance job search by improving the quality of search results in response to a job search query. Here is an overly simplified take away from it, go read Bill’s breakdown for the detailed explanation.

The job identification model can identify relevant job postings that could otherwise go unnoticed by conventional algorithms due to inherent limitations of keyword-based searching. It sounds like Google is trying to understand job position titles and how they may be connected with each other, and developing a vector vocabulary, and build ontologies of related positions.

An example:

1. At a first hair salon marketed as a rugged barbershop, advertises a job posting for a “barber”
2. At a second hair salon marketed as a trendy beauty salon, advertises a job posting for a “stylist”
3. At both, the job posting seeks a person for the occupation of a “hairdresser” who cuts and styles hair
4. In a search system limited to keyword-based searching, a searcher seeking job opportunities for a “hairdresser” searchings for job opportunities using the term “barber” may not receive available job postings for a “stylist,” “hairdresser,” or the like if those job postings do not include the term “barber”
5. The process in this patent uses a job identification model seeking to address this problem

The patent tells us how an occupation taxonomy may be trained, using training data. It also provides more details about the Job identification model. And then tells us about how a job search is performed using that job identification model.