According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.
We also considered techniques used to enhance information retrieval using concepts of semantic analysis such as ontology based similarity measures [9], [10]. In these approaches the ontology information is used to find similarity between words and find words even if the exact match is not available. Other ways in which semantic information is extracted is using Wordnet libraries. Wordnet based approaches have used concepts such as relatedness of words for information retrieval [11]-[14]. @cite_4 Demonstrated that informative structured snippets of the job postings can be generated in an unsupervised way to improve the user experience for a job search engine. in his book [15], describes the various uses of search engines in information retrieval. Recent works [16] have shown the use of encyclopedic knowledge for information retrieval. [17], describe the use of Google distance to find concept similarity. Google distance based approaches have been used in various applications such as relevant information extraction [21], [18], keyword prediction [19], and tag filtering [20]. Given the keywords that we need to look for and rank them based on the documents given is also a similar problem.
@cite_4
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KEY BENEFIT: Written by a leader in the field of information retrieval, this text provides the background and tools needed to evaluate, compare and modify search engines. KEY TOPICS: Coverage of the underlying IR and mathematical models reinforce key concepts. Numerous programming exercises make extensive use of Galago, a Java-based open source search engine. MARKET: A valuable tool for search engine and information retrieval professionals.