How Semantic search has revamped the Resume Search Engine?

By Published May 18, 2017
Resume Search Engine

CareerBuilder recently introduced an enhancement with semantic search, on its candidate search engine. What it means for most existing and prospective clients is search change in the way the search query results that recruiter can view on the dashboard.

Before we take a deep dive into what semantic search is all about, let’s understand what it is meant to be in the first place. ‘Semantic’ is defined as the meaning or interpretation of a word or sentence according to yourdictionary.com. The simplest interpretation is to understand what the meaning of any word is. What Semantic Search means is – it seeks to improve search accuracy by understanding the searcher’s intent and the contextual meaning of terms as they appear in the searchable dataspace. Semantic intends only to make sense of what a recruiter is looking on the CareerBuilder website. Whether it is right keywords for hidden talent or a position known with a different title in a different region, semantic search helps in unearthing these candidates with the right talent. This new enhancement makes the search simpler, more resourceful and increases recruiter productivity & you do not need expert lessons to use it.

Resume Search Engine

Let’s also see how it works on the resume search engine.

Once you submit a query for a profile, say content writer, the semantic search applies different meanings that it would fetch from its databank, synonyms, etc. to expand the query and delivers all the possible related results. The method makes sure all possible roles and titles are included so that potential candidates are not missed from the search. The recruiter will, of course, need to apply his background knowledge to determine the potentially best candidate. Artificial intelligence is not likely to bring in this functionality anytime soon.

The users get to see more candidates with less effort on the resume search database as the query finds resumes with related words along with the searched keywords. The result is ten times more candidates from the database instantly. It offers a personalized and transparent set of results and allows the user to keep or remove certain keywords within the results. The users are also enabled to add new keywords for more results. All these changes are remembered as personalized preferences for use in future as well. Further, the search queries are not depended on Boolean search formats or fixed query with operators. It reduces reliance on specific Boolean queries for good results, so you need not remember specific formats or operators for results and use the words as queries.

CareerBuilder uses its expertise of more than twenty years in understanding user behavior of both job seekers and employers and combining it with TextKernel to augment its offering for talent search.  When it comes to sourcing the right talent, you only need the right set of technology that helps you reduce time & effort as well as free up recruiters’ time for other recruitment activities.

CareerBuilder uses TextKernel technology for bringing the semantic search to the platform. In the words of Jacub Zavrel, Founder & CEO Text Kernel, “Great candidates often remain hidden because recruiters aren’t searching for the right keywords or job titles. Semantic search engines aim to understand what recruiters mean, rather than what they type” In recruitment, it helps to take the guesswork out of queries and add more value in return by adding more titles or related designations for the same profile.

It adds synonyms, abbreviations, typos or spelling variations all at the same time to return all possible combinations of profiles in the database. The machine learning algorithm keeps discovering the various combinations of searches, and this helps understand the context of the search. Thus, the results are more relevant.  Also known as bringing the wisdom of the crowds it will keep improving the search results continuously, and this benefit comes to all users in future. With TextKernel once run your query, you can simultaneously compare four candidates to choose the best one, assign various weights to more important components of search, select must-have and desirable keywords in a specific query, continuously provide feedback to the system as it learns-to-rank for a particular user in future. You may also include various social media and networking channels for sourcing. More so, you can create various projects that store profiles and bookmarking them for creating a talent pool. Further tagging profiles, setting up email alerts for daily notifications for new profiles and sharing the saved results with other users is simultaneously possible.

Semantic search will reduce the time spent on the screen while the work results will improve many folds as the productivity of the recruiters will go up. This will invariably increase the competitive edge for your organization as you will be able to hire faster and better talent than before. You would end up mining more data and uncovering the not so obvious talent from the database.

To see how semantic works with Resume Search Database, get in touch.

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