Search Technology and the Employment Sector

We’ve been writing a series of blogs about labor market management and IT’s changing role in the employment sector. Our latest blog, the third in the series, focuses on search technology. We examine how technological advancements can be applied to support Public & Private Employment Services. We outline how search technology has evolved over the years. And how cognitive systems play an increasing role. The rapidly changing employment market means that jobseekers often need the high-level support of well-informed caseworkers in Public Employment Services (PES).

To provide that support efficiently and effectively, caseworkers need access to systems using advanced search technologies, such as contextual and cognitive search. Within WCC, we use the most up-to-date search technology in our unique ELISE Smart Search & Match Platform. This powerful matching solution forms the basis for WCC’s Employment Platform, developed specifically to meet the sector’s needs.

The evolution of search technology

The Forrester Research model for enterprise search outlines search technology’s evolutionary path, starting with simple keyword searches through to cognitive systems. The business value grows with each search level, as does the usability or “humanization.”

Level 1 – keyword search

Search technology for helping jobseekers find employment quickly has been available for many years. Type in a keyword, and an answer will appear. But on what is this answer based? And more importantly, is it relevant or even useful? In other words, a simple keyword search only works if jobseekers know which search terms to use. And also, what options are available. If users choose unknown keywords or even alternative forms of a known keyword, they will find no matches. But as discussed in our first blog in this series, it is essential to realize that matching candidates with the first job within reach may achieve short-term success, but it is not necessarily sustainable.

Level 2 – semantic search

A semantic search seeks to understand natural language in the same way as a human. That is to say, understanding what keywords mean, contextualizing those keywords, and understanding user intent. It aims to make sense of structured and unstructured data sources such as resumes and vacancy descriptions. Jobseekers, employers, and recruiters, for example, invent new job titles and skill descriptions every day. Therefore it is vital to know which words and terms are relevant for matching a person with a job. Semantic searches also use language structure. For example, to look for similarities or the relationship between words. As a result, the outcome is more personalized results.

Level 3 – contextual search

Contextual searches process each query, understanding the context provided by the user. This search method increases the precision of results, calculating their relevance to individual users and market conditions. For instance, contextual searches can take into account the labor market and specific labor programs. They can also use knowledge about target groups, occupations, jobs, candidates, locations, and more. Machine learning is, therefore, a way to gain more significant insights into search characteristics. And it can help a PES improve their results too. To improve an Active Labor Market Policy, for instance, a PES can analyze information about input and effectiveness. Contextual information, in this case, relates to information about the labor market. In our solution, this contextual data includes taxonomies on occupations, skills, education, and certification.

Level 4 – cognitive search

Cognitive search includes all the above. But it also adds ways to interact and communicate with the system. We frame ideas in such a way that the system can understand the user and vice versa. For example, when jobseekers enter their data, the system should make helpful suggestions based on pre-existing knowledge. After the jobseekers enter their profession, the system can, for example, pre-load the most relevant skills and pre-tick appropriate checkboxes. The on-screen suggestions will be specific to each individual’s situation. Those for a recent graduate will be different from those for long-term unemployed. This functionality – known as Input Completion – is based on knowledge organized in a contextual knowledge base.

The role and future of search technology

As we have seen, search technology is evolving towards cognitive systems. The challenge now is to humanize communication with these services, making them easier to use. In our second blog in this series, we took a look at the vital role of PES in Labor Market Management and the importance of understanding trends in Labor Markets. The ongoing evolution of search technology towards cognitive systems has dramatically facilitated this understanding.

Organizations responsible for labor force management have different strategies in line with the demands of their government. Flexible, advanced, and easy-to-use PES systems based on contextual/ cognitive search technology are crucial to supporting these strategies.

WCC’s Employment Solutions already meet most cognitive search requirements. Our Employment Platform meets the needs of both Public and Private Employment Services. In doing so, it helps to bridge the gap between supply and demand. And the advanced technology used in WCC’s ELISE Smart Search and Match Platform accelerates traditional job or candidate searching. It matches candidates with vacancies and delivers highly relevant matches. Keeping abreast of – and implementing – technological advancements in the IT industry ensure that WCC remains a leading provider of advanced employment solutions. If you would like more information about our employment solutions, then please get in touch.

Article by: WCC Community
Published on: October 2, 2020

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