Search Engine Algorithm Improvement

SOLUTION

While checking a candidate for matching the recruiter’s skills requirements, the algorithm compares the required skill and the candidate’s set of skills (so it’s not that the whole pool of the recruiters skills being forced to comply with the candidate’s). This approach enables to evaluate similarity between the two skills being compared.

The platform allows candidates to import skills directly from their LinkedIn accounts instead of entering them manually. This is possible because the platform’s skill set aligns closely with the skills listed on LinkedIn. Therefore, the problem is to find skills similarity in LinkedIn skills domain.

It’s worth noting that the similarity between the skills Sr and Sc is asymmetric. For example, skill “AngularJS” usually implies having skill “JavaScript” (as AngularJS is a JavaScript framework), but that is not true for the opposite case: knowing JavaScript doesn’t necessarily mean knowing AngularJS.

Let’s denote the set of skills of user u as skills(u) . Then as an estimate of the match degree between recruiter entered skill Sr and a candidate skill Sc we can use conditional probability of having skill Sr given that the candidate has skill Sc , i.e.

According to the definition of conditional probability, we have the following equation:

Thus, in order to calculate match degree between Sr and Sc we need to estimate two kinds of probabilities:

for a given skills

for a given pair of skills s 1 and s 2.

To estimate these probabilities, we used an external data source StackOverflow. Each topic on the site is labelled with one or more tags, so the topics were found relevant to the skills matching.

We mapped a significant part of LinkedIn skills to StackOverflow tags combining the following simple techniques:

• case-insensitive comparison
• ignoring punctuation marks (e.g. spaces, colons and dashes)
• abbreviation expansion
• words normalization using lemmatization
• full-text search in the topics description

The following considerations explain the calculation of the extent to which candidate skill Sc matches the skill Sr entered by a recruiter.

Let’s denote the set of tags of a StackOverflow topic t as tags(t)

Let T be the set of all topics on StackOverflow.

Having the LinkedIn to StackOverflow mapping f, we can approximate for a random topic t ∈ T. Similarly, can be approximated by for a random topic t ∈ T .

for a given StackOverflow skill s’ and a random topic t ∈ T is estimated to be

Similarly, for given StackOverflow skills s’1 , s’2 and a random topic t ∈ T estimated to be

Therefore, the extent to which candidate skill – Sc matches the skill – Sr entered by a recruiter is calculated by the formula:

The developed smart-matching algorithm provides more reasonable and precise search results. It has greatly improved the recruiters’ ability to find suitable candidates: on average there are 27% more relevant candidates found in searching results than before.