One of the most important foundations to companies to properly grow is to choose the perfect employees that fit their needs. Not only the technical skills but also their culture that fits their aspects. On the other side, choosing the most appropriate job for job-seekers is very important to advance their career and quality of life.
Recruitment process has become increasingly difficult, choosing the right employee among plenty of candidates for each job, each having different skills, cultures and ambitions.
Recommender system technology aims to help users find items that match their personal interests. So we can use this technology to solve the recruitment problem for both sides; companies, to find appropriate candidates, and job-seekers, to find favorable positions. So let's talk about what can science offer to solve this bidirectional problem.
In the world of data science, the more information we can get, the more accurate results we may have. So let’s start with available information we can collect about job-seekers and jobs.
- Personal information, such as language, social situation and location.
- Information about current and past professional positions held by the candidate. This section may contain companies names, positions, companies descriptions, job start dates, and job finish dates. The company description field may further contain information about the company (for example the number of employees and industry).
- Information about the educational background, such as university, degrees, fields of education, start and finish dates.
- IT skills, awards and publications.
- Relocation ability.
- Activities (like, share, short list)
- Required skills.
- Nice to have skills.
- Preferred location (onsite, work from home).
- Company preferences.
To get all this information we may face another big challenge. Most of this information may have been included in a plain text (ex. resume, job post description, etc.). So, we need to apply some knowledge extraction techniques on those texts, so we can get a complete view about requirements and skills.
A good matching technique requires more than just looking into explicit information only. For example, a job post that is defined to be looking for a candidate who has a knowledge about Java programming language while on the other side a candidate who has claimed knowledge with Spring framework, so if we are just looking for a candidate with explicit defined Java skill then this candidate will not be shown in the view, although he had an implicit Java skill by using Spring framework. To solve this problem we need to enrich both the job and candidate information by using a knowledge base that can link these two skills or at least knows that using Spring framework implicitly imply a Java skill. This will improve the accuracy by looking into the meanings and concepts instead of the explicit information only.
Let’s define some guidelines we need to take care of when working on the matching.
- Matching of individuals to job depends on skills and abilities that individuals should have.
- Recommending people is a bidirectional process, it should take into account the preferences of both recruiter and candidate.
- Recommendations should be based on the candidate’s attributes, as well as the relational aspects that determine the fit between the person and the team members/company with whom the person will collaborating (fit candidate to company not only the job).
- Must distinguish between must-have and nice-to-have requirements and improve their contribution with dynamic weights.
- Use ontology to categorize jobs as a knowledge base.
- Enrich job-seeker and jobs profiles with knowledge base (knowing Cakephp framework implies knowing also PHP).
- Data normalization to avoid domination.
- Learning from the others job transitions.
Let’s list some techniques used in recommendation fields, no technique is suitable for all cases, you need first to link it with type of data you have and your whole case.
- Collaborative filtering
- In this technique, we are looking for a similar behavior between job-seekers, so we can find job-seekers who have similar interests, and make job recommendations from their jobs of interest.
- Content-based filtering
- In this technique we are looking for profile’s content for both: the job-seeker and the job post, and get the best matching between them, regardless of the behavior of the job-seeker and the company that posted the job.
- Weighted In which, the score of item recommendation is calculated from the results of all of used recommendation techniques that are available in the system.
- Switching The system uses some criteria to switch between recommendation techniques.
- Mixed In which large number of recommendations are applied simultaneously, so we can mix the results from both recommenders.
- Feature Combination uses the collaborative information as additional feature data for each item and use content-based techniques over this improved data set
- Cascade It comprises a staged process. In this technique, one recommendation technique is used first to produce a rough ranking of candidates and a second technique refines the recommendation.
- 3A Ranking algorithm maps (job, company and job-seeker) to a graph with relations between them (apply, favorite, post, like, similar, match, visit, … etc), then depends on relations and ranking to recommend items.
- Content base is used to calculate similarity between jobs, job-seekers and companies, and each of them with the other one (match profile between job and job-seeker).
General recommendation system Architecture
Figure 1 - General System architecture.
To create a self improved system you need to get feedback for the results you produced to correct yourself over time. The best feedback you can get is the feedback from the real world, so we can depend on job-seekers and companies feedback to adjust the results as desired.
- Explicit: Ask users to rate the recommendations (jobs / candidates)
- Implicit: Track interaction on recommendations (applied, accepted, short list and ignored)
- Proceedings of the 22nd International Conference on World Wide Web. Yao Lu, Sandy El Helou, Denis Gillet (2013). A Recommender System for Job Seeking and Recruiting Website.
- JOURNAL OF COMPUTERS, VOL. 8. Wenxing Hong, Siting Zheng, Huan Wang (2013). A Job Recommender System Based on User Clustering.
- International Journal of the Physical Sciences Vol 7(29). Shaha T. Al-Otaibi, Mourad Ykhlef (July 2012). A survey of job recommender systems.
- Proceedings of the fifth ACM conference on Recommender systems. Berkant Cambazoglu, Aristides Gionis (2011). Machine learning job recommendation.