Since manual searches are onerous and tedious, job recommendation systems help automating job search, suggesting the most appropriate job to a given candidate [AOY12]. Researchers have identified some requirements (Figure 5.1) that should be borne in mind when recommending candidates for a given job [MKWW06, MWK08, Kei07]:
1. The matching between candidates and jobs depends on skills and abilities that individuals should have.
2. The recommendation process is bidirectional, meaning that both the preferences of the recruiter and of the candidate should be taken into account.
3. Recommendations should be based on the candidate attributes, as well as the relational aspects in the working place.
Therefore, job recommendation is a bidirectional task, where the job with the highest match- ing degree should be recommended to a job seeker, and the job seeker with the highest matching degree should be recommended to a job. Candidates and jobs should be ranked according to some criteria, which are supposed to affect or are related to the employee performance on the job. Malinowski et al. [MKWW06] proposed a bilateral people-job recommender system to match applicants with job opening profiles, assuming that both data are available. In order to perform the matching, a supervised probabilistic model estimates the probability that an applicant likes a job. Differently, Paparrizos et al. [PCG11] recommended job positions to applicants based only on the job history of other employees. Starting from an individual who was currently employed in an institution, they aimed at predicting the next institution where the individual would have been employed. The authors relied on preceding job positions, fields of education, educational experiences, also taking into account start and finish dates. Qin et al. [QZX+18] proposed a neural network method to link persons and jobs. Recurrent neural network was used to build a word-level semantic representation for both job requirements and job seekers’ experiences. In particular, ability-aware attention strategies were designed to measure the different importance of job requirements for semantic representation, and the different contribution of each job exper- ience to an ability requirement was also measured. Yi et al. [YAC07] introduced a structured relevance model to match resumes and jobs. Almalis et al. [ATK14] introduced a content-based approach to quantify the suitability of a candidate employee for a specific job position.
38 CHAPTER 5. RECOMMENDER SYSTEMS FOR JOB SEARCH
Figure 5.1: Model of system requirements for candidates/job recommendation.Source: [AOY12].
Other existing works [KR14, CBVS14] focused on the academic degree of students, aiming to predict both their academic performance at early stage of their curricula and their placement chance, using supervised classifiers like SVMs or neural networks. Zhang et al. [ZYN14] designed a collaborative filtering method that takes advantage of background information such as students’ resumes, recruiting information, and users’ applications to jobs. Rafter et al. [RBS00] introduced an online job finder engine that uses a collaborative filtering algorithm with some user preferences, and mined server logs of web recruitment platforms to get an appropriate user representation. Buettner [Bue14] introduced a recommender system based on social network information, relying on three fit measures related to candidates. The first, called organization fit, is a macro-perspective compatibility between an employee’s personality and an organization’s culture; the second, called group fit, focuses on a user’s social interactions; the third, called job fit, is a micro-perspective matching between a candidate’s abilities or preferences, and job demands. A similar goal was pursue by Gupta and Garg [GG14], who used candidate profile matching as well as preserving their job preferences to develop a job recommender.
User profiling is one of the major flaws of job recommendation approaches, because such data are hard to retrieve, select and handle. In general, the concept of candidate profiles is relevant to properly estimate a candidate’s performance on jobs [SWNF12, PCG11]. Such profiles may contain:
• Personal information about the candidate, such as name, age, gender, location.
CHAPTER 5. RECOMMENDER SYSTEMS FOR JOB SEARCH 39 job start and finish dates.
• Information about educational experiences, including school and university names, degrees, and possibly other details.
For the sake of building profiles, Chi [Chi99] used a set of 41 skills, falling in three major classes, that are required education and experience, physical demands, and task environment. Hong et al. [HZWS13] performed an user grouping in distinct clusters and different recommendation strategies according to users’ characteristics. Other researchers [Bue14, GG14] used information related to companies, users, user preferences and social interactions. Rubin et al. [RBB02] showed the importance of extracurricular activities as a users’ skills indicator. Furthermore, they pointed out that corporate recruiters and talent pool managers would benefit from a tool that allows the systematic categorization and valuing of extracurricular and contextual activities.
While the just described job recommendation approaches use features related to the current and past experiences, either in employment or education, in Chapter 10 a method that relies on a candidate’s skillset will be introduced, where skillsets will be mined from LinkedIn, and used to represent both candidates and jobs. Thanks to this homogeneous representation, this approach will provide a prediction of the best job in relation to a user’s capabilities and knowledge.