Computer Science

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Browsing Computer Science by Author "Amani Basem Mohammed Haboub"

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    Employment Recommendation System Using Content-Based Filtering Approach and Fuzzy Logic
    (Al-Quds University, 2020-08-19) Amani Basem Mohammed Haboub; أماني باسم محمد حبوب
    Nowadays, there is a gap between the requirements of the labor market and the scientific specializations, which are available in universities. The existence of similar disciplines with different names in universities leads to a large number of specializations, especially for a specific number of jobs. These things we mentioned before make it difficult to find and match the most suitable specialization, which based on the educational outcomes for the available jobs in the labor market. The importance of the study reveals in matching Automatic job with an available job positions is a big problem for organizations and applicants. There are many techniques and algorithms to help job seekers find the right job. Some are traditional algorithms while others have been found recently, also there are a large number of hybrid algorithms that are a combination of many algorithms. All these algorithms aim to find the best job for the candidate and to make recruitment processes faster, more accurate and transparent. The literature review focused on how we can help job seekers find the most suitable jobs using techniques such as analysis, extraction, and web crawling. The problem with current method is that it calculates the similarity between job applications (job profile) and CV (user profile, this happens without measuring whether the recommended candidate is satisfying or meets the employer's goals or needs. The main question of this work (problem statement) is how to achieve the best matching between candidates and offered jobs. So, we suggest a hybrid system based on NLP tasks, BabelNet dictionary, content base filtering approach and finally fuzzy logic, all that allow system users (i.e., applicants and employers) to search for suitable jobs with matching them with job offers, which leads to best presentation for each candidate. We also suggest building a database contains different specialties and appropriate jobs, according to the opinion of stakeholders, such as academics who teach that specialization and the opinion of some workers in the labor market. Our main contribution is proposing a model to manage the recruitment process by evaluating the applicants' CVs, which contents weighting match to the offered/posted (open jobs) requirements by recognizing the employers' needs. In order to make our finding approach closer to reality, transparent and to reduce effort and time applicants and employers spend, we take into account the opinions of academics working in Palestinian universities in different disciplines and their relationship to the labor market. Also, the study provides an opportunity for applicants, who were not accepted when the education section was evaluated from 100%, or getting a low match ratio to be acceptable, and has a good chance to employ. The expert knowledge base we created effects on results by giving a wider chance to those who had a low employing chance to be accepted, also the study shows that the employee opinion effected in the candidate list. Finally, as appears from candidate lists, the system is well effective in extracting lists according to the proportion of matching required for each section in the employment announcement, and in terms of accuracy. The results are satisfactory, according to seekers qualifications, there are very close matching rates in some cases and spaced out in other cases. Also, it is easy to use through the simple user interface and flexibility in dealing with by consistent with the requirements of the employer expressed by a certain percentage ratioالوظيفة الشاغرة مع المتقدمين لها، لاستخراج أفضل النتائج.