Automatic Feature Recommendation System for Job Postings Using Unsupervised Clustering and Association Rule Mining
Year: 2026
Type: article
Abstract: All sectors of business use online employment platforms to publish job postings to find the qualified personnel they need. A job posting text should be neither long enough to make it difficult to read, nor short enough to create a feeling of carelessness, and should contain clear information about the position. However, the preparation of a proper job posting text that complies with the qualifications needed by the job requires expertise and experience. Here, we propose a feature recommendation system to assist recruiters who prepare job posting text. The proposed system mines the relations between the features on a job posting dataset obtained from Kariyer.Net. We used text-mining and natural language processing techniques and employed the DBSCAN clustering algorithm to obtain feature clusters. In addition, we used the Apriori algorithm for finding association rules and frequent item sets to discover relations between feature clusters. Furthermore, we developed a user interface to experiment with feature suggestion results on selected job titles. Our results demonstrate that using such a recommender system makes the job posting preparation process fast and easy and increases the quality of the job postings created.
Institution: Dokuz Eylül University
Language: Turkish
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7162940152