Yayınlar

This paper presents a comprehensive analysis of a Wide & Deep Learning approach enhanced with graph features for job recommendation systems. Our methodology combines three key components: a wide linear model for memorization of sparse feature interactions, a deep neural network for generalization, and a graph-based feature extraction mechanism that captures job transition patterns. The model processes five distinct feature groups: categorical cross-features, embeddings of job positions, graph-based transition features, text representations from job descriptions, and numerical attributes.

In the modern era, job searching has evolved into a time-consuming and complex process for both job seekers and recruitment professionals. Recommendation systems technology has high potential to bring job seekers and job opportunities together based on their skills and qualifications. In this research, various recommendation system algorithms are analyzed in-depth using real data gathered from Kariyer.net, Turkey’s leading employment website. The paper proposes a cascade hybrid filtering approach: initially creating a broad recommendation pool through item-based collaborative filtering, and then filtering the pool through TF-IDF text similarity.