An Image Processing-based Approach for Human Resource Recommendation with Deep Learning

with the advent of the Internet era, the frequency and proportion of job seekers using the Internet to access recruitment information have been increasing. Consequently, the volume of human re-source information, including talent profiles and job postings, has experienced an unprecedented growth, posing challenges of information overload to human resource services. Recommender systems, however, can proactively address this issue by helping users navigate through the over-whelming information and delivering content that aligns with their interests, thereby becoming a primary tool for tackling information overload in the Internet era. Meanwhile, in recent years, deep learning has achieved remarkable success in various domains such as computer vision, nat-ural language processing, and semantic recognition. However, the application of deep learning in the field of recommender systems, particularly in the context of human resources, remains limited. Current research efforts in the development of human resource recommender systems mainly rely on traditional collaborative filtering or content-based filtering algorithms, with few explorations and investigations into novel recommendation approaches. To enhance the performance of hu-man resource recommender systems, this paper proposes a deep learning-based approach. This method involves transforming human resource data into normalized images and employing im-age processing techniques for learning and training on the human resource database. By doing so, this approach effectively addresses major issues encountered in traditional collaborative filtering algorithms, such as data sparsity and cold-start problems, thereby achieving higher recommendation performance.