Job recommendation systems play a critical role in matching individuals with relevant career opportunities based on their skills and experiences. However, many existing systems struggle to balance precision and contextual relevance, leading to mismatches in job recommendations. In this paper we introduce JobMatcher, a multilayered recommendation system that integrates a well established technique, cosine similarity and KNN clustering with ChatGPT based evaluation. Initial recommendations are generated through content-based filtering and refined via clustering similar job descriptions aligned with user profiles by seniority and trajectory. To enhance contextual accuracy, GPT 3.5 turbo was prompted to act as an expert evaluator, scoring top recommendations based on skill relevance and career fit using structured and unbiased prompts. In a user study with seven domain experts and ten user profiles, system-selected jobs scored significantly higher (mean = 3.43 compared to 2.99 for KNN clustering, p = 0.0035), with moderate inter-rater agreement (Kendall’s W = 0.417). JobMatcher bridges algorithmic filtering with human like evaluation, offering a scalable, intelligent solution for improved job matching.

Recommendation systems, job recommendations, content based recommendations, ChatGPT as evaluator