JR4CE: Job Recommendation for Career Exploration

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The expanding online job market and the impact of recommender systems highlight the need for practical job recommendations. While diversity is crucial, job recommendations uniquely require high-quality diversity to accelerate career exploration rather than just maximizing quantitative metrics. Existing systems often fall short, as high diversity scores do not always align with user preferences, resulting in user dissatisfaction. To address this, we propose J ob R ecommendation for C areer E xploration ( JR4CE ), which uses user-job interaction data, explicit preferences, and current user information, aiming to achieve high-quality diversity. JR4CE has three main modules: (1) Knowledge Graph Learning Module represents explicit preferences and current information as graphs and learns latent representations of users and jobs. (2) Collaborative Filtering Module uses interaction data to refine the latent representations from (1) and predicts the likelihood of a target user’s job application. (3) Diversity Data Augmentation Module performs data augmentation using the interaction data of role model users for a target user to enhance diversity, thereby aiming to support career exploration. Based on offline metrics and qualitative case studies, our experiments using datasets constructed from an actual job search website show that JR4CE outperforms several state-of-the-arts in accuracy and the accuracy-diversity balance. Specifically, JR4CE effectively works for users in the early stages of career exploration activities.