User click prediction for personalized job recommendation

Major job search engines aggregate tens of millions of job postings online to enable job seekers to find valuable employment opportunities. Predicting the probability that a given user clicks on jobs is crucial to job search engines as the prediction can be used to provide personalized job recommendations for job seekers. This paper presents a real-world job recommender system in which job seekers subscribe to email alert to receive new job postings that match their specific interests. The architecture of the system is introduced with the focus on the recommendation and ranking component. Based on observations of click behaviors of a large number of users in a major job search engine, we develop a set of features that reflect the click behavior of individual job seekers. Furthermore, we observe that patterns of missing features may indicate various types of job seekers. We propose a probabilistic model to cluster users based on missing features and learn the corresponding prediction models for individual clusters. The parameters in this clustering-prediction process are jointly estimated by EM algorithm. We conduct experiments on a real-world testbed by comparing various models and features. The results demonstrate the effectiveness of our proposed personalized approach to user click prediction.