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User click prediction for personalized job recommendation

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Abstract

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.

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Notes

  1. http://www.simplyhired.com/

  2. http://www.indeed.com/

  3. http://www.glassdoor.com

  4. https://www.absolventen.at/

  5. http://www.whitehouse.gov/sites/default/files/omb/bulletins/2013/b-13-01.pdf

  6. http://www.onetonline.org

  7. https://sourceforge.net/p/lemur/wiki/RankLib/

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Authors and Affiliations

  1. Indiana University, 901 E 10th, Bloomington, IN, 47408, USA

    Miao Jiang

  2. Computer Engineering Department, Santa Clara University, 500 El Camino Real, Santa Clara, California, 95053, USA

    Yi Fang

  3. LinkedIn, 2029 Stierlin Ct, Mountain View, California, USA

    Huangming Xie & Meng Meng

  4. Acorns Grow, Inc., 19900 MacArthur Blvd, Suite 400, Irvine, CA, 92612, USA

    Jike Chong

Authors
  1. Miao Jiang
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  2. Yi Fang
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  3. Huangming Xie
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  4. Jike Chong
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  5. Meng Meng
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Correspondence to Miao Jiang.

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Jiang, M., Fang, Y., Xie, H. et al. User click prediction for personalized job recommendation. World Wide Web 22, 325–345 (2019). https://doi.org/10.1007/s11280-018-0568-z

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