User click prediction for personalized job recommendation

  • Miao Jiang
  • Yi Fang
  • Huangming Xie
  • Jike Chong
  • Meng Meng


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.


Click prediction Personalization Job recommendation Missing data 


  1. 1.
    Al-Otaibi, S.T., Ykhlef, M.: A survey of job recommender systems. Int. J. Phys. Sci. 7(29), 5127–5142 (2012)CrossRefGoogle Scholar
  2. 2.
    Attenberg, J., Pandey, S., Suel, T.: Modeling and predicting user behavior in sponsored search. In: SIGKDD, pp. 1067–1076. ACM (2009)Google Scholar
  3. 3.
    Balog, K., Yi, F., de Rijke, M., Serdyukov, P., Si, L., et al.: Expertise retrieval. Found. Trends Inf. Retr. 6(2-3), 127–256 (2012)CrossRefGoogle Scholar
  4. 4.
    Bishop, C.M.: Pattern recognition and machine learning, vol. 1. Springer, Berlin (2006)zbMATHGoogle Scholar
  5. 5.
    Bradley, K., Rafter, R., Smyth, B.: Case-based user profiling for content personalisation. In: Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 62–72. Springer (2000)Google Scholar
  6. 6.
    Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., Hullender, G.: Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on Machine learning, pp. 89–96. ACM (2005)Google Scholar
  7. 7.
    Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on Machine learning, pp. 129–136. ACM (2007)Google Scholar
  8. 8.
    Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In: WSDM, pp. 351–360. ACM (2010)Google Scholar
  9. 9.
    Cheng, Y., Xie, Y., Chen, Z., Agrawal, A., Choudhary, A., Guo, J.S.: A real-time system for mining job-related patterns from social media. In: SIGKDD, pp. 1450–1453. ACM (2013)Google Scholar
  10. 10.
    Färber, F., Weitzel, T., Keim, T.: An automated recommendation approach to selection in personnel recruitment. In: AMCIS, pp. 302. Citeseer (2003)Google Scholar
  11. 11.
    Freund, Y., Iyer, R., Schapire, R.E., Singer, Y.: An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res. 4(Nov), 933–969 (2003)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: ICML, pp. 13–20 (2010)Google Scholar
  13. 13.
    Harman, D.K.: The fourth text retrieval conference (TREC-4). National institute of standards and technology (1996)Google Scholar
  14. 14.
    Hutterer, M.: Enhancing a job recommender with implicit user feedback. Fakultät für Informatik, Technischen Universität Wien (2011)Google Scholar
  15. 15.
    Tobias K.: Extending the applicability of recommender systems: a multilayer framework for matching human resources. In: HICSS, pp. 169–169. IEEE (2007)Google Scholar
  16. 16.
    Koren, Y., Sill, J.: Ordrec: an ordinal model for predicting personalized item rating distributions. In: RecSys, pp. 117–124. ACM (2011)Google Scholar
  17. 17.
    Lee, D.H., Brusilovsky, P.: Fighting information overflow with personalized comprehensive information access: a proactive job recommender. In: ICAS07, pp. 21–21. IEEE (2007)Google Scholar
  18. 18.
    Lee, D.H., Brusilovsky, P.: Reinforcing recommendation using implicit negative feedback. In: User Modeling, Adaptation, and Personalization, pp. 422–427. Springer (2009)Google Scholar
  19. 19.
    Liu, T.-Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009)CrossRefGoogle Scholar
  20. 20.
    Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: SIGIR, pp. 39–46. ACM (2007)Google Scholar
  21. 21.
    Malinowski, J., Keim, T., Wendt, O., Weitzel, T.: Matching people and jobs: A bilateral recommendation approach. In: HICSS, volume 6, pp. 137–145. IEEE (2006)Google Scholar
  22. 22.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  23. 23.
    Marlin, B., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. ICML (2012)Google Scholar
  24. 24.
    Metzler, D., Bruce Croft, W.: Linear feature-based models for information retrieval. Inf. Retr. 10(3), 257–274 (2007)CrossRefGoogle Scholar
  25. 25.
    Nocedal, J.: Updating quasi-newton matrices with limited storage. Math. Comput. 35(151), 773–782 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Paparrizos, I., Barla Cambazoglu, B., Gionis, A.: Machine learned job recommendation. In: RecSys, pp. 325–328. ACM (2011)Google Scholar
  27. 27.
    Rafter, R., Smyth, B.: Passive profiling from server logs in an online recruitment environment. In: ITWP, pp. 35–41 (2001)Google Scholar
  28. 28.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)Google Scholar
  29. 29.
    Schwarz, G., et al: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A., Oliver, N.: Tfmap: optimizing map for top-n context-aware recommendation. In: SIGIR, pp. 155–164. ACM (2012)Google Scholar
  31. 31.
    Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., Hanjalic, A.: Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: RecSys, pp. 139–146. ACM (2012b)Google Scholar
  32. 32.
    Singh, A., Rose, C., Visweswariah, K., Chenthamarakshan, V., Kambhatla, N.: Prospect: a system for screening candidates for recruitment. In: CIKM, pp. 659–668. ACM (2010)Google Scholar
  33. 33.
    Wang, J., Yi, Z., Posse, C., Bhasin, A.: Is it time for a career switch. In WWW, pp. 1377–1388 (2013a)Google Scholar
  34. 34.
    Wang, T., Bian, J., Liu, S., Zhang, Y., Liu, T.-Y.: Psychological advertising: exploring user psychology for click prediction in sponsored search. In: SIGKDD, pp. 563–571. ACM (2013b)Google Scholar
  35. 35.
    Xu, J., Li, H.: Adarank: a boosting algorithm for information retrieval. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 391–398. ACM (2007)Google Scholar
  36. 36.
    Xue, G.-R., Lin, C., Yang, Q., Xi, W., Zeng, H.-J., Yong, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: SIGIR, pp. 114–121. ACM (2005)Google Scholar
  37. 37.
    Yu, H.T., Liu, C.R., Zhang, F.Z.: Reciprocal recommendation algorithm for the field of recruitment. Int. J. Inf. Comput. Sci. 8(16), 4061–4068 (2011)Google Scholar
  38. 38.
    Yi, F., Si, L., Mathur, A.P.: Discriminative probabilistic models for expert search in heterogeneous information sources. Inf. Retr. 14(2), 158–177 (2011)CrossRefGoogle Scholar
  39. 39.
    Yao, L., Helou, S.E., Gillet, D.: A recommender system for job seeking and recruiting website. In: WWW, pp. 963–966 (2013)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Miao Jiang
    • 1
  • Yi Fang
    • 2
  • Huangming Xie
    • 3
  • Jike Chong
    • 4
  • Meng Meng
    • 3
  1. 1.Indiana UniversityBloomingtonUSA
  2. 2.Computer Engineering DepartmentSanta Clara UniversitySanta ClaraUSA
  3. 3.LinkedInMountain ViewUSA
  4. 4.Acorns Grow, Inc.IrvineUSA

Personalised recommendations