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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.

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  1. 1.

  2. 2.

  3. 3.

  4. 4.

  5. 5.

  6. 6.

  7. 7.


  1. 1.

    Al-Otaibi, S.T., Ykhlef, M.: A survey of job recommender systems. Int. J. Phys. Sci. 7(29), 5127–5142 (2012)

    Article  Google Scholar 

  2. 2.

    Attenberg, J., Pandey, S., Suel, T.: Modeling and predicting user behavior in sponsored search. In: SIGKDD, pp. 1067–1076. ACM (2009)

  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)

    Article  Google Scholar 

  4. 4.

    Bishop, C.M.: Pattern recognition and machine learning, vol. 1. Springer, Berlin (2006)

    MATH  Google 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)

  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)

  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)

  8. 8.

    Cheng, H., Cantú-Paz, E.: Personalized click prediction in sponsored search. In: WSDM, pp. 351–360. ACM (2010)

  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)

  10. 10.

    Färber, F., Weitzel, T., Keim, T.: An automated recommendation approach to selection in personnel recruitment. In: AMCIS, pp. 302. Citeseer (2003)

  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)

    MathSciNet  MATH  Google 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)

  13. 13.

    Harman, D.K.: The fourth text retrieval conference (TREC-4). National institute of standards and technology (1996)

  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)

  16. 16.

    Koren, Y., Sill, J.: Ordrec: an ordinal model for predicting personalized item rating distributions. In: RecSys, pp. 117–124. ACM (2011)

  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)

  18. 18.

    Lee, D.H., Brusilovsky, P.: Reinforcing recommendation using implicit negative feedback. In: User Modeling, Adaptation, and Personalization, pp. 422–427. Springer (2009)

  19. 19.

    Liu, T.-Y.: Learning to rank for information retrieval. Found. Trends Inf. Retr. 3(3), 225–331 (2009)

    Article  Google Scholar 

  20. 20.

    Ma, H., King, I., Lyu, M.R.: Effective missing data prediction for collaborative filtering. In: SIGIR, pp. 39–46. ACM (2007)

  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)

  22. 22.

    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to information retrieval. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  23. 23.

    Marlin, B., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. ICML (2012)

  24. 24.

    Metzler, D., Bruce Croft, W.: Linear feature-based models for information retrieval. Inf. Retr. 10(3), 257–274 (2007)

    Article  Google Scholar 

  25. 25.

    Nocedal, J.: Updating quasi-newton matrices with limited storage. Math. Comput. 35(151), 773–782 (1980)

    MathSciNet  Article  MATH  Google Scholar 

  26. 26.

    Paparrizos, I., Barla Cambazoglu, B., Gionis, A.: Machine learned job recommendation. In: RecSys, pp. 325–328. ACM (2011)

  27. 27.

    Rafter, R., Smyth, B.: Passive profiling from server logs in an online recruitment environment. In: ITWP, pp. 35–41 (2001)

  28. 28.

    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: UAI, pp. 452–461 (2009)

  29. 29.

    Schwarz, G., et al: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    MathSciNet  Article  MATH  Google 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)

  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)

  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)

  33. 33.

    Wang, J., Yi, Z., Posse, C., Bhasin, A.: Is it time for a career switch. In WWW, pp. 1377–1388 (2013a)

  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)

  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)

  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)

  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)

    Article  Google 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)

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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).

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  • Click prediction
  • Personalization
  • Job recommendation
  • Missing data