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Wide & Deep Learning in Job Recommendation: An Empirical Study

  • Shaoyun Shi
  • Min Zhang
  • Hongyu Lu
  • Yiqun Liu
  • Shaopin Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10648)

Abstract

Recommender systems have become more and more popular in recent years. Collaborative Filtering and Content-Based methods are widely used for a long time. Recently, some researchers introduced deep learning algorithms into recommender system. In this paper, we try to answer some questions about a novel recommender model, Wide & Deep Learning. Firstly, how should we select and feed in features? Secondly, how does Wide & Deep Learning work? Thirdly, how to joint-train the two parts of the network? Finally, how to conduct online training with new data? For all of these, we focus on the job recommendation task, which often suffers from the cold-start problem. The experiments give us the answers of these questions.

Keywords

Recommender system Wide & Deep Learning Job recommendation 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shaoyun Shi
    • 1
  • Min Zhang
    • 1
  • Hongyu Lu
    • 1
  • Yiqun Liu
    • 1
  • Shaopin Ma
    • 1
  1. 1.Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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