NLPCC 2018 Shared Task User Profiling and Recommendation Method Summary by DUTIR_9148

  • Xiaoyu Chen
  • Jian WangEmail author
  • Yuqi Ren
  • Tong Liu
  • Hongfei Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


User profiling and personalized recommendation plays an important role in many business applications such as precision marketing and targeting advertisement. Since user data is heterogeneous, leveraging the heterogeneous information for user profiling and personalized recommendation is still a challenge. In this paper, we propose effective methods to solve two subtasks working in user profiling and recommendation. Subtask one is to predict users’ tags, we treat this subtask as a binary classification task, we combine users’ profile vector and social Large-scale Information Network Embedding (LINE) vector as user features, and use tag information as tag features, then apply a deep learning approach to predict which tags are related to a user. Subtask two is to predict the users a user would like to follow in the future. We adopt social-based collaborative filtering (CF) to solve this task. Our results achieve second place in both subtasks.


User tags prediction User following recommendation User modeling Collaborative filtering Deep learning 



This research is supported by the National Key Research Development Program of China (No. 2016YFB1001103).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Xiaoyu Chen
    • 1
  • Jian Wang
    • 1
    Email author
  • Yuqi Ren
    • 1
  • Tong Liu
    • 1
  • Hongfei Lin
    • 1
  1. 1.Dalian University of TechnologyDalian LiaoningChina

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