Overview of the NLPCC 2018 Shared Task: Social Media User Modeling

  • Fuzheng ZhangEmail author
  • Xing Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11109)


In this paper, we give the overview of the social media user modeling shared task in the NLPCC 2018. We first review the background of social media user modeling, and then describe two social media user modeling tasks in this year’s NLPCC, including the construction of the benchmark datasets and the evaluation metrics. The evaluation results of submissions from participating teams are presented in the experimental part.


User modeling Social media Recommendation 


  1. 1.
    Zhong, Y., Yuan, N.J., Zhong, W., Zhang, F., Xie, X.: You are where you go: inferring demographic attributes from location check-ins. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 295–304 (2015)Google Scholar
  2. 2.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of twitter posts for user profile construction on the social web. In: Extended Semantic Web Conference, pp. 375–389 (2011)Google Scholar
  3. 3.
    Zhang, F., Zheng, K., Yuan, N.J., Xie, X., Chen, E., Zhou, X.: A novelty-seeking based dining recommender system. In: The International Conference, pp. 1362–1372 (2015)Google Scholar
  4. 4.
    Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 272–281 (1994)Google Scholar
  5. 5.
    Adomavicius, G., Tuzhilin, A.: User profiling in personalization applications through rule discovery and validation. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 377–381 (1999)Google Scholar
  6. 6.
    Zhang, F., Yuan, N.J., Lian, D., Xie, X.: Mining novelty-seeking trait across heterogeneous domains. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 373–384 (2014)Google Scholar
  7. 7.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). Scholar
  8. 8.
    Popescul, A., Pennock, D.M., Lawrence, S.: Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 437–444 (2001)Google Scholar
  9. 9.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Comput. (Long. Beach. Calif.) 42(8), 30–37 (2009)Google Scholar
  10. 10.
    Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)Google Scholar
  11. 11.
    Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.-Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 353–362 (2016)Google Scholar
  12. 12.
    Zhang, F., Yuan, N.J., Zheng, K., Lian, D., Xie, X., Rui, Y.: Exploiting dining preference for restaurant recommendation. In: Proceedings of the 25th International Conference on World Wide Web, pp. 725–735 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Meituan AI LabBeijingChina
  2. 2.Microsoft Research AsiaBeijingChina

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