Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
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)
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)
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)
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). https://doi.org/10.1007/978-3-540-72079-9_10
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)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Comput. (Long. Beach. Calif.) 42(8), 30–37 (2009)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, F., Xie, X. (2018). Overview of the NLPCC 2018 Shared Task: Social Media User Modeling. In: Zhang, M., Ng, V., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2018. Lecture Notes in Computer Science(), vol 11109. Springer, Cham. https://doi.org/10.1007/978-3-319-99501-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-99501-4_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99500-7
Online ISBN: 978-3-319-99501-4
eBook Packages: Computer ScienceComputer Science (R0)
