Abstract
User profile inference, which aims to portray a user in detail, is one of fundamental tasks in social network analysis. Existing works still suffer from the difficulty in modeling user’s explicit attributes and social links, which is mainly caused by the text diversity and complex community structures. In this paper, we propose a hierarchical attention neural network to infer users’ missing attributes, which handles the user representation integrating both explicit personal information and social links. The core module is a hierarchical recurrent neural network which encodes both attribute-level and user-level information, and the attention mechanism can adaptively render different attributes and users with different weights. Extensive empirical studies are conducted on two real-world datasets. The experimental results show that our model prominently outperform other comparative deep models in predicting multi-value attributes (especially occupation), verify the effect of using user social links, and reveal different effects of different attention mechanism.
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Acknowledgement
This work was supported by the National Key Research and Development program of China (No. 2016YFB0801300), the National Natural Science Foundation of China grants (No. 61602466, No. 61702234).
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Kang, Z., Li, X., Cao, Y., Shang, Y., Liu, Y., Guo, L. (2018). Hierarchical Attention Networks for User Profile Inference in Social Media Systems. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_78
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DOI: https://doi.org/10.1007/978-3-030-01424-7_78
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