Abstract
Online news platforms have attracted massive users to read digital news online. The demographic information of these users such as gender is critical for these platforms to provide personalized services such as news recommendation and targeted advertising. However, the gender information of many users in online news platforms is not available. Fortunately, male and female users usually have different pattern in reading online news. Thus, the news browsing data of users can provide useful clues for inferring their genders. In this paper, we propose a neural gender prediction approach based on the news browsing data of users. Usually a news article has different kinds of information such as title, body and categories. However, the characteristics of these components are very different, and they should be processed differently. Thus, we propose to learn unified user representations for gender prediction by incorporating different components of browsed news as different views of users. In each view, we use a hierarchical framework to first learn news representations and then learn user representations from news representations. In addition, since different words in news titles and bodies usually have different informativeness for learning news representations, we use attention mechanisms to select important words. Besides, since different news articles may also have different informativeness for gender prediction, we use news-level attentions to attend to important news articles for learning informative user representations. Extensive experiments on a real-world dataset validate the effectiveness of our approach.
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Acknowledgments
The authors would like to thank Microsoft News for providing technical support and data in the experiments, and Jiun-Hung Chen (Microsoft News) and Ying Qiao (Microsoft News) for their support and discussions. This work was supported by the National Key Research and Development Program of China under Grant number 2018YFC1604002, the National Natural Science Foundation of China under Grant numbers U1836204, U1705261, U1636113, U1536201, and U1536207, and the Tsinghua University Initiative Scientific Research Program.
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Wu, C., Wu, F., Qi, T., Huang, Y., Xie, X. (2019). Neural Gender Prediction from News Browsing Data. In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics. CCL 2019. Lecture Notes in Computer Science(), vol 11856. Springer, Cham. https://doi.org/10.1007/978-3-030-32381-3_53
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