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Joint user profiling with hierarchical attention networks

Frontiers of Computer Science volume 17, Article number: 173608 (2023) Cite this article

Abstract

User profiling by inferring user personality traits, such as age and gender, plays an increasingly important role in many real-world applications. Most existing methods for user profiling either use only one type of data or ignore handling the noisy information of data. Moreover, they usually consider this problem from only one perspective. In this paper, we propose a joint user profiling model with hierarchical attention networks (JUHA) to learn informative user representations for user profiling. Our JUHA method does user profiling based on both inner-user and inter-user features. We explore inner-user features from user behaviors (e.g., purchased items and posted blogs), and inter-user features from a user-user graph (where similar users could be connected to each other). JUHA learns basic sentence and bag representations from multiple separate sources of data (user behaviors) as the first round of data preparation. In this module, convolutional neural networks (CNNs) are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy data. Following this, we build another bag which contains a user-user graph. Inter-user features are learned from this bag using propagation information between linked users in the graph. To acquire more robust data, inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag representation. Subsequently, all of the bag representations are integrated to lean comprehensive user representation by the self-attention mechanism. Our experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (2016YFB1000901), Innovative Research Team in University of the Ministry of Education (IRT17R32), and the National Natural Science Foundation of China (Grant Nos. 91746209 and 61906060).

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, Hefei, 230009, China

    Xiaojian Liu, Yi Zhu & Xindong Wu

  2. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230009, China

    Xiaojian Liu, Yi Zhu & Xindong Wu

  3. School of Information Engineering, Yangzhou University, Yangzhou, 225009, China

    Yi Zhu

  4. Mininglamp Academy of Sciences, Mininglamp, Beijing, 100193, China

    Xindong Wu

Authors
  1. Xiaojian Liu
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  2. Yi Zhu
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  3. Xindong Wu
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Corresponding author

Correspondence to Xindong Wu.

Additional information

Xiaojian Liu is currently a PhD student in the School of Computer Science and Information Engineering, Hefei University of Technology, China. He received the BS and MS degrees from Hefei University of Technology, China. His research interests are data mining and knowledge engineering, which include relation extraction, keyword extraction and user profiling.

Yi Zhu is currently an assistant professor in the School of Information Engineering, Yangzhou University, China. He received the BS degree from Anhui University, China, the MS degree from the University of Science and Technology of China, and the PhD degree from Hefei University of Technology, China. His research interests are data mining, knowledge engineering, and recommendation systems.

Xindong Wu is a professor in the School of Computer Science and Information Engineering at the Hefei University of Technology, China, and the president of Mininglamp Academy of Sciences, Mininglamp, China, and a fellow of IEEE and AAAS. He received his BS and MS degrees in computer science from the Hefei University of Technology, China, and his PhD degree in artificial intelligence from the University of Edinburgh, Britain. His research interests include data mining, big data analytics, and knowledge-based systems.

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Liu, X., Zhu, Y. & Wu, X. Joint user profiling with hierarchical attention networks. Front. Comput. Sci. 17, 173608 (2023). https://doi.org/10.1007/s11704-022-1437-6

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