Abstract
User-generated content is daily produced in social media, as such user interest summarization is critical to distill salient information from massive information. While the interested messages (e.g., tags or posts) from a single user are usually sparse becoming a bottleneck for existing methods, we propose a topic-aware graph-based neural interest summarization method (UGraphNet), enhancing user semantic mining by unearthing potential user relations and jointly learning the latent topic representations of posts that facilitates user interest learning. Experiments on two datasets collected from well-known social media platforms demonstrate the superior performance of our model in the tasks of user interest summarization and item recommendation. Further discussions also show that exploiting the latent topic representations and user relations are conductive to the user automatic language understanding.
This work was supported by National Natural Science Foundation of China under Grant No. 62102265, by the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) under Grant No. GML-KF-22-29, by the Natural Science Foundation of Guangdong Province of China under Grant No. 2022A1515011474, by the Science and Technology Development Fund, Macau SAR, China, under Grant No. (0068/2020/AGJ, SKL-IOTSC(UM)-2021-2023), and by Shenzhen Talents Special Project - Guangdong Provincial Innovation and Entrepreneurship Team Supporting Project under Grant No. 2021344612).
X. Li—unique corresponding author
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Chen, J. et al. (2023). A Topic-Aware Graph-Based Neural Network for User Interest Summarization and Item Recommendation in Social Media. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13944. Springer, Cham. https://doi.org/10.1007/978-3-031-30672-3_36
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