Abstract
How to mine social links with sparse data is a traditional problem. Researchers have ever studied the issue with transfer learning and achieved good results on telephone communication network or other acquaintances networks. However, the method would not work well on social networks sometime, as the social relationships in many social networks are not necessarily acquaintances. In this paper, we propose a new model, MS-TrBPadaboost, based on transfer learning and multiple social networks to solve the issue. The model could transfer information from multiple sources reasonably and self-adaptively so as to select more suitable knowledge and samples. The experimental results shows that our model yields better performance than state-of-the-art baselines, demonstrating the effectiveness of the model.
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Acknowledgments
This study was supported by Key-Area Research and Development Program of Guangdong Province (No. 2019B010137003).
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Zhang, K., He, L., Li, C., Wang, S., Zhang, Xy. (2020). A Multi-source Self-adaptive Transfer Learning Model for Mining Social Links. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_19
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DOI: https://doi.org/10.1007/978-3-030-55393-7_19
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