no code implementations • 31 May 2022 • Gaode Chen, Yijun Su, Xinghua Zhang, Anmin Hu, Guochun Chen, Siyuan Feng, Ji Xiang, Junbo Zhang, Yu Zheng
To address the above challenging problems, we propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems.
1 code implementation • International Conference on Database Systems for Advanced Applications(DASFAA) 2021 • Yantong Lai, Yijun Su, Cong Xue, Daren Zha
Then, we learn a unified user representation from context, sentiment and topic representations and apply multi-task learning for inferring user’s gender and age simultaneously.
1 code implementation • IEEE International Joint Conference on Neural Network 2020 • Yijun Su, Jia-Dong Zhang, Xiang Li, Daren Zha, Ji Xiang, Wei Tang, and Neng Gao
Recent studies mainly utilize social information, categorical information and/or geographical information to supplement the highly sparse check-in data.
1 code implementation • IEEE International Conference on Communications 2020 • Yijun Su, Xiang Li, Baoping Liu, Daren Zha, Ji Xiang, Wei Tang and Neng Gao.
With the popularity of location-based social networks (LBSNs), Point-of-Interest (POI) recommendation has become an essential location-based service to help people explore novel locations.
1 code implementation • International Conference on Neural Information Processing 2019 • Baoping Liu, Yijun Su, Daren Zha, Neng Gao, Ji Xiang.
First, we make full use of users’ check-in records and reviews to capture users’ intrinsic preferences (i. e., check-in, sentiment, and topic preferences).
1 code implementation • IEEE International Conference on Mobile Data Management (MDM) 2018 • Yijun Su, Xiang Li, Wei Tang, Ji Xiang, Yuanye He
In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles.