1 code implementation • IEEE Transactions on Network Science and Engineering 2023 • Ge Fan, Chaoyun Zhang, Junyang Chen, Paul Li, Yingjie Li, Victor C. M. Leung
Experiments on three real-world datasets show that our proposed architecture achieves up to 13. 14% lower prediction error over baseline approaches.
no code implementations • 14 Oct 2022 • Ge Fan, Chaoyun Zhang, Kai Wang, Junyang Chen
In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems.
no code implementations • 15 Aug 2022 • Chaoyun Zhang, Kai Wang, Hao Chen, Ge Fan, Yingjie Li, Lifang Wu, Bingchao Zheng
However, the skill rating of a novice is usually inaccurate, as current matchmaking rating algorithms require considerable amount of games for learning the true skill of a new player.
no code implementations • IEEE 38th International Conference on Data Engineering (ICDE) 2022 • Ge Fan, Chaoyun Zhang, Junyang Chen, Baopu Li, Zenglin Xu, Yingjie Li, Luyu Peng, Zhiguo Gong
Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system.
no code implementations • Neurocomputing 2022 • Ge Fan, Biao Geng, Jianrong Tao, Kai Wang, Changjie Fan, Wei Zeng
These methods may fail to capture the personalized informativeness of each vertex.
no code implementations • Neurocomputing 2022 • Ge Fan, Biao Geng, Jianrong Tao, Kai Wang, Changjie Fan, Wei Zeng
These methods may fail to capture the personalized informativeness of each vertex.
Ranked #1 on Link Prediction on PPI
1 code implementation • Demal @ The Web Conference 2021 • Ge Fan, Chaoyun Zhang, Junyang Chen, Kaishun Wu
In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute.
Ranked #1 on Recommendation Systems on BeerAdvocate
no code implementations • 27 May 2019 • Ge Fan, Wei Zeng, Shan Sun, Biao Geng, Weiyi Wang, Weibo Liu
One advantage of deep neural network is that the performance of the algorithm can be easily enhanced by augmenting the depth of the neural network.