no code implementations • 18 Apr 2022 • Likang Wu, Hao Wang, Enhong Chen, Zhi Li, Hongke Zhao, Jianhui Ma
To that end, we propose a novel framework to promote cascade size prediction by enhancing the user preference modeling according to three stages, i. e., preference topics generation, preference shift modeling, and social influence activation.
no code implementations • 18 Aug 2021 • Kai Zhang, Hao Qian, Qi Liu, Zhiqiang Zhang, Jun Zhou, Jianhui Ma, Enhong Chen
Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
no code implementations • 13 Dec 2020 • Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, Enhong Chen
In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.
no code implementations • NeurIPS 2020 • Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma, Xing Xie, Enhong Chen
The GAN-style recommenders (i. e., IRGAN) addresses the challenge by learning a generator and a discriminator adversarially, such that the generator produces increasingly difficult samples for the discriminator to accelerate optimizing the discrimination objective.
no code implementations • 2 Jan 2020 • Han Wu, Kun Zhang, Guangyi Lv, Qi Liu, Runlong Yu, Weihao Zhao, Enhong Chen, Jianhui Ma
Technological change and innovation are vitally important, especially for high-tech companies.
no code implementations • 17 Dec 2019 • Linsong Du, Shihai Shao Gang Yang, Jianhui Ma, Qinpeng Liang, Youxi Tang
The reconfigurable intelligent surface (RIS), which consists of a large number of passive and low-cost reflecting elements, has been recognized as a revolutionary technology to enhance the performance of future wireless networks.
no code implementations • 9 Nov 2018 • Mingxiao An, Yongzhou Chen, Qi Liu, Chuanren Liu, Guangyi Lv, Fangzhao Wu, Jianhui Ma
Recently deep neural networks have been successfully used for various classification tasks, especially for problems with massive perfectly labeled training data.