1 code implementation • COLING 2022 • Dugang Liu, Weihao Du, Lei LI, Weike Pan, Zhong Ming
Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency.
no code implementations • 20 Feb 2024 • Weixin Li, Yuhao Wu, Yang Liu, Weike Pan, Zhong Ming
In real recommendation scenarios, users often have different types of behaviors, such as clicking and buying.
1 code implementation • 27 Jan 2024 • Zhaohao Lin, Weike Pan, Zhong Ming
It combines the characteristics of sequential recommender systems and cross-domain recommender systems, which can capture the dynamic preferences of users and alleviate the problem of cold-start users.
no code implementations • 10 Jan 2024 • Shu Chen, Zitao Xu, Weike Pan, Qiang Yang, Zhong Ming
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).
no code implementations • 30 Aug 2023 • Xiaoqing Chen, Zhitao Li, Weike Pan, Zhong Ming
MBSR is a relatively new and worthy direction for in-depth research, which can achieve state-of-the-art recommendation through suitable modeling, and some related works have been proposed.
no code implementations • 25 Jul 2023 • Guowei Wu, Weike Pan, Zhong Ming
Graph neural networks (GNNs) have gained wide popularity in recommender systems due to their capability to capture higher-order structure information among the nodes of users and items.
no code implementations • 28 May 2023 • Youlong Ding, Xueyang Wu, Hao Wang, Weike Pan
The Transformer has emerged as a versatile and effective architecture with broad applications.
1 code implementation • 21 Mar 2023 • Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang, Zhenhua Dong, Rui Zhang, Xiuqiang He, Weike Pan, Zhong Ming
To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.
1 code implementation • 7 Feb 2023 • Dugang Liu, Yang Qiao, Xing Tang, Liang Chen, Xiuqiang He, Weike Pan, Zhong Ming
Specifically, SSTE uses a self-sampling module to generate some subsets with different degrees of bias from the original training and validation data.
1 code implementation • 6 Jul 2022 • Dugang Liu, Pengxiang Cheng, Hong Zhu, Xing Tang, Yanyu Chen, Xiaoting Wang, Weike Pan, Zhong Ming, Xiuqiang He
Tabular data is one of the most common data storage formats behind many real-world web applications such as retail, banking, and e-commerce.
no code implementations • 21 Jun 2022 • Youlong Ding, Xueyang Wu, Zhitao Li, Zeheng Wu, Shengqi Tan, Qian Xu, Weike Pan, Qiang Yang
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.
no code implementations • 14 May 2022 • Zijie Zeng, Jing Lin, Weike Pan, Zhong Ming, Zhongqi Lu
The common item-based collaborative filtering framework becomes a typical recommendation method when equipped with a certain item-to-item similarity measurement.
no code implementations • 18 Apr 2022 • Ruixuan Liu, Yanlin Wang, Yang Cao, Lingjuan Lyu, Weike Pan, Yun Chen, Hong Chen
Collecting and training over sensitive personal data raise severe privacy concerns in personalized recommendation systems, and federated learning can potentially alleviate the problem by training models over decentralized user data. However, a theoretically private solution in both the training and serving stages of federated recommendation is essential but still lacking. Furthermore, naively applying differential privacy (DP) to the two stages in federated recommendation would fail to achieve a satisfactory trade-off between privacy and utility due to the high-dimensional characteristics of model gradients and hidden representations. In this work, we propose a federated news recommendation method for achieving a better utility in model training and online serving under a DP guarantee. We first clarify the DP definition over behavior data for each round in the life-circle of federated recommendation systems. Next, we propose a privacy-preserving online serving mechanism under this definition based on the idea of decomposing user embeddings with public basic vectors and perturbing the lower-dimensional combination coefficients.
no code implementations • 9 Jul 2014 • Weike Pan
In this paper, we consider the CRAD problem from a transfer learning view, especially on how to achieve knowledge transfer from some auxiliary data.
no code implementations • NeurIPS 2009 • Chonghai Hu, Weike Pan, James T. Kwok
Regularized risk minimization often involves non-smooth optimization, either because of the loss function (e. g., hinge loss) or the regularizer (e. g., $\ell_1$-regularizer).