no code implementations • 25 Mar 2024 • Xinting Liao, Weiming Liu, Chaochao Chen, Pengyang Zhou, Fengyuan Yu, Huabin Zhu, Binhui Yao, Tao Wang, Xiaolin Zheng, Yanchao Tan
The former indicates that representation collapse in local model will subsequently impact the global model and other local models.
1 code implementation • 22 Feb 2024 • Jiajie Su, Chaochao Chen, Zibin Lin, Xi Li, Weiming Liu, Xiaolin Zheng
To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance.
no code implementations • 23 Nov 2023 • Mengling Hu, Chaochao Chen, Weiming Liu, Xinting Liao, Xiaolin Zheng
The robust short text clustering module aims to train an effective short text clustering model with local data in each client.
no code implementations • 23 Nov 2023 • Mengling Hu, Chaochao Chen, Weiming Liu, Xinyi Zhang, Xinting Liao, Xiaolin Zheng
However, most existing graph clustering methods focus on node-level clustering, i. e., grouping nodes in a single graph into clusters.
no code implementations • 6 Oct 2023 • Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Zhongxuan Han, Dan Meng, Jun Wang
To address the PoT-AU problem in recommender systems, we design a two-component loss function that consists of i) distinguishability loss: making attribute labels indistinguishable from attackers, and ii) regularization loss: preventing drastic changes in the model that result in a negative impact on recommendation performance.
no code implementations • 4 Sep 2023 • Zhongxuan Han, Chaochao Chen, Xiaolin Zheng, Weiming Liu, Jun Wang, Wenjie Cheng, Yuyuan Li
By combining the fairness loss with the original backbone model loss, we address the UOF issue and maintain the overall recommendation performance simultaneously.
no code implementations • 18 Aug 2023 • Haoze Qiu, Fei Zheng, Chaochao Chen, Xiaolin Zheng
As a privacy-preserving method for implementing Vertical Federated Learning, Split Learning has been extensively researched.
no code implementations • 17 Aug 2023 • Xinting Liao, Chaochao Chen, Weiming Liu, Pengyang Zhou, Huabin Zhu, Shuheng Shen, Weiqiang Wang, Mengling Hu, Yanchao Tan, Xiaolin Zheng
In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients optimization toward their local optimums.
no code implementations • 15 Aug 2023 • Xiaolin Zheng, Zhongyu Wang, Chaochao Chen, Feng Zhu, Jiashu Qian
In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships.
no code implementations • 15 Aug 2023 • Xiaolin Zheng, Zhongyu Wang, Chaochao Chen, Jiashu Qian, Yao Yang
The first stage builds a local inner-item hypergraph for each user and a global inter-user graph.
no code implementations • 18 Jul 2023 • Chaochao Chen, Xiaohua Feng, Jun Zhou, Jianwei Yin, Xiaolin Zheng
Large scale language models (LLM) have received significant attention and found diverse applications across various domains, but their development encounters challenges in real-world scenarios.
no code implementations • 7 Jul 2023 • Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Jiaming Zhang
To this end, we propose a novel federated unlearning framework based on incremental learning, which is independent of specific models and federated settings.
no code implementations • 26 Jun 2023 • Xiaolin Zheng, Senci Ying, Fei Zheng, Jianwei Yin, Longfei Zheng, Chaochao Chen, Fengqin Dong
In this work, we propose FedND: federated learning with noise distillation.
1 code implementation • 23 May 2023 • Xiaolin Zheng, Mengling Hu, Weiming Liu, Chaochao Chen, Xinting Liao
To tackle the above issues, we propose a Robust Short Text Clustering (RSTC) model to improve robustness against imbalanced and noisy data.
no code implementations • 11 May 2023 • Xinting Liao, Weiming Liu, Xiaolin Zheng, Binhui Yao, Chaochao Chen
Privacy-preserving cross-domain recommendation (PPCDR) refers to preserving the privacy of users when transferring the knowledge from source domain to target domain for better performance, which is vital for the long-term development of recommender systems.
no code implementations • 20 Apr 2023 • Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang, Biao Gong, Jun Wang
In this paper, we first identify two main disadvantages of directly applying existing unlearning methods in the context of recommendation, i. e., (i) unsatisfactory efficiency for large-scale recommendation models and (ii) destruction of collaboration across users and items.
no code implementations • 24 Oct 2022 • Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen, Linxun Chen, Bing Han
HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs.
no code implementations • 18 Oct 2022 • Fei Zheng, Chaochao Chen, Binhui Yao, Xiaolin Zheng
As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry.
no code implementations • 21 Sep 2022 • Xiaolin Zheng, Jiajie Su, Weiming Liu, Chaochao Chen
However, the short interaction sequences limit the performance of existing SR. To solve this problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this paper, which aims to leverage information from other domains to improve the sequential recommendation performance of a single domain.
no code implementations • 24 May 2022 • Fan Wang, Weiming Liu, Chaochao Chen, Mengying Zhu, Xiaolin Zheng
The ever-increasing data scale of user-item interactions makes it challenging for an effective and efficient recommender system.
no code implementations • 20 May 2022 • Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
no code implementations • 13 May 2022 • Weiming Liu, Xiaolin Zheng, Mengling Hu, Chaochao Chen
Most of the existing CDR models assume that both the source and target domains share the same overlapped user set for knowledge transfer.
no code implementations • 19 Apr 2022 • Yanchao Tan, Carl Yang Member, Xiangyu Wei, Ziyue Wu, Xiaolin Zheng
The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks.
no code implementations • 22 Mar 2022 • Yuyuan Li, Xiaolin Zheng, Chaochao Chen, Junlin Liu
The basic idea of most recommender systems is collaborative filtering, but existing MU methods ignore the collaborative information across users and items.
no code implementations • 10 Feb 2022 • Weiming Liu, Xiaolin Zheng, Mengling Hu, Chaochao Chen
In this paper, we focus on the Review-based Non-overlapped Recommendation (RNCDR) problem.
no code implementations • 10 Feb 2022 • Chaochao Chen, Huiwen Wu, Jiajie Su, Lingjuan Lyu, Xiaolin Zheng, Li Wang
To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.
no code implementations • NeurIPS 2021 • Weiming Liu, Jiajie Su, Chaochao Chen, Xiaolin Zheng
To address this issue, we propose DisAlign, a cross-domain recommendation framework for the CDCSR problem, which utilizes both rating and auxiliary representations from the source domain to improve the recommendation performance of the target domain.
no code implementations • Findings (EMNLP) 2021 • Ke Wang, Yangbin Shi, Jiayi Wang, Yuqi Zhang, Yu Zhao, Xiaolin Zheng
Quality Estimation (QE) plays an essential role in applications of Machine Translation (MT).
1 code implementation • 17 Aug 2021 • Fei Zheng, Chaochao Chen, Xiaolin Zheng, Mingjie Zhu
Since our method reduces the cost for element-wise function computation, it is more efficient than existing cryptographic methods.
1 code implementation • 27 Mar 2021 • Yanchao Tan, Carl Yang, Xiangyu Wei, Yun Ma, Xiaolin Zheng
Metric learning has been proposed to capture user-item interactions from implicit feedback, but existing methods only represent users and items in a single metric space, ignoring the fact that users can have multiple preferences and items can have multiple properties, which leads to potential conflicts limiting their performance in recommendation.
no code implementations • 17 Dec 2020 • Jun Zhou, Longfei Zheng, Chaochao Chen, Yan Wang, Xiaolin Zheng, Bingzhe Wu, Cen Chen, Li Wang, Jianwei Yin
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
no code implementations • 25 May 2020 • Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
no code implementations • 5 Mar 2020 • Chaochao Chen, Jun Zhou, Bingzhe Wu, Wenjin Fang, Li Wang, Yuan Qi, Xiaolin Zheng
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
no code implementations • 5 Mar 2020 • Chaochao Chen, Kevin C. Chang, Qibing Li, Xiaolin Zheng
The proposed CGM is a combination of Bayesian network and Markov random field.
no code implementations • 13 Nov 2019 • Mengying Zhu, Xiaolin Zheng, Yan Wang, Yuyuan Li, Qianqiao Liang
Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods.
no code implementations • ICLR 2019 • Qibing Li, Xiaolin Zheng
Inspired by the prediction error minimization (PEM) and embodied cognition, we propose a simple architecture to augment reward, namely Imagination Reconstruction Network (IRN).
no code implementations • NeurIPS 2018 • Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang
Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.
no code implementations • 26 Aug 2018 • Xiaolin Zheng, Mengying Zhu, Qibing Li, Chaochao Chen, Yanchao Tan
Artificial intelligence (AI) is the core technology of technological revolution and industrial transformation.
no code implementations • 25 Dec 2017 • Qibing Li, Xiaolin Zheng, Xinyue Wu
Second, due to the difficulty on training deep neural networks, existing explicit models do not fully exploit the expressive potential of deep learning.
no code implementations • 30 Nov 2017 • Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang
We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality.