no code implementations • 29 Mar 2024 • Wei Yuan, Chaoqun Yang, Liang Qu, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin
In this paper, we introduce a novel contrastive learning framework designed to fully leverage the client's sparse data through embedding augmentation, referred to as CL4FedRec.
no code implementations • 20 Mar 2024 • Chaoqun Yang, Mengdie Xu, Xiaowei Liang, Zhiguo Shi, Heng Zhang, Xianghui Cao
Furthermore, by means of the labeled multi-Bernoulli (LMB) filter with the proposed augmented LRFSs, the group structure is iteratively propagated and updated during the tracking process, which achieves the simultaneously estimation of the kinetic states, track label, and the corresponding group information of multiple group targets, and further improves the GTT tracking performance.
1 code implementation • 25 Nov 2023 • Wei Yuan, Chaoqun Yang, Liang Qu, Quoc Viet Hung Nguyen, JianXin Li, Hongzhi Yin
Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters.
no code implementations • 20 Oct 2023 • Bowen Hao, Chaoqun Yang, Lei Guo, Junliang Yu, Hongzhi Yin
By unifying pre-training and recommendation tasks as a common motif-based similarity learning task and integrating adaptable prompt parameters to guide the model in downstream recommendation tasks, MOP excels in transferring domain knowledge effectively.
no code implementations • 14 May 2023 • Wei Yuan, Shilong Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin
Therefore, when incorporating visual information in FedRecs, all existing model poisoning attacks' effectiveness becomes questionable.
no code implementations • 24 Apr 2023 • Xuhui Ren, Wei Yuan, Tong Chen, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences.
no code implementations • 26 Jan 2023 • Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin
An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack.
1 code implementation • 27 Sep 2022 • Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Quoc Viet Hung Nguyen, Hongzhi Yin
Specifically, each item is represented by a compositional code that consists of several codewords, and we learn embedding vectors to represent each codeword instead of each item.