no code implementations • 2 Feb 2024 • Minglai Shao, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, Qin Tian
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains.
no code implementations • 9 Jan 2024 • Wei Wang, Yujie Lin, Pengjie Ren, Zhumin Chen, Tsunenori Mine, Jianli Zhao, Qiang Zhao, Moyan Zhang, Xianye Ben, YuJun Li
Unlike existing research, we capture collaborative signals of neighbor interaction sequences and directly inject indistinguishable items into the target sequence before the recommendation process begins, thereby increasing the perplexity of the target sequence.
1 code implementation • 9 Nov 2023 • Shuyi Xie, Wenlin Yao, Yong Dai, Shaobo Wang, Donlin Zhou, Lifeng Jin, Xinhua Feng, Pengzhi Wei, Yujie Lin, Zhichao Hu, Dong Yu, Zhengyou Zhang, Jing Nie, Yuhong Liu
We construct a hierarchical task tree encompassing 7 major areas covering over 200 categories and over 800 tasks, which covers diverse capabilities such as question answering, reasoning, multiturn dialogue, and text generation, to evaluate LLMs in a comprehensive and in-depth manner.
no code implementations • 22 Sep 2023 • Yujie Lin, Chen Zhao, Minglai Shao, Baoluo Meng, Xujiang Zhao, Haifeng Chen
This approach effectively separates environmental information and sensitive attributes from the embedded representation of classification features.
no code implementations • 31 Aug 2023 • Yujie Lin, Chen Zhao, Minglai Shao, Xujiang Zhao, Haifeng Chen
In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable.
no code implementations • 1 Jul 2023 • Zekai Chen, Fuyi Wang, Zhiwei Zheng, Ximeng Liu, Yujie Lin
This ensures that Fedward can maintain the performance for the Non-IID scenario.
1 code implementation • 4 Mar 2023 • Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
1 code implementation • 4 Jan 2023 • Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
1 code implementation • 20 May 2020 • Menghan Wang, Yujie Lin, Guli Lin, Keping Yang, Xiao-Ming Wu
Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integrate multi-view data into a single compact representation for each node in the graph.
no code implementations • 22 Oct 2019 • Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, Xiuzhen Cheng
Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module.
no code implementations • 6 Oct 2019 • Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke
We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).
no code implementations • 24 Aug 2019 • Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke
FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.
no code implementations • 23 Jun 2018 • Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke
The generated comments can be regarded as explanations for the recommendation results.