no code implementations • 5 Mar 2024 • Dong Yao, Asaad Alghamdi, Qingrong Xia, Xiaoye Qu, Xinyu Duan, Zhefeng Wang, Yi Zheng, Baoxing Huai, Peilun Cheng, Zhou Zhao
Although DC-Match is a simple yet effective method for semantic matching, it highly depends on the external NER techniques to identify the keywords of sentences, which limits the performance of semantic matching for minor languages since satisfactory NER tools are usually hard to obtain.
no code implementations • 3 May 2023 • Dong Yao, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Wenqiao Zhang, Rui Zhang, Xiaofei He, Fei Wu
In contrast, modalities that do not cause users' behaviors are potential noises and might mislead the learning of a recommendation model.
no code implementations • 17 Aug 2022 • Shengyu Zhang, Bofang Li, Dong Yao, Fuli Feng, Jieming Zhu, Wenyan Fan, Zhou Zhao, Xiaofei He, Tat-Seng Chua, Fei Wu
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e. g., popular items) or even weird ones that are far beyond users' interests.
1 code implementation • 17 Aug 2022 • Shengyu Zhang, Lingxiao Yang, Dong Yao, Yujie Lu, Fuli Feng, Zhou Zhao, Tat-Seng Chua, Fei Wu
Specifically, Re4 encapsulates three backward flows, i. e., 1) Re-contrast, which drives each interest embedding to be distinct from other interests using contrastive learning; 2) Re-attend, which ensures the interest-item correlation estimation in the forward flow to be consistent with the criterion used in final recommendation; and 3) Re-construct, which ensures that each interest embedding can semantically reflect the information of representative items that relate to the corresponding interest.
no code implementations • 17 Mar 2022 • Dong Yao, Zhou Zhao, Shengyu Zhang, Jieming Zhu, Yudong Zhu, Rui Zhang, Xiuqiang He
We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.
no code implementations • 11 Sep 2021 • Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, Fei Wu
In this paper, we propose to learn accurate and robust user representations, which are required to be less sensitive to (attack on) noisy behaviors and trust more on the indispensable ones, by modeling counterfactual data distribution.
no code implementations • 1 Apr 2021 • Dong Yao, Shengyu Zhang, Zhou Zhao, Wenyan Fan, Jieming Zhu, Xiuqiang He, Fei Wu
Personalized recommendation system has become pervasive in various video platform.