no code implementations • 29 Aug 2023 • Meng Yuan, Fuzhen Zhuang, Zhao Zhang, Deqing Wang, Jin Dong
Specifically, in hyperbolic space, we set smaller margins in the area near to the origin, which is conducive to distinguishing between highly similar positive items and negative ones.
1 code implementation • 10 Aug 2023 • Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang
Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions.
1 code implementation • 4 Aug 2023 • Tao Zou, Le Yu, Leilei Sun, Bowen Du, Deqing Wang, Fuzhen Zhuang
Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives.
1 code implementation • 31 Jul 2023 • Ting Jiang, Shaohan Huang, Zhongzhi Luan, Deqing Wang, Fuzhen Zhuang
We also fine-tune LLMs with current contrastive learning approach, and the 2. 7B OPT model, incorporating our prompt-based method, surpasses the performance of 4. 8B ST5, achieving the new state-of-the-art results on STS tasks.
Ranked #1 on
Semantic Textual Similarity
on STS16
no code implementations • 7 Jun 2023 • Yuting Zhang, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang, Ruidong Han, Xiang Li, Wei Lin, Zhulin An, Yongjun Xu
Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling.
no code implementations • 27 May 2023 • Hao Geng, Deqing Wang, Fuzhen Zhuang, Xuehua Ming, Chenguang Du, Ting Jiang, Haolong Guo, Rui Liu
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
1 code implementation • 18 May 2023 • Chenguang Du, Kaichun Yao, HengShu Zhu, Deqing Wang, Fuzhen Zhuang, Hui Xiong
However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing.
no code implementations • 6 May 2023 • Yiqing Wu, Ruobing Xie, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Jie zhou, Yongjun Xu, Qing He
Recently, a series of pioneer studies have shown the potency of pre-trained models in sequential recommendation, illuminating the path of building an omniscient unified pre-trained recommendation model for different downstream recommendation tasks.
1 code implementation • 28 Apr 2023 • Hanwen Du, Huanhuan Yuan, Pengpeng Zhao, Fuzhen Zhuang, Guanfeng Liu, Lei Zhao, Victor S. Sheng
Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks.
1 code implementation • 18 Apr 2023 • Xinyu Du, Huanhuan Yuan, Pengpeng Zhao, Jianfeng Qu, Fuzhen Zhuang, Guanfeng Liu, Victor S. Sheng
However, many recent studies represent that current self-attention based models are low-pass filters and are inadequate to capture high-frequency information.
1 code implementation • 16 Apr 2023 • Xiuyuan Qin, Huanhuan Yuan, Pengpeng Zhao, Junhua Fang, Fuzhen Zhuang, Guanfeng Liu, Victor Sheng
By applying both data augmentation and learnable model augmentation operations, this work innovates the standard CL framework by contrasting data and model augmented views for adaptively capturing the informative features hidden in stochastic data augmentation.
no code implementations • 21 Mar 2023 • Huishi Luo, Fuzhen Zhuang, Ruobing Xie, HengShu Zhu, Deqing Wang
Recently, causal inference has attracted increasing attention from researchers of recommender systems (RS), which analyzes the relationship between a cause and its effect and has a wide range of real-world applications in multiple fields.
no code implementations • 17 Dec 2022 • Shaopeng Wei, Yu Zhao, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou
Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
no code implementations • 28 Nov 2022 • Yu Zhao, Huaming Du, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou
In contrast, this paper attempts to provide a systematic literature survey of enterprise risk analysis approaches from Big Data perspective, which reviews more than 250 representative articles in the past almost 50 years (from 1968 to 2023).
1 code implementation • 20 Nov 2022 • Ziming Wan, Deqing Wang, Xuehua Ming, Fuzhen Zhuang, Chenguang Du, Ting Jiang, Zhengyang Zhao
To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning.
1 code implementation • The 31st ACM International Conference on Information and Knowledge Management (CIKM) 2022 • Fuwei Zhang, Zhao Zhang, Xiang Ao, Fuzhen Zhuang, Yongjun Xu, Qing He.
In order to represent the facts happening in a specific time, temporal knowledge graph (TKG) embedding approaches are put forward.
Ranked #1 on
Link Prediction
on GDELT
1 code implementation • 12 Oct 2022 • Ting Jiang, Deqing Wang, Fuzhen Zhuang, Ruobing Xie, Feng Xia
These methods, such as movement pruning, use first-order information to prune PLMs while fine-tuning the remaining weights.
1 code implementation • COLING 2022 • Wei Chen, Jinglong Du, Zhao Zhang, Fuzhen Zhuang, Zhongshi He
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification).
2 code implementations • 23 Jul 2022 • Chuanguang Yang, Zhulin An, Helong Zhou, Fuzhen Zhuang, Yongjun Xu, Qian Zhan
This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks.
no code implementations • 30 Jun 2022 • Shuokai Li, Yongchun Zhu, Ruobing Xie, Zhenwei Tang, Zhao Zhang, Fuzhen Zhuang, Qing He, Hui Xiong
In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context.
1 code implementation • 26 Jun 2022 • Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, Fuzhen Zhuang
In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M$^3$FEND) to address these two challenges.
no code implementations • 29 May 2022 • Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
no code implementations • 19 May 2022 • Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xu Zhang, Leyu Lin, Qing He
Specifically, we build the personalized soft prefix prompt via a prompt generator based on user profiles and enable a sufficient training of prompts via a prompt-oriented contrastive learning with both prompt- and behavior-based augmentations.
no code implementations • 19 May 2022 • Yuanbo Xu, Yongjian Yang, En Wang, Fuzhen Zhuang, Hui Xiong
2) the PMU detection model should take both ratings and reviews into consideration, which makes PMU detection a multi-modal problem.
1 code implementation • 10 May 2022 • Yiqing Wu, Ruobing Xie, Yongchun Zhu, Fuzhen Zhuang, Xiang Ao, Xu Zhang, Leyu Lin, Qing He
In this work, we define the selective fairness task, where users can flexibly choose which sensitive attributes should the recommendation model be bias-free.
1 code implementation • 5 May 2022 • Ting Jiang, Deqing Wang, Leilei Sun, Zhongzhi Chen, Fuzhen Zhuang, Qinghong Yang
Existing methods encode label hierarchy in a global view, where label hierarchy is treated as the static hierarchical structure containing all labels.
Multi Label Text Classification
Multi-Label Text Classification
+1
no code implementations • 2 May 2022 • Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
1 code implementation • 20 Apr 2022 • Yongchun Zhu, Qiang Sheng, Juan Cao, Shuokai Li, Danding Wang, Fuzhen Zhuang
In this paper, we propose an entity debiasing framework (\textbf{ENDEF}) which generalizes fake news detection models to the future data by mitigating entity bias from a cause-effect perspective.
1 code implementation • 20 Apr 2022 • Shuokai Li, Ruobing Xie, Yongchun Zhu, Xiang Ao, Fuzhen Zhuang, Qing He
In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS.
Ranked #3 on
Recommendation Systems
on ReDial
1 code implementation • 20 Mar 2022 • Yiqing Wu, Ruobing Xie, Yongchun Zhu, Xiang Ao, Xin Chen, Xu Zhang, Fuzhen Zhuang, Leyu Lin, Qing He
We argue that MBR models should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user.
1 code implementation • 1 Feb 2022 • Yu Zhao, Shaopeng Wei, Yu Guo, Qing Yang, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou
This study for the first time considers both types of risk and their joint effects in bankruptcy prediction.
1 code implementation • 12 Jan 2022 • Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang
We propose PromptBERT, a novel contrastive learning method for learning better sentence representation.
1 code implementation • 11 Jan 2022 • Yu Zhao, Huaming Du, Ying Liu, Shaopeng Wei, Xingyan Chen, Fuzhen Zhuang, Qing Li, Ji Liu, Gang Kou
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets.
no code implementations • 4 Jan 2022 • Yongchun Zhu, Dongbo Xi, Bowen Song, Fuzhen Zhuang, Shuai Chen, Xi Gu, Qing He
Thus, in this paper, we further propose a transfer framework to tackle the cross-domain fraud detection problem, which aims to transfer knowledge from existing domains (source domains) with enough and mature data to improve the performance in the new domain (target domain).
1 code implementation • 4 Jan 2022 • Yongchun Zhu, Fuzhen Zhuang, Deqing Wang
However, in the practical scenario, labeled data can be typically collected from multiple diverse sources, and they might be different not only from the target domain but also from each other.
Image Classification
Multi-Source Unsupervised Domain Adaptation
+1
1 code implementation • 4 Jan 2022 • Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He
Based on this, we present Multi-Representation Adaptation Network (MRAN) to accomplish the cross-domain image classification task via multi-representation alignment which can capture the information from different aspects.
no code implementations • 31 Dec 2021 • Dongbo Xi, Fuzhen Zhuang, Bowen Song, Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance.
no code implementations • 31 Dec 2021 • Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, Jingjing Gu, Hui Xiong, Qing He
Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.
no code implementations • 31 Dec 2021 • Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou, Xiaohu Cheng, Fen Lin, Qing He
Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target domain to improve the performance in the target domain via reducing the shift between the data distributions.
no code implementations • 31 Dec 2021 • Dongbo Xi, Fuzhen Zhuang, Yanchi Liu, HengShu Zhu, Pengpeng Zhao, Chang Tan, Qing He
To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users.
no code implementations • 27 Dec 2021 • Fuwei Zhang, Zhao Zhang, Xiang Ao, Dehong Gao, Fuzhen Zhuang, Yi Wei, Qing He
The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.
1 code implementation • 24 Dec 2021 • Yu Zhao, Shaopeng Wei, Huaming Du, Xingyan Chen, Qing Li, Fuzhen Zhuang, Ji Liu, Gang Kou
To address this issue, we propose a novel Dual Hierarchical Attention Networks (DHAN) based on the bi-typed multi-relational heterogeneous graphs to learn comprehensive node representations with the intra-class and inter-class attention-based encoder under a hierarchical mechanism.
1 code implementation • NeurIPS 2021 • Ying Sun, HengShu Zhu, Chuan Qin, Fuzhen Zhuang, Qing He, Hui Xiong
To this end, in this paper, we aim to discern the decision-making processes of neural networks through a hierarchical voting strategy by developing an explainable deep learning model, namely Voting Transformation-based Explainable Neural Network (VOTEN).
no code implementations • 1 Dec 2021 • Denghui Zhang, Zixuan Yuan, Yanchi Liu, Hao liu, Fuzhen Zhuang, Hui Xiong, Haifeng Chen
Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.
no code implementations • 11 Nov 2021 • Zhao Zhang, Fuzhen Zhuang, HengShu Zhu, Chao Li, Hui Xiong, Qing He, Yongjun Xu
This will lead to low-quality and unreliable representations of KGs.
no code implementations • 29 Oct 2021 • Yu Zhao, Jia Song, Huali Feng, Fuzhen Zhuang, Qing Li, Xiaojie Wang, Ji Liu
Keyphrase provides accurate information of document content that is highly compact, concise, full of meanings, and widely used for discourse comprehension, organization, and text retrieval.
1 code implementation • 21 Oct 2021 • Yongchun Zhu, Zhenwei Tang, Yudan Liu, Fuzhen Zhuang, Ruobing Xie, Xu Zhang, Leyu Lin, Qing He
Specifically, a meta network fed with users' characteristic embeddings is learned to generate personalized bridge functions to achieve personalized transfer of preferences for each user.
1 code implementation • 21 Oct 2021 • Ting Jiang, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Liangjie Zhang, Qi Zhang
While pre-trained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into non-autoregressive generation tasks remains a challenge.
1 code implementation • 17 Jun 2021 • Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He
The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.
no code implementations • 16 Jun 2021 • Hao Chen, Fuzhen Zhuang, Li Xiao, Ling Ma, Haiyan Liu, Ruifang Zhang, Huiqin Jiang, Qing He
The encoder can automatically construct the population graph using phenotypic measures which have a positive impact on the final results, and further realizes the fusion of multimodal information.
3 code implementations • 31 May 2021 • Yongchun Zhu, Yudan Liu, Ruobing Xie, Fuzhen Zhuang, Xiaobo Hao, Kaikai Ge, Xu Zhang, Leyu Lin, Juan Cao
Besides, MetaHeac has been successfully deployed in WeChat for the promotion of both contents and advertisements, leading to great improvement in the quality of marketing.
3 code implementations • 18 May 2021 • Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, Yu Chen
While considerable multi-task efforts have been made in this direction, a long-standing challenge is how to explicitly model the long-path sequential dependence among audience multi-step conversions for improving the end-to-end conversion.
no code implementations • 11 May 2021 • Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang, Ruobing Xie, Dongbo Xi, Xu Zhang, Leyu Lin, Qing He
With the advantage of meta learning which has good generalization ability to novel tasks, we propose a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta stage.
no code implementations • 11 May 2021 • Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang, Kaikai Ge, Ying Sun, Xu Zhang, Leyu Lin, Juan Cao
The cold item ID embedding has two main problems: (1) A gap is existing between the cold ID embedding and the deep model.
no code implementations • 27 Jan 2021 • Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang, Zhiyuan Qi, Zhiping Shi, Juan Cao, Qing He
However, in real-world applications, few-shot learning paradigm often suffers from data shift, i. e., samples in different tasks, even in the same task, could be drawn from various data distributions.
no code implementations • 18 Jan 2021 • Qiang Zhou, Jingjing Gu, Xinjiang Lu, Fuzhen Zhuang, Yanchao Zhao, Qiuhong Wang, Xiao Zhang
Intuitively, the potential crowd flow of the new coming site can be implied by exploring the nearby sites.
1 code implementation • 9 Jan 2021 • Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, Fuzhen Zhuang
In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels.
no code implementations • 7 Sep 2020 • Denghui Zhang, Zixuan Yuan, Yanchi Liu, Fuzhen Zhuang, Haifeng Chen, Hui Xiong
Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.
no code implementations • 8 Aug 2020 • Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He
In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.
no code implementations • 21 Jul 2020 • Jingbo Zhou, Zhenwei Tang, Min Zhao, Xiang Ge, Fuzhen Zhuang, Meng Zhou, Liming Zou, Chenglei Yang, Hui Xiong
A mobile app interface usually consists of a set of user interface modules.
no code implementations • 12 Jul 2020 • Dongbo Xi, Fuzhen Zhuang, Yongchun Zhu, Pengpeng Zhao, Xiangliang Zhang, Qing He
In this paper, we propose a Graph Factorization Machine (GFM) which utilizes the popular Factorization Machine to aggregate multi-order interactions from neighborhood for recommendation.
1 code implementation • 13 Apr 2020 • Jingjing Gu, Qiang Zhou, Jingyuan Yang, Yanchi Liu, Fuzhen Zhuang, Yanchao Zhao, Hui Xiong
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.
no code implementations • 28 Feb 2020 • Qingyu Guo, Fuzhen Zhuang, Chuan Qin, HengShu Zhu, Xing Xie, Hui Xiong, Qing He
On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation.
2 code implementations • 20 Nov 2019 • Fuzhen Zhuang, Keyu Duan, Tongjia Guo, Yongchun Zhu, Dongbo Xi, Zhiyuan Qi, Qing He
The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function.
3 code implementations • 7 Nov 2019 • Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, HengShu Zhu, Hui Xiong, Qing He
In order to show the performance of different transfer learning models, over twenty representative transfer learning models are used for experiments.
no code implementations • 11 Oct 2019 • Changying Du, Jia He, Changde Du, Fuzhen Zhuang, Qing He, Guoping Long
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning.
no code implementations • 10 Oct 2019 • Changying Du, Fuzhen Zhuang, Jia He, Qing He, Guoping Long
In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data.
no code implementations • EMNLP 2018 • Zhao Zhang, Fuzhen Zhuang, Meng Qu, Fen Lin, Qing He
To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.
1 code implementation • 16 Sep 2018 • Baoyu Jing, Chenwei Lu, Deqing Wang, Fuzhen Zhuang, Cheng Niu
To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA).
no code implementations • 18 Mar 2018 • Juanying Xie, Qi Hou, Yinghuan Shi, Lv Peng, Liping Jing, Fuzhen Zhuang, Junping Zhang, Xiaoyang Tang, Shengquan Xu
We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment.
no code implementations • 12 Feb 2018 • Feiyang Pan, Qingpeng Cai, Pingzhong Tang, Fuzhen Zhuang, Qing He
We evaluate PGCR on toy datasets as well as a real-world dataset of personalized music recommendations.