检索结果-内蒙古大学图书馆

Dual Test-Time Training for Out-of-Distribution Recommender System

IEEE Transactions on Knowledge and data Engineering 2025年第6期37卷 3312-3326页

作者： Yang, Xihong Wang, Yiqi Chen, Jin Fan, Wenqi Zhao, Xiangyu Zhu, En Liu, Xinwang Lian, Defu National University of Defense Technology College of Computer Science and Technology Changsha410073 China The Hong Kong University of Science and Technology School of Business and Management Hong Kong Hong Kong City University of Hong Kong Department of Data Science Hong Kong University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology Hefei Anhui230000 China

Deep learning has been widely applied in recommender systems, which has recently achieved revolutionary progress. However, most existing learning-based methods assume that the user and item distributions remain unchanged between the training phase and the test phase. However, the distribution of user and item features can naturally shift in real-world scenarios, potentially resulting in a substantial decrease in recommendation performance. This phenomenon can be formulated as an Out-Of-Distribution (OOD) recommendation problem. To address this challenge, we propose a novel Dual Test-Time-Training framework for OOD Recommendation, termed DT3OR. In DT3OR, we incorporate a model adaptation mechanism during the test-time phase to carefully update the recommendation model, allowing the model to adapt specially to the shifting user and item features. To be specific, we propose a self-distillation task and a contrastive task to assist the model learning both the user's invariant interest preferences and the variant user/item characteristics during the test-time phase, thus facilitating a smooth adaptation to the shifting features. Furthermore, we provide theoretical analysis to support the rationale behind our dual test-time training framework. To the best of our knowledge, this paper is the first work to address OOD recommendation via a test-time-training strategy. We conduct experiments on five datasets with various backbones. Comprehensive experimental results have demonstrated the effectiveness of DT3OR compared to other state-of-the-art baselines. © 2025 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Weighting Push-SUM for Decentralized Optimization with Statistical Diversity

引用

IEEE Transactions on Control of Network Systems 2025年

作者： Zhou, Yiming Cheng, Yifei Xu, Linli Chen, Enhong University of Science and Technology of China (USTC) Anhui Province Key Laboratory of Big Data Analysis and Application Hefei China

Statistical diversity is a property of data distribution and can hinder the optimization of a decentralized network. However, the theoretical limitations of the Push-SUM protocol reduce the performance in handling the statistical diversity of optimization algorithms based on it. In this paper, we theoretically and empirically reduce the negative impact of statistical diversity on decentralized optimization using the Push-SUM protocol. Specifically, we propose the Adaptive Weighting Push-SUM protocol, a theoretical generalization of the original Push-SUM protocol where the latter is a special case of the former. Our theoretical analysis shows that, with sufficient communication, the upper bound on the consensus distance for the new protocol reduces to O(1N), whereas it remains at O(1) for the Push-SUM protocol. We adopt SGD and Momentum SGD on the new protocol and prove that the convergence rate of these two algorithms to statistical diversity is O(NT) on the new protocol, while it is O(NdT) on the Push-SUM protocol, where d is the parameter size of the training model. To address statistical diversity in practical applications of the new protocol, we develop the Moreau weighting method for its generalized weight matrix definition. This method, derived from the Moreau envelope, is an approximate optimization of the distance penalty of the Moreau envelope. We verify that the Adaptive Weighting Push-SUM protocol is practically more efficient than the Push-SUM protocol via deep learning experiments. © 2014 IEEE.

关键词： communication networks Directed decentralized optimization distributed algorithms/control learning optimization

来源：评论

学校读者我要写书评

暂无评论

Understanding the mechanism of social tie in the propagation process of social network with communication channel

引用

Frontiers of Computer Science 2019年第6期13卷 1296-1308页

作者： Kai LI Guangyi LV Zhefeng WANG Qi LIU Enhong CHEN Lisheng QIAO Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China

The propagation of information in online social networks plays a critical role in modern life,and thus has been studied *** have proposed a series of propagation models,generally,which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his ***,the research on the mechanism how social ties between users play roles in propagation process is still ***,comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works,so that the existing models failed to clearly describe the process a user be activated by a *** this end,in this paper,we analyze the close correspondence between social tie in propagation process and communication channel,thus we propose to exploit the communication channel to describe the information propagation process between users,and design a social tie channel(STC)*** model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference,and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social *** experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.

关键词： information propagation social networks mechanism of social tie communication channel

来源：评论

学校读者我要写书评

暂无评论

Exploiting Structural and Temporal Influence for Dynamic Social-Aware Recommendation

引用

Journal of Computer Science & Technology 2020年第2期35卷 281-294页

作者： Yang Liu Zhi Li Wei Huang Tong Xu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027China

Recent years have witnessed the rapid development of online social platforms,which effectively support the business intelligence and provide services for massive *** this line,large efforts have been made on the socialaware recommendation task,i.e.,leveraging social contextual information to improve recommendation *** existing methods have treated social relations in a static way,but the dynamic influence of social contextual information on users'consumption choices has been largely *** that end,in this paper,we conduct a comprehensive study to reveal the dynamic social influence on users'preferences,and then we propose a deep model called Dynamic Social-Aware Recommender System(DSRS)to integrate the users'structural and temporal social contexts to address the dynamic socialaware recommendation *** consists of two main components,i.e.,the social influence learning(SIL)and dynamic preference learning(DPL).Specifically,in the SIL module,we arrange social graphs in a sequential order and borrow the power of graph convolution networks(GCNs)to learn social ***,we design a structural-temporal attention mechanism to discriminatively model the structural social influence and the temporal social ***,in the DPL part,users'individual preferences are learned dynamically by recurrent neural networks(RNNs).Finally,with a prediction layer,we combine the users'social context and dynamic preferences to generate *** conduct extensive experiments on two real-world datasets,and the experimental results demonstrate the superiority and effectiveness of our proposed model compared with the state-of-the-art methods.

关键词： recommender system social influence sequential recommendation

来源：评论

学校读者我要写书评

暂无评论

HACAN:a hierarchical answer-aware and context-aware network for question generation

引用

Frontiers of Computer Science 2024年第5期18卷 45-55页

作者： Ruijun SUN Hanqin TAO Yanmin CHEN Qi LIU Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei230027China

Question Generation(QG)is the task of generating questions according to the given *** of the existing methods are based on Recurrent Neural Networks(RNNs)for generating questions with passage-level input for providing more details,which seriously suffer from such problems as gradient vanishing and ineffective information *** fact,reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education *** that end,in this paper,we propose a novel Hierarchical Answer-Aware and Context-Aware Network(HACAN)to construct a high-quality passage representation and judge the balance between the sentences and the whole ***,a Hierarchical Passage Encoder(HPE)is proposed to construct an answer-aware and context-aware passage representation,with a strategy of utilizing multi-hop ***,we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder(HPD)which determines when to utilize the passage *** conduct extensive experiments on the SQuAD dataset,where the results verify the effectivenesss of our model in comparison with several baselines.

关键词： question generation natural language generation natural language processing sequence to sequence

来源：评论

学校读者我要写书评

暂无评论

Study on Information Diffusion analysis in Social Networks and Its applications

引用

International Journal of Automation and computing 2018年第4期15卷 377-401页

作者： Biao Chang Tong Xu Qi Liu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer ScienceUniversity of Science and Technology of China

Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximiza- tion and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffu- sion analysis are listed for further study.

关键词： Information diffusion influence evaluation influence maximization information source detection social network.

来源：评论

学校读者我要写书评

暂无评论

Towards Exploring Large Molecular Space:An Efficient Chemical Genetic Algorithm

引用

Journal of Computer Science & Technology 2022年第6期37卷 1464-1477页

作者： Jian-Fu Zhu Zhong-Kai Hao Qi Liu Yu Yin Cheng-Qiang Lu Zhen-Ya Huang En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China

Generating molecules with desired properties is an important task in chemistry and *** efficient method may have a positive impact on finding drugs to treat diseases like *** mining and artificial intelligence may be good ways to find an efficient ***,both the generative models based on deep learning and the work based on genetic algorithms have made some progress in generating molecules and optimizing the molecule’s ***,existing methods have defects in the experimental evaluation *** methods also need to be improved in efficiency and *** solve these problems,we propose a method named the Chemical Genetic Algorithm for Large Molecular Space(CALM).Specifically,CALM employs a scalable and efficient molecular representation called molecular *** we design corresponding crossover,mutation,and mask operators inspired by domain knowledge and previous *** apply our genetic algorithm to several tasks related to molecular property optimization and constraint molecular *** results of these tasks show that our approach outperforms the other state-of-the-art deep learning and genetic algorithm methods,where the z tests performed on the results of several experiments show that our method is more than 99%likely to be *** the same time,based on the experimental results,we point out the defects in the experimental evaluation standard which affects the fair evaluation of all previous *** these defects helps to objectively evaluate the performance of all work.

关键词： data mining molecular generation genetic algorithm drug discovery artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Maximizing the effect of information adoption: A general framework

Maximizing the effect of information adoption: A general fra...

引用

2018 SIAM International Conference on data Mining, SDM 2018

作者： Jin, Tianyuan Xu, Tong Zhong, Hui Chen, Enhong Wang, Zhefeng Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China China

With the development of social networking services, social influence analyses, as well as the influence maximization tasks, have attracted wide attention in both academia and industry. Traditional studies mainly focus on simulating process of influence spread. However, two basic functions of social spread, i.e., information propagation and information adoption have not been clearly distinguished. Usually, as information adoption could be even more significant for information publishers in application scenarios, more comprehensive analysis for effect of adoption is urgently required. To that end, in this paper, we propose a novel framework to generally describe social spread, in which information adoption process is separately formulated as random events. Along this line, when we apply this framework to the information adoption maximization task, with proving that the adoption maximization problem is NP-hard and submodular, we further design a polling-based algorithm to achieve an effective approximation. Extensive experiments on four real-world data sets demonstrate the effectiveness and efficiency of proposed algorithms, which validates that our approach could better summarize the complete social spread process, and further support the necessity of distinguishing information adoption from information propagation. © 2018 by SIAM.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

A general tail item representation enhancement framework for sequential recommendation

引用

Frontiers of Computer Science 2024年第6期18卷 137-148页

作者： Mingyue CHENG Qi LIU Wenyu ZHANG Zhiding LIU Hongke ZHAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China&State Key Laboratory of Cognitive IntelligenceHefei 230026China College of Management and Economics Tianjin UniversityTianjin 300072China

Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient ***,highly skewed long-tail distribution is very common in recommender ***,in this paper,we focus on enhancing the representation of tail items to improve sequential recommendation *** empirical studies on benchmarks,we surprisingly observe that both the ranking performance and training procedure are greatly hindered by the poorly optimized tail item *** address this issue,we propose a sequential recommendation framework named TailRec that enables contextual information of tail item well-leveraged and greatly improves its corresponding *** the characteristics of the sequential recommendation task,the surrounding interaction records of each tail item are regarded as contextual information without leveraging any additional side *** approach allows for the mining of contextual information from cross-sequence behaviors to boost the performance of sequential *** a light contextual filtering component is plug-and-play for a series of SRS *** verify the effectiveness of the proposed TailRec,we conduct extensive experiments over several popular benchmark *** experimental results demonstrate that TailRec can greatly improve the recommendation results and speed up the training *** codes of our methods have been available.

关键词： sequential recommendation long-tail distribution training accelerating

来源：评论

学校读者我要写书评

暂无评论

GL-BKGNN: Graphlet-based Bi-Kernel Interpretable Graph Neural Networks

引用

Information Fusion 2025年

作者： Lixiang Xu Kang Jiang Xin Niu Enhong Chen Bin Luo Philip S. Yu School of Artificial Intelligence Hefei Institute of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Hefei China Department of Computer Science University of Illinois at Chicago Chicago IL USA

While graph neural networks (GNNs) have successfully applied generalized convolution operations to the graph domain, providing a direct method to explain the dependency between the output and the presence of certain features and structural patterns in the input graph remains challenging. Inspired by image filters in standard convolutional neural networks (CNN), we propose a neural framework that connects bi-kernel with GNNs, incorporating predefined rules and focusing on the interpretability of graph filters during training. We address graph kernels based on their differentiability, using backpropagation of differentiable graph kernels for end-to-end training to generate gradient information for model optimization. Simultaneously, we leverage the stronger stability of non-differentiable graph kernels to capture local critical subgraphs, achieving deep fusion of structural features for node updates. Extensive experiments demonstrate that our model achieves competitive performance on graph classification datasets while providing additional benefits in interpretability.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：