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Making Non-Overlapping Matters: An Unsupervised Alignment Enhanced Cross-Domain Cold-Start Recommendation

IEEE TRANSACTIONS ON knowledge AND data ENGINEERING 2025年第4期37卷 2001-2014页

作者： Wang, Zihan Yang, Yonghui Wu, Le Hong, Richang Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei 230002 Peoples R China

Cold-start recommendation is a long-standing challenge when presenting potential preferred items to new users. Most empirical studies leverage side information to promote cold-start recommendation. In this work, we focus on cross-domain cold-start recommendation, which aims to provide suggestions to those non-overlapping users who have only interacted in the source domain and are viewed as new users in the target domain. Pre-training and then mapping is the common solution for the cross-domain cold-start recommendation. The former learns domain-specific user preference, and the latter transfers preference knowledge from the source to the target domain. Despite the effectiveness, we argue that current mapping-based methods still have the following limitations. First, current mapping functions fail to fully consider the similarity of user behavioral patterns, either common transfer or personalized transfer mappings. Second, sparse supervision signals from the limited overlapping users, lead to insufficient mapping function learning for recommendation. To tackle the above limitations, we propose a novel MACDR model for cross-domain cold-start recommendation. Specifically, MACDR consists of two elaborate modules: a Prototype enhanced Mixture-Of-Experts (PMOE) based mapping function and a Preference Distribution Alignment (PDA) enhanced optimization. PMOE is designed to balance the transfer patterns of common and personalized preferences, following the basis that similar users share similar preference transfer. Furthermore, to alleviate the sparse supervision issue, PDA is designed to explore the utilization of non-overlapping users in an unsupervised manner based on the prototype distribution alignment technique. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed method.

关键词： Prototypes Training Motion pictures Optimization Itemsets Lapping Generators data models Correlation Unsupervised learning Cross-domain recommendation cold-start recommendation transfer learning mixture-of-experts unsupervised learning preference distribution alignment

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WhisPrompt: Audio classification of Chinese opera genres by transferring time-series features

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 277卷

作者： Shi, Bin Wang, Hao Qiu, Jingwen Li, Xiaomin Nanjing Univ Sch Informat Management Nanjing 210023 Peoples R China Jiangsu Key Lab Data Engn & Knowledge Serv Nanjing 210023 Peoples R China

As one of the most famous intangible cultural heritages (ICHs) in China, the art of Chinese opera has a long history and profound cultural connotation, and is of great research value. The identification and archiving of opera data in audio modal is a key for digitization and knowledge mining on this precious ICH. In current studies, this work has been hampered by two "inconsistencies": between audio features and current classification models' representation, and between upstream and downstream tasks in pre-trained audio models. To address these problems, we propose WhisPrompt by transferring time-series features and apply WhisPrompt to Chinese Opera Genres dataset (COG). We contribute (1) a novel audio classification model WhisPrompt, guiding the pre-trained ASR model to achieve cross-task transfer learning via the Prompt paradigm, and experiments show that our model can better extract features from audio data, with a 4.50% improvement compared to the SOTA baseline;(2) an audio data mining perspective based on interpretability analysis, finding that WhisPrompt's focus when characterizing opera audio is consistent with human cognition, demonstrating the good interpretability and knowledge mining potential of our model. Together, these contributions drive research into future work in audio processing, digital humanities, and knowledge mining.

关键词： Audio classification Digital humanities Transfer learning Prompt paradigm Whisper

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Breaking student-concept sparsity barrier for cognitive diagnosis

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FRONTIERS OF COMPUTER SCIENCE 2025年第11期19卷 1-12页

作者： Shao, Pengyang Zhang, Kun Gao, Chen Chen, Lei Cai, Miaomiao Wu, Le Li, Yong Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei 230601 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Educational Cognitive Diagnosis (CD) aims to provide students' mastery levels on different concepts. One common observation is that students often conduct many exercises but engage with a small subset of concepts, leading to a sparsity barrier. Current CD models mostly adopt mastery levels on all concepts as student modeling, overlooking the sparsity barrier. If a student does not interact with all concepts, we can not ensure that each dimension of mastery levels on concepts can be well-trained. In this paper, we propose a novel Enhancing Student Representations in Cognitive Diagnosis (ESR-CD), which combines application abilities and comprehension degrees for mastery levels on concepts. To model application ability, we propose a sparsity-based mask module that solely depends on the dense student-concept entries. Simultaneously, to further enhance comprehension degrees, we propose two layers: a matrix factorization layer and a relation refinement layer. Extensive experiments on two real-world datasets demonstrate the effectiveness of ESR-CD.

关键词： cognitive diagnosis student modeling educational data mining

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Unsupervised feature selection via maximum relevance and minimum global redundancy

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PATTERN RECOGNITION 2025年 164卷

作者： Zuo, Xianyu Zhang, Wenbo Wang, Xiangyu Dang, Lanxue Qiao, Baojun Wang, Yadi Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Henan Peoples R China Henan Univ Inst Data & Knowledge Engn Sch Comp & Informat Engn Kaifeng 475004 Henan Peoples R China

Unsupervised feature selection has been an important research topic in various fields since real datasets often lack complete label information. Many existing unsupervised feature selection algorithms select features based on feature relevance only without considering the redundancy between features, which may result in the selection of suboptimal features. To obtain the optimal feature subset, we propose a new unsupervised feature selection algorithm, called unsupervised Feature Selection with Max-Relevance and Minimum Global Redundancy (MRMGRFS) to select a subset of features with max-relevance and minimum global redundancy. In terms of relevance, we propose an unsupervised Feature Selection algorithm based on Spectral Clustering (SCFS), which divides all features into different clusters using spectral clustering and evaluates the relevance of features by measuring the distance between the features and the mean centers of own-cluster and heterogeneous-clusters. In terms of redundancy, the SCFS algorithm only considers the relevance of features and ignores the redundancy between features, which may select the redundant features that degrade performance. To tackle this issue, a Global Redundancy Minimization model (SJGRM) based on the SCFS and Jensen- Shannon divergence (JSD) is proposed to optimize the relevance score of the features. Furthermore, we propose an effective iterative algorithm for solving SJGRM based on the Alternating Direction Method of Multipliers (ADMM). Extensive experimental results on various public datasets demonstrate the superiority of the proposed algorithm.

关键词： Unsupervised feature selection Spectral clustering Max-relevance Minimum global redundancy

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Convert Cross-Domain Classification Into Few-Shot Learning: A Unified Prompt-Tuning Framework for Unsupervised Domain Adaptation

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 810-821页

作者： Zhu, Yi Shen, Hui Li, Yun Qiang, Jipeng Yuan, Yunhao Wu, Xindong Yangzhou Univ Sch Informat Engn Yangzhou 225009 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230601 Peoples R China

Unsupervised domain adaptation aims to facilitate learning tasks within an unlabeled target domain by leveraging labeled information from the source domain. To address the domain discrepancy, the majority of existing methods focus on learning latent features in both the source and target domains. However, the essential challenge in unsupervised domain adaptation lies in locating labeled information, specifically the true labels of the data, within the target domain. In this paper, we propose a unified prompt-tuning framework that converts cross-domain classification into few-shot learning, effectively integrating diverse cross-domain classification tasks into an iterative few-shot learning paradigm. The framework unifies all predicted pseudo labels, templates, and iterated models into a cohesive prompt-tuning model, exhibiting robust scalability for integrating other tuning modalities and offering high availability to minimize the necessity for extensive fine-tuning. Through extensive experiments on several well-known benchmark datasets, we validate the superior performance of our framework compared to other domain adaptation and fine-tuning PLMs methods, which achieves up to an impressive 99% predicting accuracy of true labels within the target domain.

关键词： Adaptation models Task analysis Predictive models data models Representation learning Recurrent neural networks Iterative methods Cross-domain classification few-shot learning prompt-tuning unsupervised domain adaptation

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LocalDGP: local degree-balanced graph partitioning for lightweight GNNs

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APPLIED INTELLIGENCE 2025年第2期55卷 1-19页

作者： Ji, Shengwei Li, Shengjie Liu, Fei Xu, Qiang Hefei Univ Sch Big Data & Artificial Intelligence Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China

Graph neural networks (GNNs) have been widely employed in various fields including knowledge graphs and social networks. When dealing with large-scale graphs, traditional full-batch training methods suffer from excessive GPU memory consumption. To solve this problem, subgraph sampling methods divide the graph into multiple subgraphs and then train the GNN on each subgraph sequentially, which can reduce GPU memory consumption. However, the existing graph partitioning algorithms (e.g., METIS) require global graph information before partitioning, and consume a significant amount of memory to store this information, which is detrimental for large-scale graph partitioning. Moreover, the GNN parameters in the subgraph sampling methods are shared among all the subgraphs. The structural differences between the subgraphs and the global graph (e.g., differences in node degree distributions) will produce a gradient bias on the subgraphs, resulting in a degradation of GNN accuracy. Therefore, a local degree-balanced graph partitioning algorithm named LocalDGP is proposed in this paper. First, in LocalDGP, only the local graph information is acquired during the partitioning process, which can reduce memory consumption. Second, the nodes are balancedly partitioned into subgraphs based on degree to ensure that the subgraph structure is consistent with the global graph. Extensive experimental results on four graph datasets demonstrate that LocalDGP can improve the accuracy of the GNNs while reducing memory consumption. The code is publicly available at https://***/li143yf/LocalDGP.

关键词： Graph neural networks Graph partitioning Local information Memory consumption

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A Cognitive Diagnosis Model With Nonlinear Dependence Between Students and Exercises

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2025年

作者： Zhang, Yuhong Lin, Zhihao Bu, Chenyang Yu, Kui Hu, Xuegang Wu, Xindong Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230601 Peoples R China

Cognitive diagnosis (CD) aims to discover students' mastery of knowledge concepts through response logs and it is an important task in intelligence education. CD is generally performed with the advantage of students doing exercises, which is learned by linear interacting between student proficiency and exercise difficulty. However, existing methods represent the proficiency and difficulty in view of knowledge concepts independently, which is in coarse-granularity, leading to the indiscrimination for different combinations of students and exercises under the same concept. To this end, we propose a cognitive diagnosis method with nonlinear dependence between students and exercises (CDND), in which, more fine-grained information is captured to distinguish the different combinations of students and exercises. First, the nonlinear dependence is captured with a novel attention-like mechanism by interacting between students and exercises. Moreover, this nonlinear dependence is embedded as the interactive discrimination vector. Second, the interactive discrimination is used to adjust the advantage using a second-order interaction, which can strengthen the discrimination of the advantage under different combinations of students and exercises. Extensive experimental results on three real data sets validate our interactive discrimination has good compatibility with existing methods, and with it, our CDND achieves an obvious improvement in performance. Our code is available on https://***/joyce99/LinZhihao/tree/main/CDND-master.

关键词： Vectors Artificial neural networks Neurons Deep learning Correlation Biological neural networks Computational modeling Attention mechanisms Logic gates Electronic learning Educational data mining neural networks (NNs) representation learning second-order interaction

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Multi-modal soft prompt-tuning for Chinese Clickbait Detection

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NEUROCOMPUTING 2025年 614卷

作者： Wang, Ye Zhu, Yi Li, Yun Wei, Liting Yuan, Yunhao Qiang, Jipeng Yangzhou Univ Sch Informat Engn Yangzhou Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Yancheng Teachers Univ Jiangsu Prov Key Constructive Lab Big Data Psychol Yancheng Peoples R China

With the rapid growth of Chinese online services, clickbait has proliferated at an unprecedented rate, designed to manipulate users into clicking for increased traffic or advertising promotion. Such clickbait not only facilitates the spread of fake news and misinformation but also enables click-jacking attacks, redirecting users to deceptive websites that steal personal information. These harmful activities can result insignificant losses and serious repercussions. The widespread presence of clickbait underscores both the importance and the challenges of developing effective detection methods. To date, the research paradigm of clickbait detection evolved from deep neural networks to fine-tuned Pre-trained Language Models (PLMs) and, more recently, into prompt-tuning models. However, these methods may suffer two main limitations: (1) they fail to utilize the multi-modal context information in news or posts and explore the higher-level feature representations to enhance the performance of clickbait detection;(2) they largely ignore the diverse range of Chinese expressive forms and neglect the complex semantics and syntactic structures of textual content to assist in learning abetter news representation. To overcome these limitations, we proposed a Multi-modal Soft Prompt-tuning Method (MSP) for Chinese Clickbait Detection, which jointly models the textual and image information into a continuous prompt embedding as the input of PLMs. Specifically, firstly, the soft prompt- tuning model including Graph Attention Network and Contrastive Language-Image Pre-training are employed to learn the feature representations of texts and images in news or posts, respectively. Then the obtained text and image representations are re-input into the soft prompt-tuning model with automatic template generation. The extensive experiments on three Chinese clickbait detection datasets demonstrate that our MSP achieved state-of-the-art performance.

关键词： Chinese Clickbait Detection Soft prompt-tuning Multi-modal

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An Automatic Paper-Reviewer Recommendation Algorithm Based on Depth and Breadth

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 607-616页

作者： Zheng, Xiulin Li, Peipei Wu, Xindong Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China

Paper-reviewer recommendation is an effective method to match reviewers for papers in peer review. However, existing recommendation methods are either sensitive to the order of paper process or not excepted fair in total. How to efficiently and accurately recommend is still a tough task and needs to be further explored. Thus, in this paper, we propose a greedy-version automatic paper-reviewer recommendation algorithm regarding both aspects of depth and breadth, named as GMCTS. More specifically, from the depth aspect, we focus on the maximum weight matching between paper and reviewer for each recommendation. While from the breadth aspect, we consider the broadest of distinctive topics (expertise) covered by the recommended reviewers. Furthermore, to avoid the unfairness of recommendation, we consider three types of constraints both theoretically and experimentally, including paper demand constraint, reviewer workload constraint and multiple types of Conflict Of Interests (COIs). Finally, extensive experiments conducted on benchmark datasets demonstrate that GMCTS achieves an impressive performance. Specifically, GMCTS scores a 90.5% and 12.67% gain in topic coverage and recall respectively compared to some advanced recommendation methods.

关键词： Vectors Task analysis Semantics Reviews Symbols Recurrent neural networks Recommender systems Paper-reviewer recommendation conflict of interests depth and breadth

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EduStudio: towards a unified library for student cognitive modeling

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FRONTIERS OF COMPUTER SCIENCE 2025年第8期19卷 1-16页

作者： Wu, Le Chen, Xiangzhi Liu, Fei Xie, Junsong Xia, Chenao Tan, Zhengtao Tian, Mi Li, Jinglong Zhang, Kun Lian, Defu Hong, Richang Wang, Meng Hefei Univ Technol Key Lab Knowledge Engn Big Data Hefei 230601 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230026 Peoples R China

Student cognitive modeling is a fundamental task in the intelligence education field. It serves as the basis for various downstream applications, such as student profiling, personalized educational content recommendation, and adaptive testing. Cognitive Diagnosis (CD) and knowledge Tracing (KT) are two mainstream categories for student cognitive modeling, which measure the cognitive ability from a limited time (e.g., an exam) and the learning ability dynamics over a long period (e.g., learning records from a year), respectively. Recent efforts have been dedicated to the development of open-source code libraries for student cognitive modeling. However, existing libraries often focus on a particular category and overlook the relationships between them. Additionally, these libraries lack sufficient modularization, which hinders reusability. To address these limitations, we have developed a unified PyTorch-based library EduStudio, which unifies CD and KT for student cognitive modeling. The design philosophy of EduStudio is from two folds. From a horizontal perspective, EduStudio employs the modularization that separates the main step pipeline of each algorithm. From a vertical perspective, we use templates with the inheritance style to implement each module. We also provide eco-services of EduStudio, such as the repository that collects resources about student cognitive modeling and the leaderboard that demonstrates comparison among models. Our open-source project is available at the website of ***.

关键词： open-source library student cognitive modeling intelligence education

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