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34th ACM Web Conference, WWW 2025

作者： Bian, Qingtian de Carvalho, Marcus Li, Tieying Xu, Jiaxing Fang, Hui Ke, Yiping College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Northeastern University Shenyang China Research Institute for Interdisciplinary Sciences Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9798400712746

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA’s effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. © 2025 Copyright held by the owner/author(s).

关键词： cross-domain sequential recommendation

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Partially Linear Single-Index Model in the Presence of Measurement Error

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Journal of Systems science & Complexity 2022年第6期35卷 2361-2380页

作者： LIN Hongmei SHI Jianhong TONG Tiejun ZHANG Riquan School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationEast China Normal UniversityShanghai 200062China School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China Department of Mathematics Hong Kong Baptist UniversityHongKong 519087China School of Statistics East China Normal UniversityShanghai 200062China

The partially linear single-index model(PLSIM) is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates. This paper considers the PLSIM with measurement error possibly in all the variables. The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function. The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method, and apply it to analyze the osteoporosis prevention data.

关键词： Local linear regression measurement error partially linear model SIMEX single-index model

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Graph-Based Multisample Comparison with Application to Feature Selection for Multi-Category Responses

SSRN

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SSRN 2025年

作者： Pu, Dan Lan, Wei Tsai, Chih-Ling School of Statistics Joint Laboratory of Data Science and Business Intelligence Southwestern University of Finance and Economics Chengdu China School of Statistics Center of Statistical Research Big Data Laboratory on Financial Security and Behavior Southwestern University of Finance and Economics Chengdu China Graduate School of Management University of California DavisCA United States

This article proposes a graph-based maximum pairwise difference (MPD) test to compare K-sample distributions. This test can be utilized to identify the most informative features contributing to the heterogeneity of multiple multivariate distributions. To construct the test, we first pool observations from multiple samples to create a similarity graph based on the distances between any two observations. For each observation, we next evaluate its connection probability to each sample. Then, we doubly maximize the squared differences of these connection probabilities via the within and between samples. Accordingly, the MPD test is a max-of-squares test and can effectively detect sparse alternatives. In addition, we show the asymptotic null distribution of the test statistic and establish its consistency under alternatives. To illustrate the finite sample performance of the proposed test, we conduct extensive simulation studies. The results indicate that the MPD test outperforms some commonly used multisample tests. Finally, we employ it to select relevant features related to corporate credit ratings. © 2025, The Authors. All rights reserved.

关键词： Feature Selection

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A Multifacet Hierarchical Sentiment-Topic Model with Application to Multi-Brand Online Review Analysis

arXiv

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arXiv 2025年

作者： Liang, Qiao Deng, Xinwei Joint Laboratory of Data Science and Business Intelligence Southwestern University of Finance and Economics Chengdu China School of Statistics Southwestern University of Finance and Economics Chengdu China Department of Statistics Virginia Tech BlacksburgVA United States

Multi-brand analysis based on review comments and ratings is a commonly used strategy to compare different brands in marketing. It can help consumers make more informed decisions and help marketers understand their brand’s position in the market. In this work, we propose a multifacet hierarchical sentiment-topic model (MH-STM) to detect brand-associated sentiment polarities towards multiple comparative aspects from online customer reviews. The proposed method is built on a unified generative framework that explains review words with a hierarchical brand-associated topic model and the overall polarity score with a regression model on the empirical topic distribution. Moreover, a novel hierarchical Pólya urn (HPU) scheme is proposed to enhance the topic-word association among topic hierarchy, such that the general topics shared by all brands are separated effectively from the unique topics specific to individual brands. The performance of the proposed method is evaluated on both synthetic data and two real-world review corpora. Experimental studies demonstrate that the proposed method can be effective in detecting reasonable topic hierarchy and deriving accurate brand-associated rankings on multi-aspects. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic models

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BASIC: Bipartite Assisted Spectral-clustering for Identifying Communities in Large-scale Networks

arXiv

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arXiv 2025年

作者： Gao, Tianchen Liu, Jingyuan Pan, Rui Sun, Ao Department of Statistics and Data Science School of Economics Xiamen University Fujian Xiamen361102 China MOE Key Laboratory of Econometrics Department of Statistics and Data Science School of Economics Wang Yanan Institute for Studies in Economics Fujian Key Lab of Statistics Xiamen University Fujian Xiamen361102 China School of Statistics and Mathematics Central University of Finance and Economics Beijing Beijing100081 China Data Sciences and Operations Department Marshall School of Business University of Southern California Los AngelesCA90089 United States

Community detection, which focuses on recovering the group structure within networks, is a crucial and fundamental task in network analysis. However, the detection process can be quite challenging and unstable when community signals are weak. Motivated by a newly collected large-scale academic network dataset from the Web of science, which includes multi-layer network information, we propose a Bipartite Assisted Spectral-clustering approach for Identifying Communities (BASIC), which incorporates the bipartite network information into the community structure learning of the primary network. The accuracy and stability enhancement of BASIC is validated theoretically on the basis of the degree-corrected stochastic block model framework, as well as numerically through extensive simulation studies. We rigorously study the convergence rate of BASIC even under weak signal scenarios and prove that BASIC yields a tighter upper error bound than that based on the primary network information alone. We utilize the proposed BASIC method to analyze the newly collected large-scale academic network dataset from statistical papers. During the author collaboration network structure learning, we incorporate the bipartite network information from author-paper, author-institution, and author-region relationships. From both statistical and interpretative perspectives, these bipartite networks greatly aid in identifying communities within the primary collaboration network. Copyright © 2025, The Authors. All rights reserved.

关键词： Stochastic models

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FINITE s-GEODESIC-TRANSITIVE DIGRAPHS

arXiv

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arXiv 2023年

作者： Jin, Wei School of Statistics Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China School of Mathematics and Statistics Central South University Hunan Changsha410075 China

This paper initiates the investigation of the family of (G, s)-geodesic-transitive digraphs with s ≥ 2. We first give a global analysis by providing a reduction result. Let Γ be such a digraph and let N be a normal subgroup of G maximal with respect to having at least 3 orbits. Then the quotient digraph ΓN is (G/N, s′)-geodesic-transitive where s′ = min{s, diam(ΓN )}, G/N is either quasiprimitive or bi-quasiprimitive on V (ΓN ), and ΓN is either directed or an undirected complete graph. Moreover, it is further shown that if Γ is not (G, 2)-arc-transitive, then G/N is quasiprimitive on V (ΓN ). On the other hand, we also consider the case that the normal subgroup N of G has one orbit on the vertex set. We show that if N is regular on V (Γ), then Γ is a circuit, and particularly each (G, s)-geodesic-transitive normal Cayley digraph with s ≥ 2, is a circuit. Finally, we investigate (G, 2)-geodesic-transitive digraphs with either valency at most 5 or diameter at most 2. Let Γ be a (G, 2)-geodesic-transitive digraph. It is proved that: if Γ has valency at most 5, then Γ is (G, 2)-arc-transitive;if Γ has diameter 2, then Γ is a balanced incomplete block design with the Hadamard parameters. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

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Modeling of unsupervised knowledge graph of events based on mutual information among neighbor domains and sparse representation

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Defence Technology（防务技术） 2022年第12期18卷 2150-2159页

作者： Jing-Tao Sun Jing-Ming Li Qiu-Yu Zhang School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxi710121China School of Management Science and Engineering Anhui University of Finance and EconomicsBengbuAnhui230030China School of Computer and Communication Lanzhou University of TechnologyLanzhouGansu730050China

Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,***,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of ***,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature ***,the events knowledge graph is constructed by means of sparse representation and l1 *** experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding *** experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.

关键词： Text event mining Knowledge graph of events Mutual information among neighbor domains Sparse representation

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Machine Learning-based Prediction of Maximum Inundation Depth and Maximum Inundation Flow Rate for Flooding 5

Machine Learning-based Prediction of Maximum Inundation Dept...

引用

5th International Conference on Computer, Big data and Artificial Intelligence, ICCBD+AI 2024

作者： Tian, Tian Jiang, Lexin Zhang, Zongjia Yang, Lili Cheng, Xiangju South China University of Technology School of Civil Engineering and Transportation Guangzhou China Jiujiang Vocational University of Civil Engineering and Architecture Jiujiang China Jinan University School of Public Administration & Emergency Management Guangzhou China Southern University of Science and Technology SUSTech Academy of Finance and Economics Shenzhen China Southern University of Science and Technology Department of Statistics and Data Science Shenzhen China South China University of Technology State Key Laboratory of Subtropical Building and Urban Science Guangzhou China

ISBN: (纸本)9798331533991

In this paper, taking Yangshuo County, which is frequently affected by mountain floods and Lijiang River transit floods together, as an example, we utilize the high-precision flood data generated by the coupled hydrological-hydrodynamic models in a machine learning sample library and construct the maximum inundation water depth prediction model (KNN-Depth) and the maximum inundation flow rate prediction model (KNN-Speed) based on the machine learning K-nearest-neighbor algorithm (KNN) to predict the maximum inundation water depth and maximum inundation flow rate. The prediction performance of the models has been tested and examined with 17 data sets of historical floods, in which a total of six flood risk classes have been classified to carry out flood risk prediction in the study area by considering the two indicators of maximum inundation water depth and maximum inundation flow rate. The results show that: 1) The maximum inundation water depth predicted by KNN-Depth is close to the simulation results of the maximum inundation water depth of the hydrodynamic model, and the mean values of the Coefficients of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) indexes of the 17 floods are 0.988, 0.127m, and 0.060m, respectively;2) The maximum inundation flow velocity generated by KNN-Speed model is similar to the simulation results of the maximum inundation flow velocity of the hydrodynamic model, and the mean values of R2, RMSE and MAE indicators of the 17 floods are 0.968, 0.051m/s and 0.020m/s, respectively;3) The running time of KNN-Depth and KNN-Speed models is 2s and 10s, respectively for the prediction;4) The research indicates that the flood risk in Yangshuo County within 500m of the banks of the main rivers during a large flood is level 6 in general, and the flood risk in the new and old urban areas is up to level 5. © 2024 IEEE.

关键词： Nearest neighbor search

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Strong convergence of a tamed theta scheme for McKean-Vlasov NSDDEs driven by fractional Brownian motion

arXiv

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arXiv 2024年

作者： Tan, Li Wang, Shengrong School of Statistics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics Jiangxi Nanchang330013 China College of Modern Economics and Management of Jiangxi University of Finance and Economics Jiangxi Jiujiang332020 China

In this article, we propose a tamed theta Euler-Maruyama (EM) scheme forsuperlinearly growing neutral McKean-Vlasov stochastic differential delayequations driven by fractional Brownian motion with Hurst exponent$H\\in(1/2,1)$. The analytical properties including uniqueness and existence,boundedness of moment and propagation of chaos are investigated. Moreover, theconvergence rate of the numerical scheme is established. © 2024, CC0.

关键词： Stochastic systems

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Functional Adaptive Huber Linear Regression

arXiv

引用

arXiv 2024年

作者： Peng, Ling Liu, Xiaohui Lian, Heng School of Statistics and Data Science Jiangxi University of Finance and Economics Jiangxi Nanchang China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and Economics Jiangxi Nanchang China School of Mathematics and Statistics Victoria University of Wellington Wellington6140 New Zealand City University of Hong Kong Shenzhen Research Institute Shenzhen China Department of Mathematics City University of Hong Kong Hong Kong

Robust estimation has played an important role in statistical and machine learning. However, its applications to functional linear regression are still under-developed. In this paper, we focus on Huber’s loss with a diverging robustness parameter which was previously used in parametric models. Compared to other robust methods such as median regression, the distinction is that the proposed method aims to estimate the conditional mean robustly, instead of estimating the conditional median. We only require (1+κ)-th moment assumption (κ > 0) on the noise distribution, and the established error bounds match the optimal rate in the least-squares case as soon as κ ≥ 1. We establish convergence rate in probability when the functional predictor has a finite 4-th moment, and finite-sample bound with exponential tail when the functional predictor is Gaussian, in terms of both prediction error and L2 error. The results also extend to the case of functional estimation in a reproducing kernel Hilbert space (RKHS). © 2024, CC BY.

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