Xindong Wu

Biographic Data

ID7170253
NAMEXindong Wu
GIVEN NAMESXindong
FAMILY NAMEWu
SIGNATUREWU X
AFFILIATIONSHefei University of Technology
ORCID0000-0003-2396-1704
VERIFIEDYes
TOTAL WORKS11
TOTAL CITATIONS0
AUTHOR COUNT10
EDITOR COUNT1
FIRST PUBLICATION YEAR2008
LATEST PUBLICATION YEAR2026
H-INDEX0

  • Where to Go: A Spatial Social Force Graph Neural Network for Predicting Pedestrian Trajectories From Videos With Complex Motion Scenarios

    Open AccessShaojie Qiao, Rongmin Tang et al.ARTICLEIEEE Transactions on Computational…2026

    Traditional pedestrian trajectory prediction models focus on spatio–temporal data without proper consideration of individual interactions with the environment, mutual interactions, and contextual information, resulting in low prediction performance in real applications. In this article, we propose a new pedestrian trajectory prediction model called spatial social force graph neural network (SSF-GNN). First, SSF-GNN adopts a gate recurrent unit (G…

  • Heterogeneous Neighborhood-Enhanced Graph Contrastive Learning for Recommendation

    Open AccessLei Sang, Chi Zhang et al.ARTICLEIEEE Transactions on Computational…2026

    Heterogeneous self-supervised graph learning has gained considerable attention in recommender systems for its ability to capture diverse semantic and structural relationships in real-world data. Contrastive learning enhances representation learning by maximizing agreement between positive pairs while distinguishing negative ones in cross-views. However, two key challenges remain: 1) noise, such as false negatives, that degrades representation qua…

  • Optimizing Feature Interaction via Information Bottleneck for CTR Prediction

    Open AccessLei Sang, Hanwei Li et al.ARTICLEIEEE Transactions on Computational…2026

    Click-through rate (CTR) prediction plays a pivotal role in recommender systems and online advertising by estimating the probability of user engagement with recommended items or advertisements. However, existing methodologies encounter multiple challenges. First, current approaches often struggle to maintain robustness in the presence of noise. This challenge arises from the inherent complexity of real-world data, where noisy or irrelevant featur…

  • Enhancing Explainable Sequential Recommendation With Disentangled Representations and Auxiliary Review Explanations

    Open AccessJinpeng Chen, Huachen Guan et al.ARTICLEIEEE Transactions on Computational…2026

  • Intent-Aware Contrastive Learning for Cross-Domain Recommendation

    Open AccessLei Sang, Yi Shen et al.ARTICLEIEEE Transactions on Computational…2026

    Accurately capturing user preferences across diverse domains is a fundamental challenge in cross-domain recommendation (CDR) systems. Recent literature has established that disentangling user preferences into global and domain-specific components significantly enhances recommendation performance. However, existing CDR systems are hindered by two critical challenges: 1) how to align user representations across domains to accommodate the discrepanc…

  • Dynamic Graph Learning to Denoise Implicit Feedback for Graph Collaborative Filtering

    Open AccessHuiting Liu, Xinchen Xiong et al.ARTICLEIEEE Transactions on Computational…2025

    Due to the inherent challenges in acquiring explicit feedback, graph collaborative filtering (GCF) models often resort to implicit feedback. However, there exists noise in implicit feedback that may not accurately reflect users’ preferences. This noise will be amplified by the aggregating and propagating operations of GCF, thereby affecting the performance of GCF. Existing noise mitigation methods attempt to filter noisy samples from implicit fee…

  • A Cognitive Diagnosis Model With Nonlinear Dependence Between Students and Exercises

    Open AccessYuhong Zhang, Zhihao Lin et al.ARTICLEIEEE Transactions on Computational…2025

    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 c…

  • Adversarial Heterogeneous Graph Neural Network for Robust Recommendation

    Open AccessLei Sang, Min Xu et al.ARTICLEIEEE Transactions on Computational…2023

    Recommendation systems play a vital role in identifying the hidden interactions between users and items in online social networks. Recently, graph neural networks (GNNs) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for a recommendation. However, existing GNN-based methods do not have broad applicability to heterogeneous graphs that integrate auxiliary data with diverse types…

  • A twitter recruitment intelligent system: Association rule mining for smoking cessation

    Open AccessAhmed Abdeen Hamed, Xindong Wu et al.ARTICLESocial Network Analysis and Mining2014

  • The Top Ten Algorithms in Data Mining

    Xindong Wu, Vipin KumarBOOKTop Ten Algorithms in Data Mining2009

    Identifying some of the most influential algorithms that are widely used in the data mining community, this volume provides a description of each algorithm, discusses the impact of the algorithms, and reviews current and future research on the algorithms. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have e…

  • Top 10 algorithms in data mining

    Open AccessXindong Wu, Vipin Kumar et al.ARTICLEKnowledge and Information Systems2008