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Haolong Xiang

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16 papers

2 author rows

AAAI Conference 2026 Conference Paper

IdeFN: Identifying Unclicked Space False Negatives via Relaxed Partial Optimal Transport for Conversion Rate Prediction

Weiyi Zhong
Weiming Liu
Lianyong Qi
Xiaoran Zhao
Xiaolong Xu
Haolong Xiang
Yang Cao
Shichao Pei

Accurate conversion rate (CVR) prediction is critical for recommender systems to capture user conversion intent and increase platform revenues. Traditional CVR models commonly suffer from sample selection bias (SSB) and data sparsity (DS), which has led to the adoption of click-through & conversion rate (CTCVR) multi-task learning frameworks to alleviate these issues. However, existing methods implicitly mislabel some unclicked samples with genuine conversion potential as negatives, thereby exacerbating the false negative sample (FNS) problem. To address this, we propose IdeFN, a multi‑task CVR framework that identifies false negatives in the unclicked space to enable CVR prediction across the entire exposure space and leverages CTR as an auxiliary task for shared‑parameter learning. Specifically, IdeFN consists of two main components, i.e., relaxed partial optimal transport (RPOT) module and sample relabeling mechanism (SRM). The former estimates the soft matching strengths between unclicked samples and positive samples under a relaxed partial optimal transport formulation, establishing corresponding relationships between these samples. The latter adaptively re-labels the unclicked samples according to the derived matching strengths, without relying on static or heuristic thresholds, thus enhancing the reliability of the generated pseudo-labels. Experimental results demonstrate that IdeFN effectively mitigates the FNS problem, achieving substantial improvements in CVR prediction accuracy.

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AAAI Conference 2026 Conference Paper

MTP: Exploring Multimodal Urban Traffic Profiling with Modality Augmentation and Spectrum Fusion

Haolong Xiang
Peisi Wang
Xiaolong Xu
Kun Yi
Xuyun Zhang
Quan Z. Sheng
Amin Beheshti
Wei Fan

With rapid urbanization in the modern era, traffic signals from various sensors have been playing a significant role in monitoring the states of cities, which provides a strong foundation in ensuring safe travel, reducing traffic congestion and optimizing urban mobility. Most existing methods for traffic time series modeling often rely on the original data modality, i.e., numerical direct readings from the sensors in cities. However, this unimodal approach overlooks the semantic information existing in multimodal heterogeneous urban data in different perspectives, which hinders a comprehensive understanding of traffic signals and limits the accurate prediction of complex traffic dynamics. To address this problem, we propose a novel Multimodal framework, MTP, for urban Traffic Profiling, which learns multimodal features through numeric, visual, and textual perspectives in the frequency domain. The three branches drive a multimodal perspective of traffic signal learning for augmentation, while the frequency learning strategies delicately refine the information for extraction. Specifically, we first conduct the visual augmentation for the traffic time series, which transforms the original modality into periodicity images and frequency images for visual learning. Also, we augment descriptive texts for the traffic time series based on the specific topic, background information and item description for textual learning. To complement the numeric information, we utilize frequency multilayer perceptrons for learning on the original modality. We design a hierarchical contrastive learning on the three branches to fuse the three modalities. Finally, extensive experiments on six real-world datasets demonstrate superior performance compared with the state-of-the-art approaches.

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NeurIPS Conference 2025 Conference Paper

A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments

Siyuan Wu
Yongzhe Jia
Haolong Xiang
Xiaolong Xu
Xuyun Zhang
Lianyong Qi
Wanchun Dou

Federated learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a shared model without exposing their raw data. However, existing FL research has primarily focused on optimizing learning performance based on the assumption of uniform client participation, with few studies delving into performance fairness under inconsistent client participation, particularly in model-heterogeneous FL environments. In view of this challenge, we propose PHP-FL, a novel model-heterogeneous FL method that explicitly addresses scenarios with varying client participation probabilities to enhance both model accuracy and performance fairness. Speciﬁcally, we introduce a Dual-End Aligned ensemble Learning (DEAL) module, where small auxiliary models on clients are used for dual-end knowledge alignment and local ensemble learning, effectively tackling model heterogeneity without a public dataset. Furthermore, to mitigate update conﬂicts caused by inconsistent participation probabilities, we propose an Importance-driven Selective Parameter Update (ISPU) module, which accurately updates critical local parameters based on training progress. Finally, we implement PHP-FL on a lightweight FL platform with heterogeneous clients across three different client participation patterns. Extensive experiments under heterogeneous settings and diverse client participation patterns demonstrate that PHP-FL achieves state-of-the-art performance in both accuracy and fairness. Our code is available at: https: //github. com/Siyuan01/PHP-FL-main.

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IJCAI Conference 2025 Conference Paper

CLLMRec: Contrastive Learning with LLMs-based View Augmentation for Sequential Recommendation

Fan Lu
Xiaolong Xu
Haolong Xiang
Lianyong Qi
Xiaokang Zhou
Fei Dai
Wanchun Dou

Sequential recommendation generates embedding representations from historical user-item interactions to recommend the next potential interaction item. Due to the complexity and variability of historical user-item interactions, extracting effective user features is quite challenging. Recent studies have employed sequential networks such as time series networks and Transformers to capture the intricate dependencies and temporal patterns in historical user-item interactions, extracting more effective user features. However, limited by the scarcity and suboptimal quality of data, these methods struggle to capture subtle differences in user sequences, which results in diminished recommendation accuracy. To address the above issue, we propose a contrastive learning framework with LLMs-based view augmentation (CLLMRec), which effectively mines differences in behavioral sequences through sample generation. Specifically, CLLMRec utilizes LLMs (Large Language Models) to augment views and expand user behavior sequence representations, providing high-quality positive and negative samples. Subsequently, CLLMRec employs the augmented views for effective contrastive learning, capturing subtle differences in behavioral sequences to suppress interference from irrelevant noise. Experimental results on three public datasets demonstrate that the proposed method outperforms state-of-the-art baseline models, and significantly enhances recommendation performance.

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AAAI Conference 2025 Conference Paper

DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation

Wenwen Gong
Yangliao Geng
Dan Zhang
Yifan Zhu
Xiaolong Xu
Haolong Xiang
Amin Beheshti
Xuyun Zhang

Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning (DivGCL) model for diversifying recommendations. Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M.

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ICML Conference 2025 Conference Paper

DocKS-RAG: Optimizing Document-Level Relation Extraction through LLM-Enhanced Hybrid Prompt Tuning

Xiaolong Xu 0001
Yibo Zhou
Haolong Xiang
Xiaoyong Li 0002
Xuyun Zhang
Lianyong Qi
Wanchun Dou

Document-level relation extraction (RE) aims to extract comprehensive correlations between entities and relations from documents. Most of existing works conduct transfer learning on pre-trained language models (PLMs), which allows for richer contextual representation to improve the performance. However, such PLMs-based methods suffer from incorporating structural knowledge, such as entity-entity interactions. Moreover, current works struggle to infer the implicit relations between entities across different sentences, which results in poor prediction. To deal with the above issues, we propose a novel and effective framework, named DocKS-RAG, which introduces extra structural knowledge and semantic information to further enhance the performance of document-level RE. Specifically, we construct a Document-level Knowledge Graph from the observable documentation data to better capture the structural information between entities and relations. Then, a Sentence-level Semantic Retrieval-Augmented Generation mechanism is designed to consider the similarity in different sentences by retrieving the relevant contextual semantic information. Furthermore, we present a hybrid-prompt tuning method on large language models (LLMs) for specific document-level RE tasks. Finally, extensive experiments conducted on two benchmark datasets demonstrate that our proposed framework enhances all the metrics compared with state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

Empowering Multimodal Road Traffic Profiling with Vision Language Models and Frequency Spectrum Fusion

Haolong Xiang
Xiaolong Xu
Guangdong Wang
Xuyun Zhang
Xiaoyong Li
Qi Zhang
Amin Beheshti
Wei Fan

With the rapid urbanization in the modern era, smart traffic profiling based on multimodal sources of data has been playing a significant role in ensuring safe travel, reducing traffic congestion and optimizing urban mobility. Most existing methods for traffic profiling on the road level usually utilize single-modality data, i. e. , they mainly focus on image processing with deep vision models or auxiliary analysis on the textual data. However, the joint modeling and multimodal fusion of the textual and visual modalities have been rarely studied in road traffic profiling, which largely hinders the accurate prediction or classification of traffic conditions. To address this issue, we propose a novel multimodal learning and fusion framework for road traffic profiling, named TraffiCFUS. Specifically, given the traffic images, our TraffiCFUS framework first introduces Vision Language Models (VLMs) to generate text and then creates tailored prompt instructions for refining this text according to the specific scene requirements of road traffic profiling. Next, we apply the discrete Fourier transform to convert multimodal data from the spatial domain to the frequency domain and perform a cross-modal spectrum transform to filter out irrelevant information for traffic profiling. Furthermore, the processed spatial multimodal data is combined to generate fusion loss and interaction loss with contrastive learning. Finally, extensive experiments on four real-world datasets illustrate superior performance compared with the state-of-the-art approaches.

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NeurIPS Conference 2025 Conference Paper

HPSERec: A Hierarchical Partitioning and Stepwise Enhancement Framework for Long-tailed Sequential Recommendation

Xiaolong Xu
Xudong Zhao
Haolong Xiang
Xuyun Zhang
Wei Shen
Hongsheng Hu
Lianyong Qi

The long-tail problem in sequential recommender systems stems from imbalanced interaction data, resulting in suboptimal model performance for tail users and items. Recent studies have leveraged head data to enhance tail data for diminish the impact of the long-tail problem. However, these methods often adopt ad-hoc strategies to distinguish between head and tail data, which fails to capture the underlying distributional characteristics and structural properties of each category. Moreover, due to a substantial representational gap exists between head and tail data, head-to-tail enhancement strategies are susceptible to negative transfer, often leading to a decline in overall model performance. To address these issues, we propose a hierarchical partitioning and stepwise enhancement framework, called HPSERec, for long-tailed sequential recommendation. HPSERec partitions the item set into subsets based on a data imbalance metric, assigning an expert network to each subset to capture user-specific local features. Subsequently, we apply knowledge distillation to progressively improve long-tail interest representation, followed by a Sinkhorn optimal transport-based feedback module, which aligns user representations across expert levels through a globally optimal and softly matched mapping. Extensive experiments on three real-world datasets demonstrate that HPSERec consistently outperforms all baseline methods. The implementation code is available at https: //anonymous. 4open. science/r/HPSERec-2404.

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IJCAI Conference 2025 Conference Paper

MEGAD: A Memory-Efficient Framework for Large-Scale Attributed Graph Anomaly Detection

Yifan Zhang
Haolong Xiang
Xiaolong Xu
Zishun Rui
Xiaoyong Li
Lianyong Qi
Fei Dai

Graph anomaly detection (GAD), with its ability to accurately identify anomalous patterns in graph data, plays a vital role in areas such as network security, social media platforms, and fraud detection. Graph autoencoder-based methods are widely used for GAD due to their efficiency and effectiveness in capturing complex patterns and learning meaningful representations. However, the above methods are constrained by hardware memory, hindering the detection for large-scale graph data. In this paper, we propose a Memory-Efficient framework for large-scale attributed Graph Anomaly Detection (MEGAD). Specifically, MEGAD first generates node embeddings and then refines them through a lightweight joint optimization model, ensuring minimal memory overhead. The optimized embeddings are subsequently fed into a detector to compute anomaly scores. Extensive experiments demonstrate that our framework achieves comparable accuracy to state-of-the-art methods across multiple datasets while significantly reducing memory consumption on large-scale graphs.

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AAAI Conference 2025 Conference Paper

NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification

Xiaolong Xu
Yibo Zhou
Haolong Xiang
Xiaoyong Li
Xuyun Zhang
Lianyong Qi
Wanchun Dou

Graph Neural Networks (GNNs) are widely applied on graph-level tasks, such as node classification, link prediction and graph generation. Existing GNNs mostly adopt a message-passing mechanism to aggregate node information with their neighbors, which often makes node information similar after rounds of aggregations and leads to oversmoothing. Although recent works have made improvements by combining different message aggregation methods or introducing semantic encodings as priors, these message-passing based GNNs still fail to combat oversmoothing after multiple iterations of node aggregation. Besides, the feature extraction ability of these methods is restricted because of the graph sparsity that hinders the aggregation of node information. To deal with the above two issues, we propose Neighborhood-based and Label-enhanced Graph Transformer (NLGT), a novel and effective framework for graph learning. Specifically, we present a label-enhanced feature fusion mechanism that integrate the shallow node features and label embeddings as enhanced features. Moreover, we design a neighborhood-based mask attention mechanism to alleviate the negative effects caused by the sparsity of the graph. In the predicting stage, we aggregate the prediction results from multiple sampled sub-graphs and apply voting mechanisms to enhance the accuracy and robustness of our framework. Finally, extensive experiments are conducted on four open benchmark datasets, which demonstrate the effectiveness and robustness of our proposed framework compared with existing state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers

Siyuan Wu
Yongzhe Jia
Bowen Liu
Haolong Xiang
Xiaolong Xu
Wanchun Dou

Personalized federated learning (PFL) has recently gained significant attention for its capability to address the poor convergence performance on highly heterogeneous data and the lack of personalized solutions of traditional federated learning (FL). Existing mainstream approaches either perform personalized aggregation based on a specific model architecture to leverage global knowledge or achieve personalization by exploiting client similarities. However, the former overlooks the discrepancies in client data distributions by indiscriminately aggregating all clients, while the latter lacks fine-grained collaboration of classifiers relevant to local tasks. In view of this challenge, we propose a Personalized Federated learning method for Enhancing Collaboration among Similar Classifiers (PFedCS), which aims at improving the client’s accuracy on local tasks. Concretely, it is achieved by leveraging awareness of the client classifier similarities to address the above problems. By iteratively measuring the distance of the classifier parameters between clients and clustering with each client as a cluster center, the central server adaptively identifies the collaborating clients with similar data distributions. In addition, a distance-constrained aggregation method is designed to generate customized collaborative classifiers to guide local training. As a result, extensive experimental evaluations conducted on three datasets demonstrate that our method achieves state-of-the-art performance.

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IJCAI Conference 2025 Conference Paper

Universal Backdoor Defense via Label Consistency in Vertical Federated Learning

Peng Chen
Haolong Xiang
Xin Du
Xiaolong Xu
Xuhao Jiang
Zhihui Lu
Jirui Yang
Qiang Duan

Backdoor attacks in vertical federated learning (VFL) are particularly concerning as they can covertly compromise VFL decision-making, posing a severe threat to critical applications of VFL. Existing defense mechanisms typically involve either label obfuscation during training or model pruning during inference. However, the inherent limitations on the defender's access to the global model and complete training data in VFL environments fundamentally constrain the effectiveness of these conventional methods. To address these limitations, we propose the Universal Backdoor Defense (UBD) framework. UBD leverages Label Consistent Clustering (LCC) to synthesize plausible latent triggers associated with the backdoor class. This synthesized information is then utilized for mitigating backdoor threats through Linear Probing (LP), guided by a constraint on Batch Normalization (BN) statistics. Positioned within a unified VFL backdoor defense paradigm, UBD offers a generalized framework for both detection and mitigation that critically does not necessitate access to the entire model or dataset. Extensive experiments across multiple datasets rigorously demonstrate the efficacy of the UBD framework, achieving state-of-the-art performance against diverse backdoor attack types in VFL, including both dirty-label and clean-label variants.

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JBHI Journal 2025 Journal Article

ViResGF-Net: Gated Multi-Scale Hybrid Vision Transformer for Robust Fundus Image Multi-Label Classification

Binghan Chen
Haolong Xiang
Jiayi Wan
Muhammad Bilal
Xiaolong Xu

With the acceleration of the global population aging process, fundus diseases such as cataracts and glaucoma have become major factors leading to visual impairment. In the field of ophthalmic diagnosis, the traditional diagnosis and treatment mode mainly relies on doctors to make pathological judgments by observing fundus images with the naked eye. However, due to differences in evaluation standards among doctors, there are discrepancies in diagnostic results for the same fundus photo. In addition, most doctors only specialize in specific fundus diseases, making it difficult to accurately diagnose cases where multiple diseases coexist. To tackle the aforementioned issues, this paper proposes a gated multi-scale hybrid vision Transformer model, designated as ViResGF-Net, for the multi-class classification of fundus diseases. The model integrates the dual-branch structure of Convolutional Neural Network (CNN) and Vision Transformer (ViT). While retaining the global modeling capability of ViT, it performs local feature extraction through the CNN branch and introduces the Feature Pyramid Network (FPN) structure to further enhance the local feature extraction capability of the CNN branch. In the feature fusion stage, a Gated Fusion Unit (GFU) module is added to fuse the feature vectors of the two branches. Finally, the MLP classifier gives the prediction results based on the integrated feature vectors. Through extensive experiments, our model achieved an accuracy of 93. 56%, a precision of 92. 99%, and an F1 score of 92. 36%, all of which are better than those of other models.

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IJCAI Conference 2024 Conference Paper

Attention Based Document-level Relation Extraction with None Class Ranking Loss

Xiaolong Xu
Chenbin Li
Haolong Xiang
Lianyong Qi
Xuyun Zhang
Wanchun Dou

Through document-level relation extraction (RE), the analysis of the global relation between entities in the text is feasible, and more comprehensive and accurate semantic information can be obtained. In document-level RE, the model needs to infer the implicit relations between two entities in different sentences. To obtain more semantic information, existing methods mainly focus on exploring entity representations. However, they ignore the correlations and indivisibility between relations, entities and contexts. Furthermore, current methods only independently estimate the cases of predefined relations, ignoring the case of "no relation'', which results in poor prediction. To address the above issues, we propose a document-level RE method based on attention mechanisms, which considers the case of "no relation''. Specifically, our approach leverages graph attention and multi-head attention networks to capture the correlations and indivisibility among relations, entities, and contexts, respectively. In addition, a novel multi-label loss function that promotes large margins in label confidence scores between each predefined class and the none class is employed to improve the prediction performance. Extensive experiments conducted on benchmarking datasets demonstrate that our proposed method outperforms the state-of-the-art baselines with higher accuracy.

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IJCAI Conference 2023 Conference Paper

OptIForest: Optimal Isolation Forest for Anomaly Detection

Haolong Xiang
Xuyun Zhang
Hongsheng Hu
Lianyong Qi
Wanchun Dou
Mark Dras
Amin Beheshti
Xiaolong Xu

Anomaly detection plays an increasingly important role in various fields for critical tasks such as intrusion detection in cybersecurity, financial risk detection, and human health monitoring. A variety of anomaly detection methods have been proposed, and a category based on the isolation forest mechanism stands out due to its simplicity, effectiveness, and efficiency, e. g. , iForest is often employed as a state-of-the-art detector for real deployment. While the majority of isolation forests use the binary structure, a framework LSHiForest has demonstrated that the multi-fork isolation tree structure can lead to better detection performance. However, there is no theoretical work answering the fundamentally and practically important question on the optimal tree structure for an isolation forest with respect to the branching factor. In this paper, we establish a theory on isolation efficiency to answer the question and determine the optimal branching factor for an isolation tree. Based on the theoretical underpinning, we design a practical optimal isolation forest OptIForest incorporating clustering based learning to hash which enables more information to be learned from data for better isolation quality. The rationale of our approach relies on a better bias-variance trade-off achieved by bias reduction in OptIForest. Extensive experiments on a series of benchmarking datasets for comparative and ablation studies demonstrate that our approach can efficiently and robustly achieve better detection performance in general than the state-of-the-arts including the deep learning based methods.

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IS Journal 2021 Journal Article

Isolation Forest Based Anomaly Detection Framework on Non-IID Data

Haolong Xiang
Jiayu Wang
Kotagiri Ramamohanarao
Zoran Salcic
Wanchun Dou
Xuyun Zhang

Anomaly detection is a significant but challenging data mining task in a wide range of applications. Different domains usually use different ways to measure the characteristics of data and to define the anomaly types. As a result, it is a big challenge to develop a versatile anomaly detection framework that can be universally applied with satisfactory performance in most, if not all, applications. In this article, we propose a generic isolation forest based ensemble framework named EDBHiForest, which can be universally applied to data spaces with arbitrary distance measures. It is realized through embedding the isolation forest structure with extended distance-based hashing (EDBH), which can significantly enhance the versatility and applicability of isolation forest based anomaly detection. This framework overcomes the limitations of existing isolation forest based methods that can only be applied to datasets with a very limited range of distance measure types. Extensive experiments on various non-independent and identically distributed datasets demonstrate the effectiveness and efficiency of our approach.

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