OpenRaiser/Intern-Atlas huggingface dataset:
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
Authors: Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao
Venue: CIKM
Year: 2023

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.
Adaptive Graph Neural Diffusion for Traffic Demand Forecasting
Authors: Yiling Wu, Xinfeng Zhang, Yaowei Wang
Venue: CIKM
Year: 2023

This paper studies the problem of spatial-temporal modeling for traffic demand forecasting. In practice, the temporal-spatial dependencies are complex. Conventional methods using graph convolutional networks and gated recurrent units cannot fully explore the patterns of demand evolution. Therefore, we propose Adaptive Graph Neural Diffusion (AGND) for spatial-temporal graph modeling. Specifically, complex spatial relations are modeled with a diffusion process by the graph neural diffusion. The spatial attention mechanism and a data-driven semantic adjacency matrix are used to describe the diffusivity function in the graph neural diffusion, which provides both local and global spatial information. Long-term temporal dependencies are modeled by the temporal attention mechanism. The proposed method is applied to two real-world datasets, and the results show that the proposed method outperforms state-of-the-art methods.
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Authors: Ning Wu, Ming Gong, Linjun Shou, Jian Pei, Daxin Jiang
Venue: CIKM
Year: 2023

Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately under the condition of data sparsity. To address this challenge, some methods have leveraged external user behavior data from multiple platforms to enrich user representation. However, all of these methods require a consistent user ID across platforms and ignore the information from similar users. In this study, we propose RUEL, a novel retrieval-based sequential recommender that can effectively incorporate external anonymous user behavior data from Edge browser logs to enhance recommendation. We first collect and preprocess a large volume of Edge browser logs over a one-year period and link them to target entities that correspond to candidate items in recommendation datasets. We then design a contrastive learning framework with a momentum encoder and a memory bank to retrieve the most relevant and diverse browsing sequences from the full browsing log based on the semantic similarity between user representations. After retrieval, we apply an item-level attentive selector to filter out noisy items and generate refined sequence embeddings for the final predictor. RUEL is the first method that connects user browsing data with typical recommendation datasets and can be generalized to various recommendation scenarios and datasets. We conduct extensive experiments on four real datasets for sequential recommendation tasks and demonstrate that RUEL significantly outperforms state-of-the-art baselines. We also conduct ablation studies and qualitative analysis to validate the effectiveness of each component of RUEL and provide additional insights into our method.
Promoting Diversity in Mixed Complex Cooperative and Competitive Multi-Agent Environment
Authors: Jia Wu, Zixiao Huang
Venue: CIKM
Year: 2023

This paper introduces a new approach for promoting diversity of behavior in complex multi-agent environments that pose three challenges: 1) competition or collaboration among agents of diverse types, 2) the need for complex multi-agent coordination, which makes it challenging to achieve risky cooperation strategies, and 3) a large number of agents in the environment, leading to increased complexity when considering agent-to-agent relationships. To address the first two challenges, we leverage Reward Randomization in combination with Bayesian Optimization to train agents to exhibit diverse strategic behaviors, thereby mitigating the issue of risky cooperation. To address the challenge of learning in a large number of agents, we utilize MAPPO with parameter sharing to enhance learning efficiency. Experimental results demonstrate that within this multi-agent environment, agents can effectively learn multiple visually distinct behaviors, and the incorporation of these two techniques significantly improves agents' performance.
Community-Based Hierarchical Positive-Unlabeled (PU) Model Fusion for Chronic Disease Prediction
Authors: Yang Wu, Xurui Li, Xuhong Zhang, Yangyang Kang, Changlong Sun, Xiaozhong Liu
Venue: CIKM
Year: 2023

Positive-Unlabeled (PU) Learning is a challenge presented by binary classification problems where there is an abundance of unlabeled data along with a small number of positive data instances, which can be used to address chronic disease screening problem. State-of-the-art PU learning methods have resulted in the development of various risk estimators, yet they neglect the differences among distinct populations. To address this issue, we present a novel Positive-Unlabeled Learning Tree (PUtree) algorithm. PUtree is designed to take into account communities such as different age or income brackets, in tasks of chronic disease prediction. We propose a novel approach for binary decision-making, which hierarchically builds community-based PU models and then aggregates their deliverables. Our method can explicate each PU model on the tree for the optimized non-leaf PU node splitting. Furthermore, a mask-recovery data augmentation strategy enables sufficient training of the model in individual communities. Additionally, the proposed approach includes an adversarial PU risk estimator to capture hierarchical PU-relationships, and a model fusion network that integrates data from each tree path, resulting in robust binary classification results. We demonstrate the superior performance of PUtree as well as its variants on two benchmarks and a new diabetes-prediction dataset.
Rethinking Sentiment Analysis under Uncertainty
Authors: Yuefei Wu, Bin Shi, Jiarun Chen, Yuhang Liu, Bo Dong, Qinghua Zheng, Hua Wei
Venue: CIKM
Year: 2023

Sentiment Analysis (SA) is a fundamental task in natural language processing, which is widely used in public decision-making. Recently, deep learning have demonstrated great potential to deal with this task. However, prior works have mostly treated SA as a deterministic classification problem, and meanwhile, without quantifying the predictive uncertainty. This presents a serious problem in the SA, different annotator, due to the differences in beliefs, values, and experiences, may have different perspectives on how to label the text sentiment. Such situation will lead to inevitable data uncertainty and make the deterministic classification models feel puzzle to make decision. To address this issue, we propose a new SA paradigm with the consideration of uncertainty and conduct an expensive empirical study. Specifically, we treat SA as the regression task and introduce uncertainty quantification to obtain confidence intervals for predictions, which enables the risk assessment ability of the model and can improve the credibility of SA-aids decision-making. Experiments on five datasets show that our proposed new paradigm effectively quantifies uncertainty in SA while remaining competitive performance to point estimation, in addition to being capable of Out-Of-Distribution~(OOD) detection.
MTKDN: Multi-Task Knowledge Disentanglement Network for Recommendation
Authors: Haotian Wu, Bowen Xing, Ivor W. Tsang
Venue: CIKM
Year: 2023

Multi-task learning (MTL) is a widely adopted machine learning paradigm in recommender systems. However, existing MTL models often suffer from performance degeneration with negative transfer and seesaw phenomena. Some works attempt to alleviate the negative transfer and seesaw issues by separating task-specific and shared experts to mitigate the harmful interference between task-specific and shared knowledge. Despite the success of these efforts, task-specific and shared knowledge have still not been thoroughly decoupled. There may still exist unnecessary mixture between the shared and task-specific knowledge, which may harm MLT models' performances. To tackle this problem, in this paper, we propose multi-task knowledge disentanglement network (MTKDN) to further reduce harmful interference between the shared and task-specific knowledge. Specifically, we propose a novel contrastive disentanglement mechanism to explicitly decouple the shared and task-specific knowledge in corresponding hidden spaces. In this way, the unnecessary mixture between shared and task-specific knowledge can be reduced. As for optimization objectives, we propose individual optimization objectives for shared and task-specific experts, by which we can encourage these two kinds of experts to focus more on extracting the shared and task-specific knowledge, respectively. Additionally, we propose a margin regularization to ensure that the fusion of shared and task-specific knowledge can outperform exploiting either of them alone. We conduct extensive experiments on open-source large-scale recommendation datasets. The experimental results demonstrate that MTKDN significantly outperforms state-of-the-art MTL models. In addition, the ablation experiments further verify the necessity of our proposed contrastive disentanglement mechanism and the novel loss settings.
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
Authors: Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu
Venue: CIKM
Year: 2023

Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch.
SplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily
Authors: Bin Wu, Xinyu Yao, Boyan Zhang, Kuo-Ming Chao, Yinsheng Li
Venue: CIKM
Year: 2023

Fraudsters in the real world frequently add more legitimate links while concealing their direct ones with other fraudsters, leading to heterophily in fraud graphs, which is a problem that most GNN-based techniques are not built to solve. Several works have been proposed to tackle the issue from the spatial domain. However, researches on addressing the heterophily problem in the spectral domain are still limited due to a lack of understanding of spectral energy distribution in graphs with heterophily. In this paper, we analyze the spectral distribution with different heterophily degrees and observe that the heterophily of fraud nodes leads to the spectral energy moving from low-frequency to high-frequency. Further, we verify that splitting graphs using heterophilic and homophilic edges can obtain more significant expressions of signals in different frequency bands. The observation drives us to propose the spectral graph neural network, SplitGNN, to capture signals for fraud detection against heterophily. SplitGNN uses an edge classifier to split the original graph and adopts flexible band-pass graph filters to learn representations. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method. The code and data are available at https://github.com/Split-GNN/SplitGNN.
HiPo: Detecting Fake News via Historical and Multi-Modal Analyses of Social Media Posts
Authors: Tianshu Xiao, Sichang Guo, Jingcheng Huang, Riccardo Spolaor, Xiuzhen Cheng
Venue: CIKM
Year: 2023

In recent years, fake news has been a primary concern as it plays a significant role in influencing the political, economic, and social spheres. The scientific community has proposed several solutions to detect such fraudulent information. However, such solutions are unsuitable for social media posts since they cannot extract sufficient information from one-line textual and graphical content or are highly dependent on prior knowledge, which may be unavailable in the case of unprecedented events (e.g., breaking news). This paper tackles this issue by proposing HiPo, a novel multi-modal historical post-based fake news detection method. By combining the features extracted from the graphical and textual content, HiPo assesses the truthfulness of a social media post by building its historical context from prior off-label posts with high similarity, therefore, achieving online detection without maintaining a context or knowledge database. We evaluate the performance of HiPo via an exhaustive set of experiments involving four real-world datasets. Our method achieves a detection accuracy higher than 84%, outperforming the state-of-the-art methods in most experimental instances.
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
Authors: Youshao Xiao, Shangchun Zhao, Zhenglei Zhou, Zhaoxin Huan, Lin Ju, Xiaolu Zhang, Lin Wang, Jun Zhou
Venue: CIKM
Year: 2023

Recently, a new paradigm, meta learning, has been widely applied to Deep Learning Recommendation Models (DLRM) and significantly improves statistical performance, especially in cold-start scenarios. However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster. It is because the conventional deep learning pipeline is not optimized for two task-specific datasets and two update loops in meta learning. This paper provides a high-performance framework for large-scale training for Optimization-based Meta DLRM models over the G PU cluster, namely G -Meta. Firstly, G-Meta utilizes both data parallelism and model parallelism with careful orchestration regarding computation and communication efficiency, to enable high-speed distributed training. Secondly, it proposes a Meta-IO pipeline for efficient data ingestion to alleviate the I/O bottleneck. Various experimental results show that G-Meta achieves notable training speed without loss of statistical performance. Since early 2022, G-Meta has been deployed in Alipay's core advertising and recommender system, shrinking the continuous delivery of models by four times. It also obtains 6.48% improvement in Conversion Rate (CVR) and 1.06% increase in CPM (Cost Per Mille) in Alipay's homepage display advertising, with the benefit of larger training samples and tasks.
Exploiting Homeostatic Synaptic Modulation in Spiking Neural Networks for Semi-Supervised Graph Learning
Authors: Mingkun Xu
Venue: CIKM
Year: 2023

Semi-supervised graph learning (SSL) is an important task in machine learning that aims to make predictions based on a limited amount of labeled data and a larger set of unlabeled structured data, which can be effectively processed by biological neural networks. In this paper, we investigate the effects of the underlying homeostatic synaptic modulation (HSM) in spiking neural networks (SNNs) on such scenario. We propose a novel framework that integrates HSM into the spiking graph convolutional network to maintain stability by regulating the strength of synapses based on the activity of neurons, allowing for stable graph learning in a semi-supervised setting. Experimental results on citation benchmark datasets demonstrate that the proposed HSM mechanism can enable SNNs with superior capabilities of convergence and generalization, meanwhile possessing expected characteristics of sparsity and call-back phenomenon. The proposed framework provides a promising approach for exploiting HSM in neural network architectures for efficient graph learning.
An Efficient Selective Ensemble Learning with Rejection Approach for Classification
Authors: Hao Xu, Chiranjeet Chetia
Venue: CIKM
Year: 2023

Recent studies found that selective ensemble learning (e.g., dynamic ensemble selection) shows better predictive performance for classification tasks, compared to traditional static ensemble. However, there are some limitations of the available methods, such as high computational cost and multiple restrictions in base model ranking and aggregation (especially for class-imbalanced data modeling). Besides, the current methods make predictions for all data without measuring the credibility regarding different data patterns. This paper proposes a selective ensemble learning with rejection approach that aggregates base models from a different perspective. The approach introduces rejection measures to quantify base model credibility, and learns how to use the models according to their credibility on different sample patterns. It avoids the complexity in base model ranking and therefore is computationally more efficient than current methods. Any common evaluation metrics can be adopted in the selective ensemble strategy, which allows the developed model to handle class-imbalanced data properly. Also, a global rejection region is developed which indicates whether the ensemble model can provide reliable predictions for the targets. We implement the approach in the modeling of 12 datasets, including both class-imbalanced and class-balanced cases. Results show that the approach significantly reduces the inference time while showing promising performance, compared to 8 dynamic ensemble selection methods in the literature. Feature contributions and impacts of different rejection ratios on performance are also investigated to better demonstrate the approach.
DPGN: Denoising Periodic Graph Network for Life Service Recommendation
Authors: Hao Xu, Huixuan Chi, Danyang Liu, Sheng Zhou, Mengdi Zhang
Venue: CIKM
Year: 2023

Different from traditional e-commerce platforms, life service recommender systems provide hundreds of millions of users with daily necessities services such as nearby food ordering. In this scenario, users have instant intentions and living habits, which exhibit a periodic tendency to click or buy products with similar intentions. This can be summarized as the intentional periodicity problem, which was not well-studied in previous works. Existing periodic-related recommenders exploit time-sensitive functions to capture the evolution of user preferences. However, these methods are easily affected by the real noisy signal in life service platforms, wherein the recent noisy signals can mislead the instant intention and living habits modeling. We summarize it as the noise issue. Although there are some denoising recommenders, these methods cannot effectively solve the noise issue for intentional periodicity modeling. To alleviate the issues, we propose a novel Denoising Periodic Graph Network (DPGN) for life service recommendation. First, to alleviate the noisy signals and model the instant intention accurately, we propose (i) temporal pooling (TP) to encode the most representative information shared by recent behaviors; (ii) temporal encoding (TE) to encode the relative time intervals. Second, to capture the user's living habits accurately, we propose the memory mechanism to maintain a series of instant intentions in different time periods. Third, to further capture the intentional periodicity, we propose the temporal graph transformer (TGT) layer to aggregate temporal information. Last, the denoising task is further proposed to alleviate the noisy signals. Extensive experiments on both real-world public and industrial datasets validate the state-of-the-art performance of DPGN. Code is available in https://github.com/ytchx1999/DPGN
A Joint Training-Calibration Framework for Test-Time Personalization with Label Shift in Federated Learning
Authors: Jian Xu, Shao-Lun Huang
Venue: CIKM
Year: 2023

The data heterogeneity has been a challenging issue in federated learning in both training and inference stages, which motivates a variety of approaches to learn either personalized models for participating clients or test-time adaptations for unseen clients. One such approach is employing a shared feature representation and a customized classifier head for each client. However, previous works either neglect the global head with rich knowledge or assume the new clients have enough labeled data, which significantly limit their broader practicality. In this work, we propose a lightweight framework to tackle with the label shift issue during the model deployment by test priors estimation and model prediction calibration. We also demonstrate the importance of training a balanced global model in FL so as to guarantee the general effectiveness of prior estimation approaches. Evaluation results on benchmark datasets demonstrate the superiority of our framework for model adaptation in unseen clients with unknown label shifts.
MDCC: A Multimodal Dynamic Dataset for Donation-based Crowdfunding Campaigns
Authors: Xovee Xu, Jiayang Li, Fan Zhou
Venue: CIKM
Year: 2023

Crowdfunding platforms have become pivotal financial support avenues for diverse causes, yet the success rates are surprisingly low. Previous research has largely focused on reward-based crowdfunding, leaving donation-based platforms under-studied. In addition, the roles of multimodal data (e.g., textual descriptions and visual photos) and dynamic elements (e.g., sequences of donations, project updates, and comments) in influencing campaign success have been largely overlooked. This paper introduces MDCC, a Multimodal Dynamic dataset for donation-based Crowdfunding Campaigns, collected from 14,961 projects on GoFundMe, incorporates multimodal project information and captures project dynamics, thus providing a comprehensive tool for analyzing donation-based crowdfunding. The dataset is expected to inspire innovative methodologies and facilitate understanding of project success determinants. Our preliminary experiments demonstrate the significance of multimodal and dynamic crowdfunding data on predicting the success of donation-based projects.