OpenRaiser/Intern-Atlas huggingface dataset:
Confidence-Aware Multi-Field Model Calibration
Authors: Yuang Zhao, Chuhan Wu, Qinglin Jia, Hong Zhu, Jia Yan, Libin Zong, Linxuan Zhang, Zhenhua Dong, Muyu Zhang
Venue: CIKM
Year: 2024

Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mismatches between predicted probabilities and true likelihoods due to the rapid shift of data distributions and intrinsic model biases. Calibration aims to address this issue by post-processing model predictions, and field-aware calibration can adjust model output on different feature field values to satisfy fine-grained advertising demands. Unfortunately, the observed samples corresponding to certain field values can be seriously limited to make confident calibrations, which may yield bias amplification and online disturbance. In this paper, we propose a confidence-aware multi-field calibration method, which adaptively adjusts the calibration intensity based on confidence levels derived from sample statistics. It also utilizes multiple fields for joint model calibration according to their importance to mitigate the impact of data sparsity on a single field. Extensive offline and online experiments show the superiority of our method in boosting advertising performance and reducing prediction deviations.
ECRT: Flexible Sequence Enhancement Framework for Cross-Domain Information Reuse in Recommendation
Authors: Weiqiang Zhao, Zi-Yuan Wu, Yatao Yang, Lifeng Hua, Hao Xiong
Venue: CIKM
Year: 2024

Chronological sequence of user-item interactions is a key feature in recommender systems, as it reveals the transition of users' interests as well as contextual relevance between nearby items. In modern e-commerce applications, various scenarios are usually integrated in one entry page, and the behavior sequence tend to be a combination of user-item interactions across multiple domains, such as on-sale goods, search queries, short videos, livestreams, etc. However, traditional domain-specified recommendations only deal with the interactions within the target domain, which neglects the overall profiles depicted by the behavior across the entire application, leading to overestimation of retargeted items as well as underestimation of unseen ones. So it is crucial to leverage cross-domain data from prominent domains to better supplement user behavior sequences for our targets. To tackle this problem, we propose the Enhanced Cross-domain Ralation Transfer (ECRT) framework to make flexible sequence augmentation with the assist of cross-domain information from other domains. We first employ similarity-based retrieval to obtain relevant sequence information from neighbor domains and build a heterogeneous graph to represent the complex behavior of users. Then we use innovative mining approaches to sample relevant information from the graph to supplement users' behavior sequences, and a hierarchical gated attention structure is used to aggregate these augmented information. We apply our proposed method in the livestream recommendation of Taobao channel pages, and the final experimental results indicate that our method demonstrates excellent performance in both online and offline environments, with an excess of up to 3.6% in main online indicators beyond past SOTA methods.
Correcting Biases of Shapley Value Attributions for Informative Machine Learning Model Explanations
Authors: Ningsheng Zhao, Jia Yuan Yu, Trang Bui, Krzysztof Dzieciolowski
Venue: CIKM
Year: 2024

Shapley value attribution (SVA) is an increasingly popular Explainable AI (XAI) approach that has been widely used in many recent applied studies to gain new insights into the underlying information systems. However, most existing SVA methods are error-prone, providing biased or unreliable explanations that fail to correctly capture the informational dependencies between features and model outputs. These explanation errors can be decomposed into two components: 1) observation bias which stems from data sparsity and leads to over-informativeness; and 2) structural bias which stems from distributional assumptions and leads to under-informativeness. To alleviate these biases, in this paper, we propose a series of refinement methods that combine out-of-distribution (OOD) detection and importance sampling. In essence, our methods aim to rectify the distribution drift caused by distributional assumptions. We apply our refinement methods to two popular SVAs: the marginal SVA and the surrogate model-based SVA. Our extensive experiments show that the proposed methods significantly enhance the informativeness of both local and global Shapley value-based explanations.
Scaling Vison-Language Foundation Model to 12 Billion Parameters in Baidu Dynamic Image Advertising
Authors: Xinyu Zhao, Kang Zhao, Zhipeng Jin, Yi Yang, Wen Tao, Xiaodong Chen, Cong Han, Shuanglong Li, Lin Liu
Venue: CIKM
Year: 2024

Dynamic image advertising is an add-on service in search advertising that matches visuals to search ads in real-time. However, the image matching system encompasses various sub-tasks with different objectives, increasing the complexity of achieving global optimization. Besides, prevalent long-tailed data poses a challenge to the multimodal representation learning in dynamic image advertising. Recently, vision-language pre-trained models have achieved remarkable performance across a variety of multimodal tasks, and implemented as the foundational representation model in electronic business scenarios. In this paper, to improve multimodal content understanding in Dynamic Image adVERtising, we present a viSion-language rEpresentation model (referred to as DIVERSE) that learns on cross-view and cross-token contrastive loss. Moreover, with large-scale curated advertising image-text data and extensive efficient training techniques, we scale DIVERSE to 12 billion parameters, which is the biggest Chinese multimodal representation model in industrial practices. Experiment results demonstrate the distinct advantages of DIVERSE12B in business datasets, with competitive performance on public benchmarks. Further evaluation in downstream applications including ad text-image retrieval, text-image relevance modeling, and image content moderation, shows that it outperforms previous separately-trained models across offline and online metrics. Moreover, DIVERSE12B has been implemented on the system primary traffic of Baidu Search Ads, bringing considerable increase to both user experience, and revenue for advertisers and search engine.
FGITrans: Cross-City Transformer for Fine-grained Urban Flow Inference
Authors: Yuhao Zheng, Yishuo Cai, Zihao Cai, Changjun Fan, Senzhang Wang, Jianxin Wang
Venue: CIKM
Year: 2024

Inferring the fine-grained urban flows based on the coarse-grained flow observations is practically important to many smart city-related applications. Adequate data is usually a prerequisite for existing machine learning methods, especially most deep learning models. However, many cities still suffer from the data scarcity issue due to the unbalanced city development levels. To mitigate this issue, we propose a novel cross-city fine-grained urban flow inference model named FGITrans, which aims to effectively transfer the knowledge from the data-rich cities to the data-scarce cities. Specifically, we design a weight-sharing triple-branch transformer framework which adopts self-attention and cross-attention for source/target city feature learning and domain alignment, respectively. Then, we propose a novel spatio-temporal adaptive embedding (STAE) layer for our transformer framework, and introduce a cross-city knowledge distillation (CKD) loss to narrow the cross-city disparities. The CKD loss explicitly enforces the framework to learn the discriminative domain-specific and domain-invariant representations simultaneously. Extensive experiments conducted on four large real-world datasets validate the effectiveness of FGITrans compared with the state-of-the-art baselines.
Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
Authors: Liangwei Nathan Zheng, Zhengyang Li, Chang George Dong, Wei Emma Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
Venue: CIKM
Year: 2024

Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4%, 94.9%, 60% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git.
AdaTM: Fine-grained Urban Flow Inference with Adaptive Knowledge Transfer across Multiple Cities
Authors: Yuhao Zheng, Jinyang Wu, Zihao Cai, Senzhang Wang, Jianxin Wang
Venue: CIKM
Year: 2024

Inferring the fine-grained urban traffic flows based on the coarse-grained traffic flow observations is practically important to many real applications for smart city. Existing approaches mostly rely on a large number of high quality urban flow data, but neglect the data sparsity issue which is common in real-world scenarios. Therefore, the performance of existing methods may not be promising towards cities that lack sufficient traffic flow data. How to design a more generalizable urban flow inference model that is able to effectively transfer knowledge across multiple cities is challenging and remains as an open research problem. In this paper, we propose a novel fine-grained urban flow inference model named AdaTM, which leverages the city-specific and city-invariant knowledge extracted from multiple cities. Specifically, we first propose a transformer-based urban feature extraction network named UBFormer to comprehensively extract the spatial-temporal features of multiple source cities. Then, we incorporate a learnable integrator to fuse the city-invariant and city-specific feature representations for the target city with sparse traffic flow data. Finally, we construct the feature representation of the target city through adaptive feature fusion and infer its fine-grained urban flows through the designed urban flow upsampler. Extensive experiments conducted on four large real-world datasets demonstrate the effectiveness of our approach.
AdaTrans: Adaptive Transfer Time Prediction for Multi-modal Transportation Modes
Authors: Shuxin Zhong, Hua Wei, Wenjun Lyu, Guang Yang, Zhiqing Hong, Guang Wang, Yu Yang, Desheng Zhang
Venue: CIKM
Year: 2024

Multi-modal transportation leverages the advantages of various transportation modes, leading to more efficient urban traveling services. Accurately predicting transfer times between different modes provides guidance for tasks such as trip planning and transportation management. Most existing transfer time prediction works rely on strong assumptions, e.g., predetermined routes, assumed speeds, and predefined downstream transportation timetables. However, these assumptions are hard to hold in practice due to internal factors like individual preferences and external factors like dynamic traffic conditions. These factors are dynamic and vary with location and time, presenting a significant challenge. To address this, we introduce an adaptive transfer time prediction framework, AdaTrans, to forecast personalized transfer times between upstream and downstream transportation modes. Firstly, an attribute learning module is designed to model the trends of internal factors. Then a spatial-temporal adaptive learning component is designed to learn dynamic external factors. Finally, an aggregation component with a capsule network is employed to fuse the influences of these factors. The extensive evaluation results in two real-world datasets demonstrate that AdaTrans effectively harnesses insights from internal and external factors, outperforming state-of-the-art methods by ~20%.
A Behavior-aware Cause Identification Framework for Order Cancellation in Logistics Service
Authors: Shuxin Zhong, Yahan Gu, Wenjun Lyu, Hongyu Lin, Guang Yang, Yao Lu, Guang Wang, Yu Yang, Desheng Zhang
Venue: CIKM
Year: 2024

Logistics platforms provide real-time door-to-door order pickup services to enhance customer convenience. However, a high volume of unexpected order cancellations negatively impacts both customer satisfaction and logistics profitability. Identifying whether these cancellations are due to customers' decisions or couriers' behaviors is crucial for implementing targeted operational improvements. While traditional methods directly interpret customer-courier dialogues, incorporating situational context (e.g., couriers' historical performance and current workloads) helps us to accurately understand the hidden content. The main challenges lie in dynamically correlating couriers' varying behaviors with dialogue content. To tackle this challenge, we develop COCO, a cause identification framework for order cancellation in logistics, which includes: i) Multi-modal features exploration, which analyzes dialogues and couriers' behaviors (both historical and current); ii) Multi-modal features aggregation, which uses a hierarchical attention mechanism to adaptively capture the dynamic correlations within dialogues and behaviors; iii) LLM-enhanced refinement, which leverages Large Language Models to accurately process a large number of unlabeled dialogues, significantly enhancing COCO's generalization and performance. Our extensive evaluation with JD Logistics demonstrates COCO's exceptional performance, achieving an 12.2% increase in precision and a 9.1% improvement in recall over existing methods. Furthermore, after deploying COCO at JD Logistics, it has achieved an accuracy of 89.5%, further demonstrating its practical utility.
Adaptive Cross-platform Transportation Time Prediction for Logistics
Authors: Shuxin Zhong, Wenjun Lyu, Zhiqing Hong, Guang Yang, Weijian Zuo, Haotian Wang, Guang Wang, Yu Yang, Desheng Zhang
Venue: CIKM
Year: 2024

Accurate prediction of order transportation time is essential for customer satisfaction in logistics. Existing methods based on origin-destination (OD) pairs do not consider the diversity of road segments, while route-based methods may fail to account for real-time traffic conditions due to the infrequent dispatch schedules of logistics vehicles. In reality, e-commerce platforms have collaborated with multiple logistics companies for parcel delivery, providing a richer dataset that offers a more comprehensive view of real-time transportation conditions. The key insight is that data from one company can serve as internal capability detectors and data from others can act as external environment detectors. However, a significant challenge arises in inferring travel-time-correlated station pairs across different companies, especially without full disclosure of station information. To address this, we design an Adaptive cross-platform Transportation time prediction framework built upon a hypergraph structure, named AdaTrans, comprising: i) A spatial-temporal routing graph learner employs node-centric and edge-centric hyperedges to address the complex, non-pairwise correlations among stations and station pairs within and across companies; ii) A spatial-temporal graph-based transportation time predictor that utilizes multi-task learning to enhance overall transportation time prediction by leveraging the correlations between interconnected sub-tasks (i.e., dwell and travel times prediction) Extensive evaluation with real-world data collected from JD.com, a leading e-commerce platform in China, demonstrates that consolidating records from other companies reduces RMSE, MAE, and MAPE by 12.63%, 5.18%, and 16.67%, compared to state-of-the-art methods.
Interaction-level Membership Inference Attack against Recommender Systems with Long-tailed Distribution
Authors: Da Zhong, Xiuling Wang, Zhichao Xu, Jun Xu, Wendy Hui Wang
Venue: CIKM
Year: 2024

Recommender systems (RSs) are susceptible to Interaction-level Membership Inference Attacks (IMIAs), which aim to determine whether specific user-item interactions are present in the training data of the target RS. However, existing IMIAs struggle with inferring the membership of tail interactions, i.e., the interactions involving tail items, due to the limited information available about these items. This paper introduces MINER, a new IMIA designed to enhance attack performance against RSs with long-tailed item distribution. MINER addresses the information scarcity of tail items at both the feature and sample levels. At the feature level, MINER leverages the Knowledge Graphs (KGs) to obtain the auxiliary knowledge of tail items. At the sample level, MINER designs a Bilateral-Branch Network (BBN) as the attack model. The BBN trains two branches independently, with one branch trained on interaction samples with the original long-tailed item distribution and the other on interaction samples with a more balanced item distribution. The outputs of the two branches are aggregated using a cumulative learning component. Our experimental results demonstrate that MINER significantly enhances the attack accuracy of IMIA, especially for tail interactions. Beyond attack design, we design a defense mechanism named RGL to defend against MINER. Empirical evaluations demonstrate that RGL effectively mitigates the privacy risks posed by MINER while preserving recommendation accuracy. Our code is available at https://github.com/dzhong2/MINER.
Long-Term Hydrologic Time Series Prediction with LSPM
Authors: Sicheng Zhou, David C. Anastasiu
Venue: CIKM
Year: 2024

Predicting multivariate time series has been a topic of interest among researchers for a long time, especially in hydrological prediction. Due to the presence of extreme events, hydrological prediction requires capturing long-range dependencies and modeling rare but significant extreme values. Accurate prediction of these dependencies is often accomplished using complex models, such as stacked RNNs or transformer-based models, which can be computationally expensive and challenging to train. In addition, existing studies have identified a strong correlation between streamflow and rainfall data. However, the use of additional input data in these studies has often been insufficient, resulting in predictions with low accuracy. In this paper, we address these issues and propose LSPM, a Long Short-term Polar-Learning time series forecasting Model. LSPM learns polar representations through a feature reuse method called EDDU (Encoder Double-Decoder Unit). EDDU creatively incorporates exogenous input to generate long-term predictions based on these learned representations. To maximize the use of indicator sequences from exogenous data, LSPM enhances short-term predictions by a carefully designed loss function and integrates them into the overall forecast, improving robustness to short-term severe events. Experiments on four real-life hydrologic streamflow datasets demonstrate that LSPM significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.
A Power Method to Alleviate Over-smoothing for Recommendation
Authors: Peng Zhou, Yachao Cui, Han Cao
Venue: CIKM
Year: 2024

In recent years, graph convolution networks (GCNs) have been widely used in recommender systems due to high-order node information propagation and aggregation mechanisms. However, existing GCN-based recommender systems drop sharply in performance as the depth of the network increases. This phenomenon is called over-smoothing, which refers to the fact that the embeddings of all nodes become more similar and indistinguishable. Previous works have rarely explored over-smoothing from characteristics of the recommendation field. Specifically, we found experimentally that too many layers can lead to such large loss values that they are difficult to decrease. After theoretical analysis, we can effectively solve the problem of difficulty in decreasing the loss value by adding only a hyperparameter, called "power". This hyperparameter can effectively control the smoothness and alleviate the over-smoothing problem. Experiments on four public datasets demonstrate that this hyperparameter can effectively improve performance.
REDI: Recurrent Diffusion Model for Probabilistic Time Series Forecasting
Authors: Shiyang Zhou, Zehao Gu, Yun Xiong, Yang Luo, Qiang Wang, Xiaofeng Gao
Venue: CIKM
Year: 2024

Time series forecasting (TSF) consists of point prediction and probabilistic forecasting. Unlike point forecasting which predicts an expected value of a future target, probabilistic time series forecasting models the uncertainty in data by predicting the distribution of future values, which enhances decision-making flexibility and improves risk management. Traditional probabilistic forecasting methods usually assume a fixed distribution of data, which is not always true for time series. Recently, there have been efforts to adapt diffusion models for time series owing to their exceptional ability to model the distribution of data without prior assumptions. However, how to apply advantages of diffusion models to time series forecasting remains a substantial challenge due to specific issues in time series such as distribution drift and complex dynamic temporal patterns. In this paper, we focus on the adaptation of diffusion models for time series forecasting. We propose REDI, a recurrent diffusion model that achieves effective probabilistic time series prediction with recurrent forward diffusion process and step-aware guidance in backward denoising process. The recurrent forward diffusion process enables the model to pay more attention to the impact of recent history on future values during the diffusion process, while the step-aware guidance facilitates precise guidance based on historical information during the denoising process. We conduct experiments on 5 real-world datasets and achieve average rankings of 1.8 for deterministic metrics and 1.5 for probabilistic metrics across 12 baselines, which strongly demonstrates the effectiveness of REDI.