OpenRaiser/Intern-Atlas huggingface dataset:
Factor Model-Based Large Covariance Estimation from Streaming Data Using a Knowledge-Based Sketch Matrix
Authors: Xiao Tan, Zhaoyang Wang, Hao Qian, Jun Zhou, Peibo Duan, Dian Shen, Meng Wang, Beilun Wang
Venue: CIKM
Year: 2024

Covariance matrix estimation is an important problem in statistics, with wide applications in finance, neuroscience, meteorology, oceanography, and other fields. However, when the data are high-dimensional and constantly generated and updated in a streaming fashion, the covariance matrix estimation faces huge challenges, including the curse of dimensionality and limited memory space. The existing methods either assume sparsity, ignoring any possible common factor among the variables, or obtain poor performance in recovering the covariance matrix directly from sketched data. To address these issues, we propose a novel method - KEEF: Knowledge-based Time and Memory Efficient Covariance Estimator in Factor Model and its extended variation. Our method leverages historical data to train a knowledge-based sketch matrix, which is used to accelerate the factor analysis of streaming data and directly estimates the covariance matrix from the sketched data. We provide theoretical guarantees, showing the advantages of our method in terms of time and space complexity, as well as accuracy. We conduct extensive experiments on synthetic and real-world data, comparing KEEF with several state-of-the-art methods, demonstrating the superior performance of our method.
Multi-turn Classroom Dialogue Dataset: Assessing Student Performance from One-on-one Conversations
Authors: Jiahao Chen, Zitao Liu, Mingliang Hou, Xiangyu Zhao, Weiqi Luo
Venue: CIKM
Year: 2024

Accurately judging student on-going performance is crucial for adaptive teaching. In this work, we focus on the task of automatically predicting students' levels of mastery of math questions from teacher-student classroom dialogue data in online one-on-one classes. As a step toward this direction, we introduce the Multi-turn Classroom Dialogue (MCD) dataset as a benchmark testing the capabilities of machine learning models in classroom conversation understanding of student performance judgment. Our dataset contains aligned multi-turn spoken language of 5000+ unique samples of solving grade-8 math questions collected from 500+ hours' worth of online one-on-one tutoring classes. In our experiments, we assess various state-of-the-art models on the MCD dataset, highlighting the importance of understanding multi-turn dialogues and handling noisy ASR transcriptions. Our findings demonstrate the dataset's utility in advancing research on automated student performance assessment. To encourage reproducible research, we make our data publicly available at https://github.com/ai4ed/MCD.
Multi-Granularity Modeling in Recommendation: from the Multi-Scenario Perspective
Authors: Yuhao Wang
Venue: CIKM
Year: 2024

In today's digital landscape, Deep Recommender Systems (DRS) play a crucial role in navigating and customizing online content for individual preferences. However, conventional methods, which mainly depend on single recommendation task, scenario, data modality and user behavior, are increasingly seen as insufficient due to their inability to accurately reflect users' complex and changing preferences. This gap underscores the need for multi-granularity modeling, which are central to overcoming these limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process, thus promising significant enhancements in recommendation precision, efficiency, and customization. In this paper, from the multi-scenario perspective, we illustrate our existing explorations and present results. Ultimately, we wish to highlight our multi-granularity approach sheds light on building the next generation of recommender system1 .
Boosting Entity Recognition by leveraging Cross-task Domain Models for Weak Supervision
Authors: Sanjay Agrawal, Srujana Merugu, Vivek Sembium
Venue: CIKM
Year: 2024

Entity Recognition (ER) is a common natural language processing task encountered in a number of real-world applications. For common domains and named entities such as places and organisations, there exists sufficient high quality annotated data and foundational models such as T5 and GPT-3.5 also provide highly accurate predictions. However, for niche domains such as e-commerce and medicine with specialized entity types, there is a paucity of labeled data since manual labeling of tokens is often time-consuming and expensive, which makes entity recognition challenging for such domains. Recent works such as NEEDLE [48] propose hybrid solutions to efficiently combine a small amount of strongly labeled (human-annotated) with a large amount of weakly labeled (distant supervision) data to yield superior performance relative to supervised training. The extensive noise in the weakly labeled data, however, remains a challenge. In this paper, we propose WeSDoM (Weak Supervision with Domain Models), which leverages pretrained encoder models from the same domain but different tasks to create domain ontologies that can enable the creation of less noisy weakly labeled data. Experiments on internal e-commerce and public biomedical NER datasets demonstrate that WeSDoM outperforms existing SOTA baselines by a significant margin. We achieve new SOTA F1 scores on two popular Biomedical NER datasets, BC5CDR-chem 94.27, BC5CDR-disease 91.23.
H2D: Hierarchical Heterogeneous Graph Learning Framework for Drug-Drug Interaction Prediction
Authors: Ran Zhang, Xuezhi Wang, Sheng Wang, Kunpeng Liu, Yuanchun Zhou, Pengfei Wang
Venue: CIKM
Year: 2024

Accurately predicting Drug-Drug Interactions (DDIs) is critical to designing effective drug combination therapies. Recently, Artificial Intelligence (AI)-powered DDI prediction approaches have emerged as a new paradigm. However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a H ierarchical H eterogeneous graph learning framework for D DI prediction, namely H2D. H2D employs an internal-to-external, local-to-global hierarchical perspective, exploiting intra-molecular multi-granularity structures and inter-molecular biomedical interactions to mutually enhance across hierarchical levels. Extensive experimental results demonstrate H2D's effectiveness on three real-world DDI prediction tasks (binary-class, multi-class, and multi-label). In sum, H2D achieves state-of-the-art performance in DDI prediction by leveraging the multi-scale graph structures, opening up new avenues in AI-powered DDI prediction.
CourIRL: Predicting Couriers' Behavior in Last-Mile Delivery Using Crossed-Attention Inverse Reinforcement Learning
Authors: Shuai Wang, Tongtong Kong, Baoshen Guo, Li Lin, Haotian Wang
Venue: CIKM
Year: 2024

Human behavior prediction is an essential AI-based task, which has inspired many real-world applications. In last-mile logistics, predicting couriers' behavior can benefit the couriers' preference learning and workflow optimization. In this paper, we devote to the behavioral prediction of courier workload and quantify their workload by the working time spent at each area of interest (AOI). Considering the behavior interpretability of inverse reinforcement learning (IRL), existing studies have applied IRL to some real-world transportation prediction scenarios. However, in last-mile logistics, the platform assigns multiple orders to each courier, and couriers also receive new tasks in real-time, which additionally influence the couriers' subsequent decisions. The uncertainty in decision spaces and dynamic the workflow distribution make it more challenging to predict the couriers' working time. In this paper, we propose CourIRL, a practical IRL-based framework leveraging cross-attention to integrate Couriers' historical and spatio-temporal features to predict their future working time. CourIRL formulates the couriers' pick-up and delivery tour as a sequential decision-making process and designs a model-free IRL to learn decision-making preference vectors. A multi-head cross-attention mechanism-based deep regression model is proposed for fine-grained working-time prediction. The results of extensive experiments on two real-world datasets demonstrate that the proposed CourIRL surpasses the state-of-the-art baselines by an average of 6.11% across settings, showing the efficacy and potential contributions of CourIRL in last-mile logistics.
Demonstration of a Multi-agent Framework for Text to SQL Applications with Large Language Models
Authors: Chen Shen, Jin Wang, Sajjadur Rahman, Eser Kandogan
Venue: CIKM
Year: 2024

The Text-to-SQL problem aims at developing natural language query interfaces for relational database systems by converting the text input into executable SQL queries. Recently, using Large Language Models (LLM) has emerged as a new paradigm for the Text-to-SQL problem. To this end, the LLM needs to understand not only user input but also information from the database. In this demo, we present multi-agent SQL (MageSQL), an LLM based Text-to-SQL approach that tackles the task by orchestrating multiple agents in a pipeline. We will showcase a user-friendly interface to demonstrate the inner workings of our approach that allows users to add and modify the agents with different functionalities, customize prompts, and see their impact on specific examples. Through several use cases, we will demonstrate how to (i) construct a Text-to-SQL pipeline with multiple agents; (ii) generate prompts for LLM with various templates and strategies; and (iii) monitor the results of natural language queries and perform debugging.
MPHDetect: Multi-View Prompting and Hypergraph Fusion for Malevolence Detection in Dialogues
Authors: Bo Xu, Xuening Qiao, Hongfei Lin, Linlin Zong
Venue: CIKM
Year: 2024

Malevolence detection in dialogues aims to identify harmful or inappropriate utterances, significantly impacting dialogue quality and user satisfaction. Although existing studies have shown promising performance by modeling interaction patterns from dialogue history, various malevolence-invoking factors, such as fine-grained emotions, evolving topics and user profiles, are often overlooked. To comprehensively consider these factors, we propose a hypergraph fusion model by employing multi-view LLM-driven prompts for malevolence detection in dialogues. Our model integrates emotion context, topic context, user profile context and interaction context, utilizing hypergraphs to establish high-order contextual relationships from multi views for deducing malevolence-invoking semantics. Experimental results on two benchmark datasets demonstrate that our model achieves the state-of-the-art performance.
Hierarchical Information Propagation and Aggregation in Disentangled Graph Networks for Audience Expansion
Authors: Li Lin, Xinyao Chen, Kaiwen Xia, Shuai Wang, Desheng Zhang, Tian He
Venue: CIKM
Year: 2024

With the development of the logistics industry, the user base of logistics services has expanded swiftly. This rapid increase in user scale presents significant challenges for logistics business management. A fundamental issue in such scenarios is audience expansion, which aims to find users willing to sign long-term services with logistics companies to foster business growth. Existing methods in addressing audience expansion mainly assume user modeling is entangled and neglects the inherent community structure among users. Due to these limitations, the effectiveness of traditional methods in achieving accurate user expansion is often restricted. Our work introduces a novel heterogeneous graph-based model, named Hi-DGN, which concentrates on the Hierarchical information propagation and aggregation in Disentangled Graph Networks for audience expansion. It consists of three main components: (i) the disentangled embedding layer to decouple user representations into different aspects, enabling the extraction of differentiated features; (ii) the hierarchical information propagation module partitions individual nodes into distinct groups and propagates information from group nodes to individual nodes hierarchically to capture diverse granularity representations; and (iii) the aggregation module to fuse all relation-specific embeddings to generate global node embeddings. Extensive experiments on two real-world datasets demonstrate the effectiveness of our method in various evaluation settings.
Efficient and Secure Contribution Estimation in Vertical Federated Learning
Authors: Juan Li, Rui Deng, Tianzi Zang, Mingqi Kong, Kun Zhu
Venue: CIKM
Year: 2024

As necessary information about whether cooperation can be reached, rewards should be determined in advance in Vertical Federated Learning (VFL). To determine reasonable rewards, participant contributions should be estimated precisely. We propose a Vertically Federated Contribution Estimation (VF-CE) method. VF-CE calculates Mutual Information (MI) between distributed features and the label using a neural network trained via VFL itself. Note that compensation for CE is low as it only covers computation costs, and reward for real VFL training is high as it needs to cover training costs as well as participants' contributions to model performance and the resulting business benefits. Because MI presents a strong positive correlation with the final model performance, contributions to model performance can be estimated based on contributions to MI. We integrate a scalar-level attention mechanism in MI neural network. The attention weights of participants are treated as their contributions. We find that attention weights can effectively measure contribution redundancy, as its Spearman correlation coefficient with Shapley value is as high as 0.963. We demonstrate that VF-CE also satisfies properties of balance, zero element, and symmetry concerning fairness, which are hallmark properties of Shapley value. Compared with existing work, we consider contribution redundancy precisely, efficiently output approximated Shapley values through one MI calculation instead of 2 n where n is the number of participants, and introduce no extra privacy risk except the inherent risk in VFL, i.e., gradient transmission.
DIFN: A Dual Intention-aware Network for Repurchase Recommendation with Hierarchical Spatio-temporal Fusion
Authors: Li Lin, Xin Xu, Hai Wang, Tian He, Desheng Zhang, Shuai Wang
Venue: CIKM
Year: 2024

Recommendation systems play a crucial role in both industrial applications and research fields, which target to understand user preferences and intentions to provide personalized services. Compared to conventional recommendations, repurchase recommendations aim to suggest suitable products to users that they used to buy based on their intention evolution. Existing research on product recommendation can mainly be divided into behavior sequence-based methods and graph-based methods. Although these methods represent user interests and preference features effectively, they still fail to model repurchase behaviors because (i) the environment causing repurchase intention change is neglected and (ii) the lack of feedback after purchasing makes it difficult to learn the impacts of diverse behaviors. To comprehensively consider these limitations, we design a D ual I ntention-aware F usion N etwork framework (DIFN) to understand the effects of environment and after-purchasing feedback on users' intentions. Firstly, a hierarchical graph-based multi-level relational attention module is designed to effectively extract basic user features and spatial features from complex environmental information. Then, we introduce a behavior intention module and a usage intention module for different types of feedback data. Finally, we propose a dual intention fusion network that effectively fuses user basic features with spatial attributes and user intention features with temporal attributes for recommendation. Comprehensive evaluations on real-world datasets show that our method exceeds state-of-the-art baselines, which show an average of 8.2% improvements in different metrics.
Hierarchical Spatio-Temporal Graph Learning Based on Metapath Aggregation for Emergency Supply Forecasting
Authors: Li Lin, Kaiwen Xia, Anqi Zheng, Shijie Hu, Shuai Wang
Venue: CIKM
Year: 2024

Integrated Warehousing and Distribution Supply Networks (IWDSN) have shown their high efficiency in E-commerce. Efficient supply capacity prediction is crucial for logistics systems to maintain the delivery capacity to meet users' requirements. However, unforeseen events such as extreme weather and public health emergencies pose challenges in supply forecasting. Previous work mainly infers supply optimization based on the invariant topology of logistic networks, neglecting dynamic routing and distinct node effects reacting to emergencies. To address these challenges, the hierarchical relations among warehouses, sorting centers, and delivery stations in logistic networks are necessary to learn the diverse reactions. In this paper, we propose a hierarchical spatio-temporal graph learning model to predict the emergency supply capacity of IWDSN based on micro and macro graphs. The micro graph shows transportation connectivity while the macro graph shows the geographical correlation. Specifically, it consists of three components. (1) For micro graphs, a metapath aggregation strategy is designed to capture dynamic routing information on both route-view and event-view graphs. (2) For macro graphs, a bipartite graph learning approach to extract spatial representations. (3) For spatio-temporal feature fusion, the spatio-temporal joint forecasting module combines the temporal feature from the time-series encoder with hierarchical spatial features to predict the future supply capacity. The extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model, which achieves state-of-the-art performance compared with advanced baselines.
Multi-Scale Contrastive Attention Representation Learning for Encrypted Traffic Classification
Authors: Shuo Yang, Xinran Zheng, Jinze Li, Jinfeng Xu, Edith C. H. Ngai
Venue: CIKM
Year: 2024

Encrypted traffic classification is essential for network security and management. However, the encrypted nature makes it challenging to extract representative features from raw traffic data. Existing end-to-end methods ignore byte correlations within packets and potential correlations among packets, hindering the learning of real traffic semantics and leading to suboptimal performance. This paper proposes MsETC, a multi-scale contrastive attention representation learning method for encrypted traffic classification. MsETC divides the raw packet byte sequence into multi-scale patches and then extracts dual views for contrastive learning from both the inter-patch and intra-patch perspectives. This allows the model to capture correlations among bytes within a packet as well as the potential interactions between packets. Extensive experiments on real-world datasets demonstrate that the proposed method achieves superior classification performance with lower complexity.
Scalable Transformer for High Dimensional Multivariate Time Series Forecasting
Authors: Xin Zhou, Weiqing Wang, Wray L. Buntine, Shilin Qu, Abishek Sriramulu, Weicong Tan, Christoph Bergmeir
Venue: CIKM
Year: 2024

Deep models for Multivariate Time Series (MTS) forecasting have recently demonstrated significant success. Channel-dependent models capture complex dependencies that channel-independent models cannot capture. However, the number of channels in real-world applications outpaces the capabilities of existing channel-dependent models, and contrary to common expectations, some models underperform the channel-independent models in handling high-dimensional data, which raises questions about the performance of channel-dependent models. To address this, our study first investigates the reasons behind the suboptimal performance of these channel-dependent models on high-dimensional MTS data. Our analysis reveals that two primary issues lie in the introduced noise from unrelated series that increases the difficulty of capturing the crucial inter-channel dependencies, and challenges in training strategies due to high-dimensional data. To address these issues, we propose STHD, the Scalable Transformer for High-Dimensional Multivariate Time Series Forecasting. STHD has three components: a) Relation Matrix Sparsity that limits the noise introduced and alleviates the memory issue; b) ReIndex applied as a training strategy to enable a more flexible batch size setting and increase the diversity of training data; and c) Transformer that handles 2-D inputs and captures channel dependencies. These components jointly enable STHD to manage the high-dimensional MTS while maintaining computational feasibility. Furthermore, experimental results show STHD's considerable improvement on three high-dimensional datasets: Crime-Chicago, Wiki-People, and Traffic. The source code and dataset are publicly available https://github.com/xinzzzhou/ScalableTransformer4HighDimensionMTSF.git.
Document-Level Relation Extraction Based on Heterogeneous Graph Reasoning
Authors: Dong Li, Miao Li, Zhi-Lei Lei, Baoyan Song, Xiaohuan Shan
Venue: CIKM
Year: 2024

The goal of document-level relation extraction is to extract semantic information from multiple sentences within a document and identify the relations between entities across sentences. However, effectively representing the document's content and reasoning about cross-sentence entities presents a formidable challenge. In this paper, we propose an efficient Document-Level Relation Extraction Model based on Heterogeneous Graph Reasoning (HGR-DREM), which enables relation extraction more accurate. Specifically, we first construct a document-level heterogeneous graph to comprehensively capture the semantic relations between entities. Then, we design a meta-path attention-based reasoning mechanism to enhance the mutual influence among graph nodes. Furthermore, we utilize an extended adjacency matrix to represent the heterogeneous graph and leverage graph convolutional neural networks (GCNs) to extract high-dimensional features. The experiments on a real-world dataset demonstrate the effectiveness of our proposed model. All codes have been released at https://github.com/NuyoaH-code/HGR-DREM.
Multi-Stage Refined Visual Captioning for Baidu Ad Creatives Generation
Authors: Yi Yang, Xinyu Zhao, Kang Zhao, Zhipeng Jin, Wen Tao, Lin Liu, Shuanglong Li
Venue: CIKM
Year: 2024

High-quality multimodal training data is of critical importance for improving of multimodal model performance. However, the utilization of web-crawled vision-caption pairs is hindered by the presence of noise and irrelevance, as well as a lack of Chinese data. Large Language Models (LLM) and Large Multimodal Models (LMM) has demonstrated promising performance in cross-modal understanding and generation. In light of this, we propose a Chinese visual captioning pipeline for the synthesis of high-quality data. Our pipeline is comprised of two phases: the initial training of an encoder for visual understanding; and the subsequent fine-tuning of a captioning model in a two-stage iterative human-in-the-loop process, where the captioning model incorporates the pre-trained vision encoder and LLM by a visual cross-attention querying transformer. Extensive experiments have been conducted to validate our framework, including both quantitative and qualitative evaluation of captions generated from images and videos. The synthesis pipeline has been integrated into the ad image creative generation process in Baidu Search Ads, resulting in enhanced capabilities in prompt following.