OpenRaiser/Intern-Atlas huggingface dataset:
Mystique: A Budget Pacing System for Performance Optimization in Online Advertising
Authors: Rotem Stram, Rani Abboud, Alex Shtoff, Oren Somekh, Ariel Raviv, Yair Koren
Venue: WWW
Year: 2024

Online advertising plays a pivotal role in sustaining the accessibility of free content on the Internet, serving as a primary revenue source for websites and online services. This dynamic marketplace sees advertisers allocating budgets and competing for the opportunity to present ads to users engaging with web pages, online services, and mobile apps. Modern online advertising often employs first-price auctions to determine ad placements. Yet, conducting auctions as isolated events in a greedy manner, may lead to sub-optimal results, necessitating some form of budget pacing. Traditionally, budget pacing has been achieved through hard throttling, where ads or campaigns are selectively made eligible for each auction using a biased coin-toss with a specified probability (or pacing-signal). More recently, the pacing signal has been leveraged to soft throttle ads, and is used as a multiplicative factor on their bids, thus enabling participation in all auctions but with potentially modified bids. In this study, we introduce Mystique, a "soft" throttling-based budget pacing system. Mystique operates on two levels: it utilizes spending data to establish a daily target spending curve for each campaign, and continuously updates a pacing signal to align the actual spending with this curve. Our offline evaluation in a complex simulated marketplace, demonstrates Mystique's ability to outperform several baseline algorithms, enabling budget depletion while securing more opportunities. Mystique has been in production for several years now, serving a major native advertising marketplace, and successfully pacing over one billion USD annually.
Enabling Patient-side Disease Prediction via the Integration of Patient Narratives
Authors: Zhixiang Su, Yinan Zhang, Jiazheng Jing, Jie Xiao, Zhiqi Shen
Venue: WWW
Year: 2024

Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measures. However, most recent disease prediction approaches heavily rely on laboratory test outcomes (e.g., blood tests and medical imaging from X-rays). Gaining access to such data for precise disease prediction is often a complex task from the standpoint of a patient and is always only available post-patient consultation. To make disease prediction available from patient-side, we propose Personalized Medical Disease Prediction (PoMP), which predicts diseases using patient health narratives including textual descriptions and demographic information. By applying PoMP, patients can gain a clearer comprehension of their conditions, empowering them to directly seek appropriate medical specialists and thereby reducing the time spent navigating healthcare communication to locate suitable doctors. We conducted extensive experiments using real-world data from Haodf to showcase the effectiveness of PoMP.
MetroGNN: Metro Network Expansion with Reinforcement Learning
Authors: Hongyuan Su, Yu Zheng, Jingtao Ding, Depeng Jin, Yong Li
Venue: WWW
Year: 2024

Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement learning framework to address a Markov decision process within an urban heterogeneous multi-graph. Our approach employs an attentive policy network that intelligently selects nodes based on information captured by a graph neural network. Experiments on real-world urban data demonstrate that our proposed methodology substantially improve the satisfied transportation demands by over 30% when compared with state-of-the-art methods. Codes are published at https://github.com/tsinghua-fib-lab/MetroGNN.
Discovering and Measuring CDNs Prone to Domain Fronting
Authors: Karthika Subramani, Roberto Perdisci, Pierros-Christos Skafidas, Manos Antonakakis
Venue: WWW
Year: 2024

Domain fronting is a network communication technique that involves leveraging (or abusing) content delivery networks (CDNs) to disguise the final destination of network packets by presenting them as if they were intended for a different domain than their actual endpoint. This technique can be used for both benign and malicious purposes, such as circumventing censorship or hiding malware-related communications from network security systems. Since domain fronting has been known for a few years, some popular CDN providers have implemented traffic filtering approaches to curb its use at their CDN infrastructure. However, it remains unclear to what extent domain fronting has been mitigated. To better understand whether domain fronting can still be effectively used, we propose a systematic approach to discover CDNs that are still prone to domain fronting. To this end, we leverage passive and active DNS traffic analysis to pinpoint domain names served by CDNs and build an automated tool that can be used to discover CDNs that allow domain fronting in their infrastructure. Our results reveal that domain fronting is feasible in 22 out of 30 CDNs that we tested, including some major CDN providers like Akamai and Fastly. This indicates that domain fronting remains widely available and can be easily abused for malicious purposes.
Triage of Messages and Conversations in a Large-Scale Child Victimization Corpus
Authors: Prasanna Lakkur Subramanyam, Mohit Iyyer, Brian Neil Levine
Venue: WWW
Year: 2024

Children are among the most vulnerable online populations. Reports of child sexual exploitation on social media and apps have grown annually at an alarming rate and are overwhelming investigators. Even a single case can require examining millions of messages involving hundreds of victims. Triage and prioritization based on victims' experiences is an unfortunate necessity. Using a chat dataset of more than 3 million messages between victims and perpetrators, we evaluate and contribute tools for analyzing the experiences of victims of sexual exploitation. We develop both supervised and unsupervised methods to classify messages into categories of interest to law enforcement, such as age requests, persuasion, and sexual messages. We also introduce a conversation clustering technique to illuminate differences among victims' experiences based on their chat history. Through a qualitative analysis, we demonstrate that the learned clusters are coherent and represent distinct conversation patterns. For example, we can distinguish groups of users who never comply with sexual requests, comply after a few conversations, or comply immediately after being targeted. We expect this approach and associated visualizations will aid law enforcement, industry moderators, and sociologists who need to analyze massive corpora in this domain. Finally, we validate prior models derived from conversations involving adults pretending to be minors and provide statistics that could help undercover adults more accurately portray minor victims.
Aligning Out-of-Distribution Web Images and Caption Semantics via Evidential Learning
Authors: Guohao Sun, Yue Bai, Xueying Yang, Yi Fang, Yun Fu, Zhiqiang Tao
Venue: WWW
Year: 2024

Vision-language models, pre-trained on web-scale datasets, have the potential to greatly enhance the intelligence of web applications (e.g., search engines, chatbots, and art tools). Precisely, these models align disparate domains into a co-embedding space, achieving impressive zero-shot performance on multi-modal tasks (e.g., image-text retrieval, VQA). However, existing methods often rely on well-prepared data that less frequently contain noise and variability encountered in real-world scenarios, leading to severe performance drops in handling out-of-distribution (OOD) samples. This work first comprehensively analyzes the performance drop between in-distribution (ID) and OOD retrieval. Based on empirical observations, we introduce a novel approach, Evidential Language-Image Posterior (ELIP), to achieve robust alignment between web images and semantic knowledge across various OOD cases by leveraging evidential uncertainties. The proposed ELIP can be seamlessly integrated into general image-text contrastive learning frameworks, providing an efficient fine-tuning approach without exacerbating the need for additional data. To validate the effectiveness of ELIP, we systematically design a series of OOD cases (e.g., image distortion, spelling errors, and a combination of both) on two benchmark datasets to mimic noisy data in real-world web applications. Our experimental results demonstrate that ELIP improves the performance and robustness of mainstream pre-trained vision-language models facing OOD samples in image-text retrieval tasks.
Collaborative-Enhanced Prediction of Spending on Newly Downloaded Mobile Games under Consumption Uncertainty
Authors: Peijie Sun, Yifan Wang, Min Zhang, Chuhan Wu, Yan Fang, Hong Zhu, Yuan Fang, Meng Wang
Venue: WWW
Year: 2024

With the surge in mobile gaming, accurately predicting user spending on newly downloaded games has become paramount for maximizing revenue. However, the inherently unpredictable nature of user behavior poses significant challenges in this endeavor. To address this, we propose a robust model training and evaluation framework aimed at standardizing spending data to mitigate label variance and extremes, ensuring stability in the modeling process. Within this framework, we introduce a collaborative-enhanced model designed to predict user game spending without relying on user IDs, thus ensuring user privacy and enabling seamless online training. Our model adopts a unique approach by separately representing user preferences and game features before merging them as input to the spending prediction module. Through rigorous experimentation, our approach demonstrates notable improvements over production models, achieving a remarkable 17.11% enhancement on offline data and an impressive 50.65% boost in an online A/B test. In summary, our contributions underscore the importance of stable model training frameworks and the efficacy of collaborative-enhanced models in predicting user spending behavior in mobile gaming. The code associated with this paper has also been released at the following link https://doi.org/10.5281/zenodo.10775846.
Matching Feature Separation Network for Domain Adaptation in Entity Matching
Authors: Chenchen Sun, Yang Xu, Derong Shen, Tiezheng Nie
Venue: WWW
Year: 2024

Entity matching (EM) determines whether two records from different data sources refer to the same real-world entity. It is a fundamental task in knowledge graph construction and data integration. Currently, deep learning (DL) based EM methods have achieved state-of-the-art (SOTA) results. However, apply-ing DL-based EM methods often costs a lot of human efforts to label the data. To address this challenge, we propose a new do-main adaptation (DA) framework for EM called Matching Fea-ture Separation Network (MFSN). We implement DA by sepa-rating private and common matching features. Briefly, MFSN first uses three encoders to explicitly model the private and common matching features in both the source and target do-mains. Then, it transfers the knowledge learned from the source common matching features to the target domain. We also pro-pose an enhanced variant called Feature Representation and Separation Enhanced MFSN (MFSN-FRSE). Compared with MFSN, it has superior feature representation and separation capabilities. We evaluate the effectiveness of MFSN and MFSN-FRSE on twelve DA in EM tasks. The results show that our framework is approximately 7% higher in F1 score on average than the previous SOTA methods. Then, we verify the effec-tiveness of each module in MFSN and MFSN-FRSE by ablation study. Finally, we explore the optimal strategy of each module in MFSN and MFSN-FRSE through detailed tests.
Interpretable Knowledge Tracing with Multiscale State Representation
Authors: Jianwen Sun, Fenghua Yu, Qian Wan, Qing Li, Sannyuya Liu, Xiaoxuan Shen
Venue: WWW
Year: 2024

Knowledge Tracing (KT) is vital for education, continuously monitoring students' knowledge states (mastery of knowledge) as they interact with online education materials. Despite significant advancements in deep learning-based KT models, existing approaches often struggle to strike the right balance in granularity, leading to either overly coarse or excessively fine tracing and representation of students' knowledge states, thereby limiting their performance. Additionally, achieving a high-performing model while ensuring interpretability presents a challenge. Therefore, in this paper, we propose a novel approach called Multiscale-state-based Interpretable Knowledge Tracing (MIKT). Specifically, MIKT traces students' knowledge states on two scales: a coarse-grained representation to trace students' domain knowledge state, and a fine-grained representation to monitor their conceptual knowledge state. Furthermore, the classical psychological measurement model, IRT (Item Response Theory), is introduced to explain the prediction process of MIKT, enhancing its interpretability without sacrificing performance. Additionally, we extended the Rasch representation method to effectively handle scenarios where questions are associated with multiple concepts, making it more applicable to real-world situations. We extensively compared MIKT with 20 state-of-the-art KT models on four widely-used public datasets. Experimental results demonstrate that MIKT outperforms other models while maintaining its interpretability. Moreover, experimental observations have revealed that our proposed extended Rasch representation method not only benefits MIKT but also significantly improves the performance of other KT baseline models. The code can be found at https://github.com/lilstrawberry/MIKT.
Efficient Computation for Diagonal of Forest Matrix via Variance-Reduced Forest Sampling
Authors: Haoxin Sun, Zhongzhi Zhang
Venue: WWW
Year: 2024

The forest matrix of a graph, particularly its diagonal elements, has far-reaching implications in network science and machine learning. The state-of-the-art algorithms for the diagonal of forest matrix computation are based on the fast Laplacian solver. However, these algorithms encounter limitations when applied to digraphs due to the incapacity of the Laplacian solver. To overcome the issue, in this paper, we propose three novel sampling-based algorithms:SCF,SCFV,and SCFV+. Our first algorithm SCF leverages a probability interpretation of the diagonal of the forest matrix and utilizes an extension of Wilson's algorithm to sample spanning converging forests. To reduce the variance in the forest sampling, we develop two novel variance-reduced techniques. The first technique, leading to the proposal of the SCFV algorithm, is inspired by opinion dynamics in graphs and applies matrix-vector iteration to the spanning forest sampling. While SCFV achieves reduced variance compared to SCF, the cross-product term in its variance expression can be complex and potentially large in certain graphs. Therefore, we develop another technique, leading to a new iteration equation and the SCFV+ algorithm. SCFV+ achieves further reduced variance without the cross-product term in the variance of SCFV. We prove that SCFV+ can achieve a relative error guarantee with high probability and maintain a linear time complexity relative to the number of nodes in the graph, presenting a superior theoretical result compared to state-of-the-art algorithms. Finally, we conduct extensive experiments on various real-world networks, showing that our algorithms achieve better estimation accuracy and are more time-efficient than the state-of-the-art algorithms. Particularly, our algorithms are scalable to massive graphs with more than twenty million nodes in both undirected and directed graphs.
Requirements and Challenges for Query Execution across Decentralized Environments
Authors: Ruben Taelman
Venue: WWW
Year: 2024

Due to the economic and societal problems being caused by the Web's growing centralization, there is an increasing interest in de-centralizing data on the Web. This decentralization does however cause a number of technical challenges. If we want to give users in decentralized environments the same level of user experience as they are used to with centralized applications, we need solutions to these challenges. We discuss how query engines can act as layer between applications on the one hand, and decentralized environments on the other hand, Query engines therefore act as an abstraction layer that hides the complexities of decentralized data management for application developers. In this article, we outline the requirements for query engines over decentralized environments. Furthermore, we show how existing approaches meet these requirements, and which challenges remain. As such, this article offers a high-level overview of a roadmap in the query and decentralization research domains.
Graphameleon: Relational Learning and Anomaly Detection on Web Navigation Traces Captured as Knowledge Graphs
Authors: Lionel Tailhardat, Benjamin Stach, Yoan Chabot, Raphaël Troncy
Venue: WWW
Year: 2024

User and Entity Behavior Analytics (UEBA) is key for managing security risks on information systems and comprehending user activities' impact on the network infrastructure. However, accessing network traffic and Web logs is challenging due to encryption or decentralized systems. Qualifying activities also requires contextualizing them according to the network's topology, as it determines potential exchanges and carries information about which services are used. This complexity hinders learning behavioral patterns when precise user action sequences are needed. We propose to tackle these challenges with Graphameleon, an open-source Web extension for capturing Web navigation traces. We model user activities in an RDF Knowledge Graph (KG), drawing from the UCO and NORIA-O ontologies. With this approach, we are able to distinguish analytics strategies implemented across different websites.
Term Importance for Transformer-Based QA Retrieval: A Case Study of StackExchange
Authors: Bryan Zhi Yang Tan, Hady W. Lauw
Venue: WWW
Year: 2024

Question-answering (QA) retrieval is the task of retrieving the most relevant answer to a given question from a collection of answers. Various approaches to QA retrieval have been developed recently. One successful and popular model is Contextualized Late Interaction over BERT (ColBERT), a transformer-based approach that adopts a query-document scoring mechanism that retains the granularity of transformer matching, whilst improving on efficiency. However, one key limitation is that it requires further fine-tuning for new query or collection types. In this work, we explore and propose several non-parametric retrieval augmentation methods based on explicit signals of term importance that improve over ColBERT's baseline performance. In particular, we consider the QA retrieval task in the context of StackExchange question-answering forum, verifying the effectiveness of our methods in this setting.
Air-CAD: Edge-Assisted Multi-Drone Network for Real-time Crowd Anomaly Detection
Authors: Yuanzheng Tan, Qing Li, Junkun Peng, Zhenhui Yuan, Yong Jiang
Venue: WWW
Year: 2024

Drones connected via the web are increasingly being used for crowd anomaly detection (CAD). Existing solutions, however, face many challenges, such as low accuracy and high latency due to drones' dynamic shooting distances and angles as well as limited computing and networking capabilities. In this paper, we propose Air-CAD, an edge-assisted multi-drone network that uses air-ground cooperation to achieve fast and accurate CAD. Air-CAD consists of two stages: person detection and multi-feature analysis. To improve CAD accuracy, Air-CAD dynamically adjusts the inference of person detection model based on drones' shooting distances and assigns appropriate feature analysis tasks to drones shooting at variable angles. To achieve fast CAD, edge devices connected to drones are deployed to offload assigned feature analysis tasks from drones. Air-CAD schedules the connection between each drone and edge to accelerate processing based on drone's assigned task and the computing/network resources of the edge device. To validate the performance of Air-CAD, we generate a new simulated human stampede dataset captured from various drone-view recordings. We deploy and evaluate Air-CAD in both simulation and real-world testbed. Experimental results show that Air-CAD achieves 95.33% AUROC and real-time inference latency within 0.47 seconds.
Enhancing Progressive Diagnosis Prediction in Healthcare with Continuous Normalizing Flows
Authors: Yanchao Tan, Hengyu Zhang, Zihao Zhou, Guofang Ma, Fan Wang, Weiming Liu, Xinting Liao, Vicki Stover Hertzberg, Carl Yang
Venue: WWW
Year: 2024

Progressive diagnosis prediction in healthcare is a promising yet challenging task. Existing studies usually assume a pre-defined prior for generating patient distributions (e.g., Gaussian). However, the inferred approximate posterior can deviate from the real-world distribution, which further affects the modeling of continuous disease progression over time. To alleviate such inference bias, we propose an enhanced progressive diagnostic prediction model (i.e., ProCNF), which integrates continuous normalizing flows (CNF) and neural ordinary differential equations (ODEs) to achieve more accurate approximations of patient health trajectories while capturing the continuity underlying disease progression. We first learn patient embeddings with CNF to construct a complex posterior approximation of patient distributions. Then, we devise a CNF-enhanced neural ODE module for progressive diagnostic prediction, which aims to improve the modeling of disease progression for individual patients. Extensive experiments on two real-world longitudinal EHR datasets show significant performance gains brought by our method over state-of-the-art competitors.
Recent Advances in Generative Information Retrieval
Authors: Yubao Tang, Ruqing Zhang, Weiwei Sun, Jiafeng Guo, Maarten de Rijke
Venue: WWW
Year: 2024

Generative retrieval (GR) has witnessed significant growth recently in the area of information retrieval. Compared to the traditional "index-retrieve-then-rank'' pipeline, the GR paradigm aims to consolidate all information within a corpus into a single model. Typically, a sequence-to-sequence model is trained to directly map a query to its relevant document identifiers (i.e., docids). This tutorial offers an introduction to the core concepts of the GR paradigm and a comprehensive overview of recent advances in its foundations and applications. We start by providing preliminary information covering foundational aspects and problem formulations of GR. Then, our focus shifts towards recent progress in docid design, training approaches, inference strategies, and applications of GR. We end by outlining challenges and issuing a call for future GR research.This tutorial is intended to be beneficial to both researchers and industry practitioners interested in developing novel GR solutions or applying them in real-world scenarios.