OpenRaiser/Intern-Atlas huggingface dataset:
Adversarial-Enhanced Causal Multi-Task Framework for Debiasing Post-Click Conversion Rate Estimation
Authors: Xinyue Zhang, Cong Huang, Kun Zheng, Hongzu Su, Tianxu Ji, Wei Wang, Hongkai Qi, Jingjing Li
Venue: WWW
Year: 2024

In real-world industrial scenarios, post-click conversion rate (CVR) prediction models are trained offline based on click events and subsequently applied online to both clicked and unclicked events. Unfortunately, unclicked events are inevitably difficult to estimate due to user self-selection, which leads to a degradation of CVR prediction accuracy. In order to estimate the prediction of unclicked events, the current mainstream Doubly Robust (DR) estimators introduce the concept of imputed errors. However, inaccuracies in imputed errors can increase the uncertainty in the generalization bound of CVR predictions, consequently resulting in a decline in the CVR prediction accuracy. To challenge this issue, we first present a theoretical analysis of the bias and variance inherent in DR estimators and then introduce a novel causal estimator that seeks to strike a balance between bias and variance within the DR framework, thus optimizing the learning of the imputation model in a more robust manner. Additionally, drawing inspiration from adversarial learning techniques, we propose a novel dual adversarial component, which learns from both the space level and the task level to eliminate the causal influence of input features on the CTR task (i.e., the click propensity), with the goal of achieving unbiased estimations. Our extensive experimental evaluations, conducted on both the widely used benchmark and the real-world large-scale Internet giant platform, convincingly demonstrate the effectiveness of our proposed scheme. Besides, we have released a high-quality industrial dataset named Tenc-UnionAds used for selection bias research in the advertising field.
Collaborative Metapath Enhanced Corporate Default Risk Assessment on Heterogeneous Graph
Authors: Zheng Zhang, Yingsheng Ji, Jiachen Shen, Yushu Chen, Xi Zhang, Guangwen Yang
Venue: WWW
Year: 2024

Default risk assessment for small companies is a tough problem in financial services. Recent efforts utilize advanced Heterogeneous Graph Neural Networks (HGNNs) with metapaths to exploit interactive features in corporate activities for risk analysis. However, few works are proposed for commercial banks. Given a real financial graph, how to detect corporate default risks? We identify two challenges for the task. (1) Massive noisy connections hinder HGNNs to achieve strong results. (2) Multiple semantic connections greatly increase transitive default risk, while existing aggregation schemes do not leverage such connection patterns. In this work, we propose a novel Heterogeneous Graph Co-Attention Network for corporate default risk assessment. Our model takes advantage of collaborative metapaths to distill risky features by a co-attentive aggregation mechanism. First, the local attention score models the importance of neighbors under each metapath by holistic metapath context. Second, the global attention score fuse local attention scores to filter valuable/noisy signals. Then, pairwise importance learning aims to enhance attention scores of multi-metapath neighbors for risky feature distillation. Extensive experiments on large-scale banking datasets demonstrate the effectiveness of our method.
Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling
Authors: Zheng Zhang, Qi Liu, Zirui Hu, Yi Zhan, Zhenya Huang, Weibo Gao, Qingyang Mao
Venue: WWW
Year: 2024

Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has gained paramount importance. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through the theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, in order to mitigate the impact of this factor, we introduce a novel Fairness-aware Adaptive Sampling framework for meTa-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Furthermore, we provide theoretical guarantees demonstrating the convergence of FAST. Finally, empirical experiments conducted on three datasets reveal that FAST effectively enhances fairness while maintaining high accuracy. The code for FAST is available at https://github.com/zhengz99/FAST.
DiffuRetrieval: A Chain-of-Thought Enhanced Diffusion Retrieval in Sponsored Search
Authors: Yadong Zhang, Siyu Lu, Qiang Liu, Xingxing Wang
Venue: WWW
Year: 2024

Embedding-based Retrieval (EBR) system is a fundamental component that supplies candidates for downstream ranking mechanisms in the sponsored search system. To enhance search experience and ensure effective retrieval, EBR usually accounts for various objectives including the semantic relevance and personalization of search results. However, traditional multi-task EBR models ignore the intrinsic progressive relationship between relevant and personalized candidates during a search. Recognizing this gap, we make the very first attempt to utilize the representation generation capabilities of Diffusion Models in EBR. In this paper, we present a novel model DiffuRetrieval to address the progressive objectives for high-quality item retrieval. In forward process, DiffuRetrieval incrementally corrupts item representations through controlled noise injection. Conversely, in reverse process, we refine the representations based on query information in a chain-of-thought manner, initially establishing coarse-grained relevance and progressively moving towards fine-grained personalization. Online A/B tests on Meituan sponsored search platform demonstrate that our approach markedly surpasses the baselines, delivering substantial improvements in revenue, relevance and personalization.
Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Meta
Authors: Wei Zhang, Dai Li, Chen Liang, Fang Zhou, Zhongke Zhang, Xuewei Wang, Ru Li, Yi Zhou, Yaning Huang, Dong Liang, Kai Wang, Zhangyuan Wang, Zhengxing Chen, Fenggang Wu, Minghai Chen, Huayu Li, Yunnan Wu, Zhan Shu, Mindi Yuan, Sri Reddy
Venue: WWW
Year: 2024

Effective user representations are pivotal in personalized advertising. However, stringent constraints on training throughput, serving latency, and memory, often limit the complexity and input feature set of online ads ranking models. This challenge is magnified in extensive systems like Meta's, which encompass hundreds of models with diverse specifications, rendering the tailoring of user representation learning for each model impractical. To address these challenges, we present Scaling User Modeling (SUM), a framework widely deployed in Meta's ads ranking system, designed to facilitate efficient and scalable sharing of online user representation across hundreds of ads models. SUM leverages a few designated upstream user models to synthesize user embeddings from massive amounts of user features with advanced modeling techniques. These embeddings then serve as inputs to downstream online ads ranking models, promoting efficient representation sharing. To adapt to the dynamic nature of user features and ensure embedding freshness, we designed SUM Online Asynchronous Platform (SOAP), a latency-free online serving system complemented with model freshness and embedding stabilization, which enables frequent user model updates and online inference of user embeddings upon each user request. We share our hands-on deployment experiences for the SUM framework and validate its superiority through comprehensive experiments. To date, SUM has been launched to hundreds of ads ranking models in Meta, processing hundreds of billions of user requests daily, yielding significant online metric gains and infrastructure cost savings.
Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
Authors: Zhen Zhang, Meihan Liu, Anhui Wang, Hongyang Chen, Zhao Li, Jiajun Bu, Bingsheng He
Venue: WWW
Year: 2024

Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. However, most methods require a labelled source graph to provide supervision signals, which might not be accessible in the real-world settings due to regulations and privacy concerns. In this paper, we explore the scenario of source-free unsupervised graph domain adaptation, which tries to address the domain adaptation problem without accessing the labelled source graph. Specifically, we present a novel paradigm called GraphCTA, which performs model adaptation and graph adaptation collaboratively through a series of procedures: (1) conduct model adaptation based on node's neighborhood predictions in target graph considering both local and global information; (2) perform graph adaptation by updating graph structure and node attributes via neighborhood contrastive learning; and (3) the updated graph serves as an input to facilitate the subsequent iteration of model adaptation, thereby establishing a collaborative loop between model adaptation and graph adaptation. Comprehensive experiments are conducted on various public datasets. The experimental results demonstrate that our proposed model outperforms recent source-free baselines by large margins.
ESCNet: Entity-enhanced and Stance Checking Network for Multi-modal Fact-Checking
Authors: Fanrui Zhang, Jiawei Liu, Jingyi Xie, Qiang Zhang, Yongchao Xu, Zheng-Jun Zha
Venue: WWW
Year: 2024

Recently, misinformation incorporating both texts and images has been disseminated more effectively than those containing text alone on social media, raising significant concerns for multi-modal fact-checking. Existing research makes contributions to multi-modal feature extraction and interaction, but fails to fully enhance the valuable semantic representations or excavate the intricate entity information. Besides, existing multi-modal fact-checking datasets are primarily focused on English and merely concentrate on a single type of misinformation, thereby neglecting a comprehensive summary and coverage of various types of misinformation. Taking these factors into account, we construct the first large-scale Chinese Multi-modal Fact-Checking (CMFC) dataset which encompasses 46,000 claims. The CMFC covers all types of misinformation for fact-checking and is divided into two sub-datasets, Collected Chinese Multi-modal Fact-Checking (CCMF) and Synthetic Chinese Multi-modal Fact-Checking (SCMF). To establish baseline performance, we propose a novel Entity-enhanced and Stance Checking Network (ESCNet), which includes Multi-modal Feature Extraction Module, Stance Transformer, and Entity-enhanced Encoder. The ESCNet jointly models stance semantic reasoning features and knowledge-enhanced entity pair features, in order to simultaneously learn effective semantic-level and knowledge-level claim representations. Our work offers the first step and establishes a benchmark for evidence-based, multi-type, multi-modal fact-checking.
CFinDEE: A Chinese Fine-Grained Financial Dataset for Document-Level Event Extraction
Authors: Tian Zhang, Maofu Liu, Bingying Zhou
Venue: WWW
Year: 2024

Document-level event extraction faces numerous challenges in accurately modeling real-world financial scenarios, particularly due to the inadequacies in existing datasets regarding data scale and fine-grained annotations. The development of datasets is a crucial factor in driving research progress; therefore, we present a high-quality Chinese document-level event extraction dataset, CFinDEE. This dataset, grounded in real-world financial news, defines 22 event types and 116 argument roles, annotating 26,483 events and 107,096 event arguments. CFinDEE aims to address these shortcomings by providing more comprehensive annotations and data augmentation, offering richer resources for document-level event extraction in the financial domain. CFinDEE extends data both horizontally and vertically, where horizontal expansion enriches the types of financial events, enhancing the diversity of the dataset; vertical expansion, by increasing the scale of the data, effectively boosts the practical value of the dataset. Experiments conducted on multiple advanced models have validated the high applicability and effectiveness of the CFinDEE dataset for document-level event extraction tasks in the financial field.
Towards Invariant Time Series Forecasting in Smart Cities
Authors: Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
Venue: WWW
Year: 2024

In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth. The advancement of deep neural networks has significantly improved forecasting performance. However, a notable challenge lies in the ability of these models to generalize well to out-of-distribution (OOD) time series data. The inherent spatial heterogeneity and domain shifts across urban environments create hurdles that prevent models from adapting and performing effectively in new urban environments. To tackle this problem, we propose a solution to derive invariant representations for more robust predictions under different urban environments instead of relying on spurious correlation across urban environments for better generalizability. Through extensive experiments on both synthetic and real-world data, we demonstrate that our proposed method outperforms traditional time series forecasting models when tackling domain shifts in changing urban environments. The effectiveness and robustness of our method can be extended to diverse fields including climate modeling, urban planning, and smart city resource management.
Cold Start or Hot Start? Robust Slow Start in Congestion Control with A Priori Knowledge for Mobile Web Services
Authors: Jia Zhang, Haixuan Tong, Enhuan Dong, Xin Qian, Mingwei Xu, Xiaotian Li, Zili Meng
Venue: WWW
Year: 2024

Mobile web services value a quick loading of contents in the first page, which is quantified by the above-the-fold time of the first page (first AFT) and is likely to fall into the slow start phase in congestion control. However, the widely deployed slow start mechanism is "cold start", which manually hardcodes the parameters and is not suitable for the first AFT of heterogeneous mobile web services. We revisit the slow start mechanism and find that it could be optimized with a priori knowledge. However, blindly relying on a priori knowledge is not robust enough to handle the fluctuating mobile networks and unpredictable application traffic. In this paper, we propose WiseStart, a "hot-start-based" slow start mechanism. WiseStart utilizes the priori knowledge to set the initial parameters, continuously probes the new connection to handle the fluctuating network conditions, and carefully adapts to the application-limit scenarios. We implement WiseStart in a popular mobile web service online in production. Comprehensive experiments demonstrate that WiseStart reduces the First AFT by 25.43% and the average RCT at connection establishment by 16.15% compared to the default slow start mechanism and other state-of-the-art baselines.
HSDirSniper: A New Attack Exploiting Vulnerabilities in Tor's Hidden Service Directories
Authors: Qingfeng Zhang, Zhiyang Teng, Xuebin Wang, Yue Gao, Qingyun Liu, Jinqiao Shi
Venue: WWW
Year: 2024

Tor hidden services (HSs) are used to provide anonymous services to users on the Internet without revealing the location of the servers. However, existing approaches have proven ineffective in mitigating the misuse of hidden services. Our investigation reveals that the latest iteration of Tor hidden services still exhibits vulnerabilities related to Hidden Service Directories (HSDirs). Building upon this identified weakness, we introduce the HSDirSniper attack, which leverages a substantial volume of descriptors to inundate the HSDir's descriptor cache. This results in the HSDir purging all stored descriptors, thereby blocking arbitrary hidden services. Notably, our attack represents the most practical means of blocking hidden services within the current high-adversarial context. The advantage of the HSDirSniper attack lies in its covert nature, as the targeted hidden service remains unaware of the attack. Additionally, the successful execution of this attack does not require the introduction of a colluding routing node within the Tor Network. We conducted comprehensive experiments in the real-world Tor Network, and the experimental results show that an attacker equipped with a certain quantity of hidden servers can render arbitrary hidden services inaccessible up to 90% of the time. To ascertain the potential scope of damage that the HSDirSniper attack can inflict upon hidden services, we provide a formal analytical framework for quantifying the cost of the HSDirSniper attack. Finally, we discuss the ethical concerns and countermeasures.
Retention Depolarization in Recommender System
Authors: Xiaoying Zhang, Hongning Wang, Yang Liu
Venue: WWW
Year: 2024

Repeated risk minimization is a popular choice in real-world recommender systems driving their recommendation algorithms to adapt to user preferences and trends. However, numerous studies have shown that it exacerbates retention disparities among user groups, resulting in polarization within the user population. Given the primary objective of improving long-term user engagement in most industrial recommender systems and the significant commercial benefits from a diverse user population, enforcing retention fairness across user population is therefore crucial. Nonetheless, this goal is highly challenging due to the unknown dynamics of user retention (e.g., when a user would abandon the system) and the simultaneous aim to maximize the experience of every user. In this paper, we propose ReFair, the first computational framework that continuously improves recommendation algorithms while ensuring long-term retention fairness in the entire user population. ReFair alternates between environment learning (i.e., estimate the user retention dynamics) and fairness constrained policy improvement with respect to the estimated environment, while effectively handling uncertainties in the estimation. Our solution provides strong theoretical guarantees for long-term recommendation performance and retention fairness violation. Empirical experiments on two real-world recommendation datasets also demonstrate its effectiveness in realizing these two goals.
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation
Authors: Zezhong Zhang, Ted Tao Yuan
Venue: WWW
Year: 2024

Online controlled experiments have emerged as industry gold standard for assessing new web features. As new web algorithms proliferate, experimentation platform faces an increasing demand on the velocity of online experiments, which encourages adaptive traffic testing methods to speed up identifying best variant by efficiently allocating traffic. This paper proposed four Bayesian batch bandit algorithms (NB-TS, WB-TS, NB-TTTS, WB-TTTS) for eBay's experimentation platform, using summary batch statistics without incurring new engineering technical debts. The novel WB-TTTS, in particular, demonstrates as an efficient, trustworthy and robust alternative to fixed horizon A/B testing. Another novel contribution is to bring trustworthiness of best arm identification algorithms into evaluation criterion and highlight the existence of severe false positive inflation with equivalent best arms. To gain the trust of experimenters, experimentation platform must consider both efficiency and trustworthiness; However, to the best of authors' knowledge, trustworthiness as an important topic is rarely discussed. This paper shows that Bayesian bandits without neutral posterior reshaping, particularly naive Thompson sampling (NB-TS), are untrustworthy because they can always identify an arm as the best from equivalent best arms. To restore trustworthiness, a novel finding uncovers connections between convergence distribution of posterior optimal probabilities of equivalent best arms and neutral posterior reshaping, which controls false positives. Lastly, this paper presents lessons learned from eBay's experience, as well as thorough evaluations. We hope that this paper is useful to other industrial practitioners and inspires academic researchers interested in the trustworthiness of adaptive traffic experimentation.
Learning Scalable Structural Representations for Link Prediction with Bloom Signatures
Authors: Tianyi Zhang, Haoteng Yin, Rongzhe Wei, Pan Li, Anshumali Shrivastava
Venue: WWW
Year: 2024

Graph neural networks (GNNs) have shown great potential in learning on graphs, but they are known to perform sub-optimally on link prediction tasks. Existing GNNs are primarily designed to learn node-wise representations and usually fail to capture pairwise relations between target nodes, which proves to be crucial for link prediction. Recent works resort to learning more expressive edge-wise representations by enhancing vanilla GNNs with structural features such as labeling tricks and link prediction heuristics, but they suffer from high computational overhead and limited scalability. To tackle this issue, we propose to learn structural link representations by augmenting the message-passing framework of GNNs with Bloom signatures. Bloom signatures are hashing-based compact encodings of node neighborhoods, which can be efficiently merged to recover various types of edge-wise structural features. We further show that any type of neighborhood overlap-based heuristic can be estimated by a neural network that takes Bloom signatures as input. GNNs with Bloom signatures are provably more expressive than vanilla GNNs and also more scalable than existing edge-wise models. Experimental results on five standard link prediction benchmarks show that our proposed model achieves comparable or better performance than existing edge-wise GNN models while being 3-200x faster and more memory-efficient for online inference. Source code is available at https://github.com/tonyzhang617/BloomSigLP.