OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Domain Web Information Extraction at Pinterest
Authors: Michael Farag, Patrick Halina, Andrey Zaytsev, Alekhya Munagala, Imtihan Ahmed, Junhao Wang
Venue: KDD
Year: 2025

The internet offers a massive repository of unstructured information, but it's a significant challenge to convert this into a structured format. At Pinterest, the ability to accurately extract structured product data from e-commerce websites is essential to enhance user experiences and improve content distribution. In this paper, we present Pinterest's system for attribute extraction, which achieves remarkable accuracy and scalability at a manageable cost. Our approach leverages a novel webpage representation that combines structural, visual, and text modalities into a compact form, optimizing it for small model learning. This representation captures each visible HTML node with its text, style and layout information. We show how this allows simple models such as eXtreme Gradient Boosting (XGBoost) to extract attributes more accurately than much more complex Large Language Models (LLMs) such as Generative Pre-trained Transformer (GPT). Our results demonstrate a system that is highly scalable, processing over 1,000 URLs per second, while being 1000 times more cost-effective than the cheapest GPT alternatives.
CryptoMixer: Fine-grained market information-aware MLP Networks for Individual Cryptocurrency Trading Prediction
Authors: Tingsheng Feng, Zhihao Shen, Xi Zhao, Xiaoni Lu, Yuyang Zhou
Venue: KDD
Year: 2025

Accurately predicting user trading behavior in decentralized exchanges is essential for investors to mitigate risks and optimize their trading strategies. While existing research primarily focuses on predicting trading behavior in stock markets, these methods often struggle to adapt to the distinct nature of cryptocurrency trading. Specifically, they face issues such as limited adaptivity to high-frequency and algorithmic trading, as well as an insufficient consideration of fine-grained real-time market participants' behavior.Thanks to the pending mechanism of blockchain, it becomes possible to capture traders' interactions before transactions are finalized, providing valuable insights into market state. However, accurately modeling and predicting trading behavior in decentralized exchanges presents challenges, including limited adaptability to high-frequency trading, a lack of fine-grained transaction data, and high computational costs. This work proposes CryptoMixer, a lightweight fine-grained market information-aware multilayer perceptron (MLP)-based model for high-frequency cryptocurrency trading behavior prediction. Specifically, to overcome the sparsity and asynchrony of user behavior data, CryptoMixer develops a Market Information Augmenter that aggregates historical transaction data of users. Furthermore, CryptoMixer designs a Market Information Mixer as well as a Two-stream MLP Fusion Mixer to capture fine-grained user trading behavior patterns. We evaluate CryptoMixer on real-world user trading datasets from the Uniswap decentralized finance platform. Experimental results demonstrate that CryptoMixer outperforms traditional prediction models while maintaining low computational overheads, providing a practical solution for real-time cryptocurrency trading behavior prediction. The code is available at https://github.com/aqua111000/CryptoMixer.
Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
Authors: Qing Feng, Samuel Daulton, Benjamin Letham, Maximilian Balandat, Eytan Bakshy
Venue: KDD
Year: 2025

Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and learning adaptive streaming controllers. Decision-makers generally wish to optimize for long-term treatment effects of the system changes, which often requires running experiments for a long time as short-term measurements can be misleading due to non-stationarity in treatment effects over time. The sequential experimentation strategies---which typically involve several iterations---can be prohibitively long in such cases. We describe a novel approach that combines fast experiments (e.g., biased experiments run only for a few hours or days) and/or offline proxies (e.g., off-policy evaluation) with long-running, slow experiments to perform sequential, Bayesian optimization over large action spaces in a short amount of time.
FoRAGe: High-CTR Food Image Synthesis with Retrieval-Augmented Diffusion Model
Authors: Jiaxu Feng, Xinyu Gao, Muqi Huang, Kanjun Xu, Yun Xiong, Kun Zhou, Chuan Li, Feng Shi
Venue: KDD
Year: 2025

High Click-Through Rate (CTR) imagery has proven commercial value for food delivery platforms, driving a need for strategies to generate visually compelling images. Our investigations reveal a positive correlation between appropriate food backgrounds and subsequent user engagement. Despite advancements in diffusion models, inpainting new backgrounds does not guarantee high CTR, and fine-tuning diffusion models for this purpose is prohibitively expensive for the fast-paced online food delivery advertising sector. Consequently, there is a lack of cost-effective, transferable generation frameworks tailored to high-CTR food images. In this paper, we propose FoRAGe, a novel high-CTR Food image Retrieval-Augmented Generation pipeline leveraging ControlNet based on Stable Diffusion. Specifically, we construct a comprehensive food image database encompassing a diverse range of background environments. During image generation, FoRAGe retrieves high-quality background exemplars featuring analogous food subjects from the database and employs the retrieved backgrounds as conditions to guide image synthesis via the ControlNet model. Subsequently, a multimodal CTR prediction model is utilized to identify and select optimal images for deployment. Extensive online experiments demonstrate a significant increase in CTR for images generated by our proposed pipeline, and ablation studies further elucidate the impact of different strategies and configurations. Code is available at https://github.com/jiaxu-feng/FoRAGe.
Complicated Semantic Alignment for Long-Tail Query Rewriting in Taobao Search Based on Large Language Model
Authors: Yunling Feng, Gui Ling, Yue Jiang, Jianfeng Huang, Dan Ou, Qingwen Liu, Fuyu Lv, Yajing Xu
Venue: KDD
Year: 2025

In the realm of e-commerce search, semantic matching has consistently been a core issue, as it directly affects user experience and company revenue. However, users' queries often fail to effectively retrieve relevant products due to discrepancies between the user's expression habits and product names written by merchants. Even existing large language model (LLM) based query rewriting methods can bridge the semantic gap for most queries, they are still ineffective for long-tail queries with complicated semantic. In this paper, we propose Complicated Semantic Alignment Query Rewrite(CSA-QR) framework, which mitigates the semantic differences in long-tail queries with complicated semantics. CSA-QR comprises three stages: high-quality supervised fine-tuning (SFT) dataset generation, multi-dimensional alignment dataset generation, and binary feedback Proximal Policy Optimization (PPO) for reinforcement alignment. Initially, we utilize general large language models to generate rewrite candidates, followed by manual annotation to discriminate the candidates, then use the retrieval augmentation generation (RAG) based on existing annotations to produce a higher quality SFT dataset. Subsequently, we decouple the feedback data into user semantic consistency and merchant expression consistency dimensions to collect multi-dimensional alignment data. Finally, we introduce a binary feedback method to train the reward model, enabling it to better guide alignment training within our context. We also identify a set of more appropriate reward model evaluation metrics to guide our iterations. Offline experiments demonstrate the effectiveness of this method in improving retrieval performance. Online A/B tests reveal that our method significantly boosts critical metrics such as product click-through rate (CTR), gross merchandise volume (GMV) and number of transaction (#Trans) for long-tail complicated queries. CSA-QR has been deployed on Taobao, one of China's most popular online shopping platforms, since September 2024.
Hierarchical Structure Sharing Empowers Multi-task Heterogeneous GNNs for Customer Expansion
Authors: Xinyue Feng, Shuxin Zhong, Jinquan Hang, Wenjun Lyu, Yuequn Zhang, Guang Yang, Haotian Wang, Desheng Zhang, Guang Wang
Venue: KDD
Year: 2025

Customer expansion, i.e., growing a business's existing customer base by acquiring new customers, is critical for scaling operations and sustaining the long-term profitability of logistics companies. Although state-of-the-art works model this task as a single-node classification problem under a heterogeneous graph learning framework and achieve good performance, they struggle with extremely positive label sparsity issues in our scenario. Multi-task learning (MTL) offers a promising solution by introducing a correlated, label-rich task to enhance the label-sparse task prediction through knowledge sharing. However, existing MTL methods result in performance degradation because they fail to discriminate task-shared and task-specific structural patterns across tasks. This issue arises from their limited consideration of the inherently complex structure learning process of heterogeneous graph neural networks, which involves the multi-layer aggregation of multi-type relations. To address the challenge, we propose a Structure-Aware Hierarchical Information Sharing Framework (SrucHIS), which explicitly regulates structural information sharing across tasks in logistics customer expansion. SrucHIS breaks down the structure learning phase into multiple stages and introduces sharing mechanisms at each stage, effectively mitigating the influence of task-specific structural patterns during each stage. We evaluate StrucHIS on both private and public datasets, achieving a 51.41% average precision improvement on the private dataset and a 10.52% macro F1 gain on the public dataset. StrucHIS is further deployed at one of the largest logistics companies in China and demonstrates a 41.67% improvement in the success contract-signing rate over existing strategies, generating over 453K new orders within just two months.
TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery
Authors: Muhammad Hasan Ferdous, Emam Hossain, Md. Osman Gani
Venue: KDD
Year: 2025

Robust causal discovery in time series datasets depends on reliable benchmark datasets with known ground-truth causal relationships. However, such datasets remain scarce, and existing synthetic alternatives often overlook critical temporal properties inherent in real-world data, including nonstationarity driven by trends and seasonality, irregular sampling intervals, and the presence of unobserved confounders. To address these challenges, we introduce TimeGraph, a comprehensive suite of synthetic time-series benchmark datasets that systematically incorporates both linear and nonlinear dependencies while modeling key temporal characteristics such as trends, seasonal effects, and heterogeneous noise patterns. Each dataset is accompanied by a fully specified causal graph featuring varying densities and diverse noise distributions and is provided in two versions: one including unobserved confounders and one without, thereby offering extensive coverage of real-world complexity while preserving methodological neutrality. We further demonstrate the utility of TimeGraph through systematic evaluations of state-of-the-art causal discovery algorithms including PCMCI+, LPCMCI, and FGES across a diverse array of configurations and metrics. Our experiments reveal significant variations in algorithmic performance under realistic temporal conditions, underscoring the need for robust synthetic benchmarks in the fair and transparent assessment of causal discovery methods. The complete TimeGraph suite, including dataset generation scripts, evaluation metrics, and recommended experimental protocols, is freely available to facilitate reproducible research and foster community-driven advancements in time-series causal discovery.
POVE: A Preoptimized Vault of Expressions for Symbolic Regression Research and Benchmarking
Authors: Kei Sen Fong, Mehul Motani
Venue: KDD
Year: 2025

Symbolic Regression (SR) algorithms are powerful machine learning tools that discover mathematical expressions from data, but their evaluation is often hindered by the computational cost of optimizing numerical parameters (also called constants) within candidate expressions. To address this limitation, we introduce POVE (PreOptimized Vault of Expressions), a comprehensive repository of preoptimized symbolic expressions where numerical parameters have been efficiently computed using an established optimization technique in SR (i.e., BFGS) on widely used SR regression problems (i.e., SRBench). In addition to optimized constants, POVE stores the corresponding fitness values, quantifying the accuracy of each expression on its respective regression problem, allowing researchers to directly retrieve both optimized expressions and their precomputed performance metrics from memory (e.g., hash tables or dictionaries). By eliminating the need for computationally expensive numerical optimization steps, POVE enables significantly faster evaluation, large-scale benchmarking, and extensive hyperparameter analysis. It provides a structured and standardized foundation for comparing SR algorithms, assessing their consistency across large numbers of random seeds and investigating the impact of hyperparameters without the overhead of numerical parameter optimization. By offering a collection of precomputed expressions, parameters, and fitness values across diverse regression problems, POVE enhances reproducibility, accelerates experimental workflows, and enables scalable SR research. It serves as a valuable resource for both developing novel SR methodologies and improving existing algorithms by providing a reliable reference for optimal expression discovery. With POVE, SR research can shift away from costly optimization, enabling deeper algorithmic exploration and more comprehensive systematic evaluation. POVE also lowers the barrier to entry for SR research and makes large-scale evaluation significantly more accessible. POVE is publicly available at: https://github.com/kentridgeai/POVE
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection
Authors: Florian Frantzen, Michael T. Schaub
Venue: KDD
Year: 2025

In this paper, we propose HLSAD, a novel method for detecting anomalies in time-evolving simplicial complexes. While traditional graph anomaly detection techniques have been extensively studied, they often fail to capture changes in higher-order interactions that are crucial for identifying complex structural anomalies. These higher-order interactions can arise either directly from the underlying data itself or through graph lifting techniques. Our approach leverages the spectral properties of Hodge Laplacians of simplicial complexes to effectively model multi-way interactions among data points. By incorporating higher-dimensional simplicial structures into our method, our method enhances both detection accuracy and computational efficiency. Through comprehensive experiments on both synthetic and real-world datasets, we demonstrate that our approach outperforms existing graph methods in detecting both events and change points.
Web Scale Graph Mining for Cyber Threat Intelligence
Authors: Scott Freitas, Amir Gharib
Venue: KDD
Year: 2025

Defending against today's increasingly sophisticated and large-scale cyberattacks demands accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate diverse telemetry, and adapt to a constantly evolving security landscape. We introduce Threat Intelligence Tracking via Adaptive Networks (Titan), an industry-scale graph mining framework that generates cyber threat intelligence at unprecedented speed and scale. Titan introduces a suite of innovations specifically designed to address the complexities of the modern security landscape, including: (1) a dynamic threat intelligence graph that maps the intricate relationships between millions of entities, incidents, and organizations; (2) real-time update mechanisms that automatically decay and prune outdated intel; (3) integration of security domain knowledge to bootstrap initial reputation scores; and (4) reputation propagation algorithms that uncover hidden threat actor infrastructure. Integrated into Microsoft Unified Security Operations Platform (USOP), which is deployed across hundreds of thousands of organizations worldwide, Titan's threat intelligence powers key detection and disruption capabilities. With an impressive average macro-F1 score of 0.89 and a precision-recall AUC of 0.94, Titan identifies millions of high-risk entities each week, enabling a 6x increase in non-file threat intelligence. Since its deployment, Titan has increased the product's incident disruption rate by a remarkable 21%, while reducing the time to disrupt by a factor of 1.9x, and maintaining 99% precision, as confirmed by customer feedback and thorough manual evaluation by security experts--ultimately saving customers from costly security breaches.
AI4DE: The 1st International Workshop on AI for Data Editing
Authors: Yanjie Fu, Kunpeng Liu, Dongjie Wang, Xiangliang Zhang, Khalid K. Osman, Charu Aggarwal, Suzanne M. Shontz, Huan Liu, Jian Pei
Venue: KDD
Year: 2025

Machine learning traditionally emphasizes developing models for given datasets, but real-world data is often messy, making model improvement insufficient for enhancing performance. AI for data editing (AI4DE) is an emerging field that systematically improves datasets, leading to significant practical ML advancements. While experienced data scientists have manually refined datasets through trial-and-error and intuition, AI4DE approaches data enhancement as a systematic engineering discipline. AI4DE represents a shift from focusing on models to the underlying data used for training and evaluation. Despite the dominance of common model architectures and predictable scaling rules, building and using datasets remain labor-intensive and costly, lacking infrastructure and best practices. The AI4DE movement aims to develop efficient, high-productivity open data engineering tools for modern ML systems. This workshop seeks to foster an interdisciplinary AI4DE community to address practical data challenges, including data collection, generation, labeling, preprocessing, augmentation, quality evaluation, debt, and governance. By defining and shaping the AI4DE movement, this workshop aims to influence the future of AI and ML, inviting interested parties to contribute through paper submissions
Time Matters: Enhancing Sequential Recommendations with Time-Guided Graph Neural ODEs
Authors: Haoyan Fu, Zhida Qin, Shixiao Yang, Haoyao Zhang, Bin Lu, Shuang Li, Tianyu Huang, John C. S. Lui
Venue: KDD
Year: 2025

Sequential recommendation (SR) is widely deployed in e-commerce platforms, streaming services, etc., revealing significant potential to enhance user experience. The core of SR lies in exploring the sequential relationships in historical user-item interactions. However, existing methods often overlook two critical factors: irregular user interests between interactions and highly uneven item distributions over time. The former factor implies that actual user preferences are not always continuous, and long-term historical interactions may not be relevant to current purchasing behavior. Therefore, relying only on these historical interactions for recommendations may result in a lack of user interest at the target time. The latter factor, characterized by peaks and valleys in interaction frequency, may result from seasonal trends, special events, or promotions. These externally driven distributions may not align with individual user interests, leading to inaccurate recommendations. To address these deficiencies, we propose TGODE to both enhance and capture the long-term historical interactions. Specifically, we first construct the user time graph and item evolution graph, which utilize user personalized preferences and global item distribution information, respectively. To tackle the temporal sparsity caused by irregular user interactions, we design a time-guided diffusion generator to automatically obtain an augmented time-aware user graph. Additionally, we devise a user interest truncation factor to efficiently identify sparse time intervals and achieve balanced preference inference. After that, the augmented user graph and item graph are fed into a generalized graph neural ordinary differential equation (ODE) to align with the evolution of user preferences and item distributions. This allows two patterns of information evolution to be matched over time. Experimental results demonstrate that TGODE outperforms baseline methods across five datasets, with improvements ranging from 10% to 46%. The code is available at https://github.com/Qin-lab-code/TGODE.
VideoConviction: A Multimodal Benchmark for Human Conviction and Stock Market Recommendations
Authors: Michael Galarnyk, Veer Kejriwal, Agam Shah, Yash Bhardwaj, Nicholas Watney Meyer, Anand Krishnan, Sudheer Chava
Venue: KDD
Year: 2025

Social media has amplified the reach of financial influencers known as ''finfluencers,'' who share stock recommendations on platforms like YouTube. Understanding their influence requires analyzing multimodal signals like tone, delivery style, and facial expressions, which extend beyond text-based financial analysis. We introduce VideoConviction, a multimodal dataset with 6,000+ expert annotations, produced through 457 hours of human effort, to benchmark multimodal large language models (MLLMs) and text-based large language models (LLMs) in financial discourse. Our results show that while multimodal inputs improve stock ticker extraction (e.g., extracting Apple's ticker AAPL), both MLLMs and LLMs struggle to distinguish investment actions and conviction-the strength of belief conveyed through confident delivery and detailed reasoning-often misclassifying general commentary as definitive recommendations. While high-conviction recommendations perform better than low-conviction ones, they still underperform the popular S&P 500 index fund. An inverse strategy-betting against finfluencer recommendations-outperforms the S&P 500 by 6.8% in annual returns but carries greater risk (Sharpe ratio of 0.41 vs. 0.65). Our benchmark enables a diverse evaluation of multimodal tasks, comparing model performance on both full video and segmented video inputs. This enables deeper advancements in multimodal financial research. Our code, dataset, and evaluation leaderboard are available under the CC BY-NC 4.0 license.