OpenRaiser/Intern-Atlas huggingface dataset:
DIPS: Optimal Dynamic Index for Poisson πps Sampling
Authors: Jinchao Huang, Sibo Wang
Venue: KDD
Year: 2025

This paper addresses the Poisson πps sampling problem, a topic of significant academic interest in various domains and with practical data mining applications, such as influence maximization. The problem includes a set S of n elements, where each element v is assigned a weight w(v) reflecting its importance. The goal is to generate a random subset X of S, where each element v ∈ S is included in X independently with probability c ⋅ w(v)over ∑v ∈ S w(v), where 0 < c≤ 1 is a constant. The subsets must be independent across different queries. While the Poisson πps sampling problem can be reduced to the well-studied subset sampling problem, updates in Poisson πps sampling, such as adding a new element or removing an element, would cause the probabilities of all n elements to change in the corresponding subset sampling problem, making this approach impractical for dynamic scenarios. To address this, we propose a dynamic index specifically tailored for the Poisson πps sampling problem, supporting optimal expected O (1) query time and O (1) index update time, with an optimal O (n) space cost. Our solution involves recursively partitioning the set by weights and ultimately using table lookup. The core of our solution lies in addressing the challenges posed by weight explosion and correlations between elements. Empirical evaluations demonstrate that our approach achieves significant speedups in update time while maintaining consistently competitive query time compared to the subset-sampling-based methods.
MetaEformer: Unveiling and Leveraging Meta-Patterns for Complex and Dynamic Systems Load Forecasting
Authors: Shaoyuan Huang, Tiancheng Zhang, Zhongtian Zhang, Xiaofei Wang, Lanjun Wang, Xin Wang
Venue: KDD
Year: 2025

Time series forecasting is a critical and practical problem in many real-world applications, especially for industrial scenarios, where load forecasting underpins the intelligent operation of modern systems like clouds, power grids and traffic networks. However, the inherent complexity and dynamics of these systems present significant challenges. Despite advances in methods such as pattern recognition and anti-non-stationarity have led to performance gains, current methods fail to consistently ensure effectiveness across various system scenarios due to the intertwined issues of complex patterns, concept-drift, and few-shot problems. To address these challenges simultaneously, we introduce a novel scheme centered on fundamental waveform, a.k.a., meta-pattern. Specifically, we develop a unique Meta-pattern Pooling mechanism to purify and maintain meta-patterns, capturing the nuanced nature of system loads. Complementing this, the proposed Echo mechanism adaptively leverages the meta-patterns, enabling a flexible and precise pattern reconstruction. Our Meta-pattern Echo transformer (MetaEformer) seamlessly incorporates these mechanisms with the transformer-based predictor, offering end-to-end efficiency and interpretability of core processes. Demonstrating superior performance across eight benchmarks under three system scenarios, MetaEformer marks a significant advantage in accuracy, with a 37% relative improvement on fifteen state-of-the-art baselines.
On the Hyperparameter Loss Landscapes of Machine Learning Models: An Exploratory Study
Authors: Mingyu Huang, Ke Li
Venue: KDD
Year: 2025

Previous efforts on hyperparameter optimization (HPO) of machine learning (ML) models predominately focus on algorithmic advances, yet little is known about the topography of the underlying hyperparameter (HP) loss landscape, which plays a fundamental role in governing the search process of HPO. While several works have conducted fitness landscape analysis (FLA) on various ML systems, they are limited to properties of isolated landscape without interrogating the potential structural similarities among landscapes induced on different scenarios. The exploration of such similarities can provide a novel perspective for understanding the mechanism behind modern HPO methods, but has been missing. In this paper, we mapped 1,500 HP loss landscapes of 6 representative ML models on 63 datasets across different fidelity levels, with 11M+ configurations. By conducting exploratory analysis on these landscapes with fine-grained visualizations and dedicated FLA metrics, we observed a similar landscape topography across a wide range of models, datasets, and fidelities, and shed light on the mechanism behind the success of several popular methods in HPO. The artifacts associated with this paper is available at https://github.com/COLA-Laboratory/GraphFLA.
FoodPuzzle: Toward Developing Large Language Model Agents as Autonomous Flavor Scientists
Authors: Tenghao Huang, Dong Hee Lee, John Sweeney, Jiatong Shi, Emily Steliotes, Matthew Lange, Jonathan May, Muhao Chen
Venue: KDD
Year: 2025

Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typically rely on iterative, subjective testing, which lacks the efficiency and scalability required for modern demands. This paper presents three contributions to address these challenges. Firstly, we define a new problem domain for scientific agents in flavor science, conceptualized as the generation of hypotheses for flavor profile sourcing and understanding. By leveraging their capacity to identify relevant evidence and reason within large context spaces, language model-backed agents can perform the labor-intensive tasks of flavor sourcing and understanding with enhanced efficiency and precision. To facilitate research in this area, we introduce the FoodPuzzle dataset, a challenging benchmark consisting of 978 food items and 1,766 flavor molecule profiles. We propose a novel Scientific Agent approach, integrating in-context learning and retrieval augmented techniques to generate grounded hypotheses in the domain of food science. Experimental results indicate that our model significantly surpasses traditional methods in flavor profile prediction tasks, demonstrating its potential to transform flavor development practices.
A Context based Personalized Deep Network for Nearby Flight Recommendation
Authors: Maolei Huang, Detao Lv, Yao Yu, Shuhan Song, Dong Li, Zhuoran Zhuang
Venue: KDD
Year: 2025

With the flourishing development of aviation and the convenience of booking flights online, nearby flight recommendation has become the core business of Online Travel Platforms (OTPs). Nearby flight addresses the issue of inadequate flight options for travelers by offering more cost-effective alternatives, such as recommending flights from nearby cities or on nearby departure dates. Currently, mainstream OTPs adopt rule-based or simple user preference-based strategies to recommend nearby flights. However, the insufficient emphasis on the user's historical behaviors and the ignorance of nearby flight's context make these existing strategies less effective in solving the nearby flight recommendation. To this end, a Context-based Personalized Deep Net work (CPNet) is proposed in this paper for nearby flight recommendation. In CPNet, a Personalized Preferences Learning (PPL) component is first proposed to encapsulate users' individual preferences, leveraging crucial feature correlations between historical behaviors and target nearby flight. Then, a Historical Cost Learning (HCL) component is designed to learn the price sensitivity of users under the same query and the same nearby flight recommendation. Finally, we present a Context Potential Gain Learning (CPGL) component, where the important cost between target nearby flight and context flights are emphasized and learned. Offline experiments on a production dataset and a world-scale online A/B test at Fliggy. Fliggy: https://www.fliggy.com/ both demonstrate the superiority of the proposed CPNet over baselines. CPNet is now successfully deployed at Fliggy, one of the largest OTPs in China, serving millions of users every day for flight reservations.
Progressive Dependency Representation Learning for Stock Ranking in Uncertain Risk Contrasting
Authors: Li Huang, Yanzhe Xie, Qiang Gao, Kunpeng Zhang, Guisong Liu, Xueqin Chen
Venue: KDD
Year: 2025

The practice of ranking a list of stocks to facilitate investment decisions has garnered a lot of attention in the fintech field, aiming at minimizing investment risk while maximizing profitable returns. With recent developments in deep representation learning such as temporal/relational dependency, prior efforts either strive to explore the temporal dynamics behind distinct stocks or expect to expose the collaborative signals from predefined relations, resulting in promising achievements in stock ranking. However, owing to the profound or intricate fluctuations of stock markets, existing insights rarely consider the uncertain risks underlying the learning of dependency representation, which could bring a narrow perspective on how to perceive market laws and ultimately yield an unprofitable decision-making procedure. In this study, we introduce a novel Progressive Dependency representation learning solution with Uncertain risk contrasting (PDU), primarily seeking to progressively uncover multiple dependency dynamics from historical trading signals for stock ranking in addition to addressing the uncertain risks. Specifically, we devise a Progressive Dependency learning block (or PD) in PDU that can progressively capture the temporal and relational dependencies besides multi-term dependencies in the latent space, allowing a coupled exposure of diffusion impacts over historical trading. Furthermore, we introduce an uncertain risk contrasting mechanism in PDU by placing the PD block in a contrastive environment (i.e., certainty vs. uncertainty), aiming to stably enhance dependency learning in the latent space. The experimental results conducted on four real-world stock market datasets demonstrate the superiority of PDU over several baselines.
Path Complex Neural Networks for Sequential Process Activities Classification
Authors: Liang Huang, Kelin Xia, Chuan-Shen Hu
Venue: KDD
Year: 2025

Process mining aims to uncover, track, and enhance real-world workflows by deriving insights from event logs commonly found in modern information systems. With the growing focus on improving productivity within complex business operations, recent research has looked into developing process models to improve business performance metrics. As such, this study aims to enhance process mining from event logs by proposing a novel path-complex construction based on process mining sequential data and a path-complex-based message-passing mechanism for higher-order structural information. We adopt path-complex representations for event logs and their temporal connections developed from instance graphs. Representations are identified and optimised for 0-paths (events), 1-paths (two events in chronological order) and 2-paths (three consecutive events) to characterise intrinsic higher-order information among events. The proposed framework, Path Complex Neural Networks (PCNN), leverages the advantages of topological deep learning and obtains representations for higher-order complexes inductively. Additionally, we evaluated the results with four real-world benchmark datasets and found that PCNN outperforms existing models in analysing sequential and complex process data.
DCA-Bench: A Benchmark for Dataset Curation Agents
Authors: Benhao Huang, Yingzhuo Yu, Jin Huang, Xingjian Zhang, Jiaqi W. Ma
Venue: KDD
Year: 2025

The quality of datasets plays an increasingly crucial role in the research and development of modern artificial intelligence (AI). Despite the proliferation of open dataset platforms nowadays, data quality issues, such as incomplete documentation, inaccurate labels, ethical concerns, and outdated information, remain common in widely used datasets. Furthermore, these issues are often subtle and difficult to be detected by rule-based scripts, therefore requiring identification and verification by dataset users or maintainers-a process that is both time-consuming and prone to human mistakes. With the surging ability of large language models (LLM), it's promising to streamline the discovery of hidden dataset issues with LLM agents. To achieve this, one significant challenge is enabling LLM agents to detect issues in the wild rather than simply fixing known ones. In this work, we establish a benchmark to measure LLM agent's ability to tackle this challenge. We carefully curate 221 real-world test cases from eight popular dataset platforms and propose an automatic evaluation framework using GPT-4o. Our proposed framework shows strong empirical alignment with expert evaluations, validated through extensive comparisons with human annotations. Without any hints, most competitive Curator agent can only reveal ~30% of the data quality issues in the proposed dataset, highlighting the complexity of this task and indicating that applying LLM agents to real-world dataset curation still requires further in-depth exploration and innovation. The data and code is available at https://github.com/TRAIS-Lab/dca-bench.
Theory Instead of Experiment (TIE): A Creator Valuation System at Tencent
Authors: Lei Huang, Juanjuan Zhang
Venue: KDD
Year: 2025

Experiments are informative but should be used judiciously as a costly resource. Well-constructed theory may serve as a substitute. We develop a ''Theory Instead of Experiment'' (TIE) framework and, in collaboration with Tencent, apply the framework to assess how much value (e.g., user clicks) each creator contributes to its WeChat Official Accounts Platform. This TIE application models content demand and supply upon the counterfactual departure of a creator. The demand model predicts user clicks based on estimated user preferences, while the supply model captures the platform's content distribution response. Together, they predict how each creator influences user engagement through the platform's content distribution strategy. We test the predictions of the TIE system with 168 experiments, each examining a different mix of creators and involving more than 9 million unique users. The TIE system and the experiments demonstrate a 97% correlation on the key performance metric (change in user clicks). Based on its low costs, high accuracy, granular output, and minimal latency, Tencent has deployed the TIE system as the default approach to creator valuation, assessing tens of millions of creators each day while avoiding a 2.5% user click loss associated with a typical experiment.
Temporal Graph Learning Workshop
Authors: Shenyang Huang, Daniele Zambon, Andrea Cini, Farimah Poursafaei, Jacob Chmura, Julia Gastinger, Reihaneh Rabbany, Michael M. Bronstein
Venue: KDD
Year: 2025

The Temporal Graph Learning (TGL) workshop, now in its third edition at KDD 2025, offers an interdisciplinary platform for researchers to explore the evolving applications of temporal networks in various domains, including recommender systems, social network analysis, traffic analytics, and epidemiological data analysis. The workshop aims to facilitate the exchange of ideas across disciplines, highlight successes and challenges in TGL, and outline future research directions. The workshop welcomes diverse contributions, offers keynote talks from academic and industry experts, and is complemented by a panel discussion on emerging aspects of TGL.
Partial Pre-Post Code Tree: A Memory-Efficient Tree Structure for Conjunctive Rule Mining
Authors: Van Quoc Phuong Huynh, Florian Beck, Johannes Fürnkranz
Venue: KDD
Year: 2025

State-of-the-art rule mining algorithms rely on summarizing the training set into efficient data structures which allow to quickly answer arbitrary conjunctive queries about the data. The key limitation of such techniques is their memory consumption. Pre-post code trees (PPC-trees) which are the basis of several efficient association and classification rule mining algorithms, are only constructed as an intermediate representation and subsequently converted into a much more efficient N-lists structure. In this paper, we introduce partial pre-post code trees (P3C-trees), which are based on the idea that partial trees are iteratively constructed, and immediately converted into N-lists. This tight integration of these phases allows to avoid the memory bottleneck of a full PPC-tree construction, and thus enables these algorithms to tackle the memory scalability problem posed by large-scale datasets. Our experiments with big datasets confirm that the memory used by P3C-tree is orders of magnitude smaller than the memory consumed by PPC-tree, and the generated N-lists are also more effective than alternative structures such as Tidset or Diffset. Moreover, the N-list construction can also be considerably sped up with the P3C-tree structure.
Generalized Discords for Time Series Anomaly Detection with Flexible Subsequence Lengths
Authors: Makoto Imamura
Venue: KDD
Year: 2025

Time series anomaly detection (TSAD) is a central topic in data mining, with many algorithms proposed, including deep learning and LLM-based methods. However, these are often evaluated on flawed benchmarks with trivial anomalies, unrealistic densities, mislabeled ground truth, and run-to-failure biases. Recent studies using higher-quality datasets show that simpler distance-based methods, such as discords, can outperform deep learning; However, they typically rely on sliding windows with fixed lengths, limiting flexibility. We introduce GDFlex (Generalized Discords with Flexible Subsequence Length), a discord-based method that detects anomalies across multiple time scales using length-normalized Euclidean distance. It also incorporates bias-correction mechanisms to address limitations in Euclidean distance and z-normalization. GDFlex achieves notable accuracy gains over existing methods while remaining scalable, interpretable, and efficient.
Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation
Authors: Hyunsik Jeon, Satoshi Koide, Yu Wang, Zhankui He, Julian J. McAuley
Venue: KDD
Year: 2025

Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains, visually driven categories such as fashion or home decor potentially require detailed visual information related to color, style, or design. To address this challenge, we propose LaViC (Large Vision-Language Conversational Recommendation Framework), a novel approach that integrates compact image representations into dialogue-based recommendation systems. LaViC leverages a large vision-language model in a two-stage process: (1) visual knowledge self-distillation, which condenses product images from thousands of tokens into a small set of visual tokens in a self-distillation manner, significantly reducing computational overhead, and (2) recommendation fine-tuning, which enables the model to incorporate both dialogue context and distilled visual tokens, providing a unified mechanism for capturing textual and visual features. To support rigorous evaluation of visually-aware conversational recommendation, we construct a new dataset by aligning Reddit conversations with Amazon product listings across multiple visually oriented categories (e.g., fashion, beauty, and home). This dataset covers realistic user queries and product appearances in domains where visual details are crucial. Extensive experiments demonstrate that LaViC significantly outperforms text-only conversational recommendation methods and open-source vision-language baselines. Moreover, LaViC achieves competitive or superior accuracy compared to prominent proprietary baselines (e.g., GPT-3.5-turbo, GPT-4o-mini, and GPT-4o), demonstrating the necessity of explicitly using visual data for capturing product attributes and showing the effectiveness of our vision-language integration. Our code and dataset are available at https://github.com/jeon185/LaViC.
Bid2X: Revealing Dynamics of Bidding Environment in Online Advertising from A Foundation Model Lens
Authors: Jiahao Ji, Tianyu Wang, Yeshu Li, Yusen Huo, Zhilin Zhang, Chuan Yu, Jian Xu, Bo Zheng
Venue: KDD
Year: 2025

Auto-bidding is crucial in facilitating online advertising by automatically providing bids for advertisers. While previous work has made great efforts to model bidding environments for better ad performance, it has limitations in generalizability across environments since these models are typically tailored for specific bidding scenarios. To this end, we approach the scenario-independent principles through a unified function that estimates the achieved effect under specific bids, such as budget consumption, gross merchandise volume (GMV), page views, etc. Then, we propose a bidding foundation model Bid2X to learn this fundamental function from data in various scenarios. Our Bid2X is built over uniform series embeddings that encode heterogeneous data through tailored embedding methods. To capture complex inter-variable and dynamic temporal dependencies in bidding data, we propose two attention mechanisms separately treating embeddings of different variables and embeddings at different times as attention tokens for representation learning. On top of the learned variable and temporal representations, a variable-aware fusion module is used to perform adaptive bidding outcome prediction. To model the unique bidding data distribution, we devise a zero-inflated projection module to incorporate the estimated non-zero probability into its value prediction, which makes up a joint optimization objective containing classification and regression. The objective is proven to converge to the zero-inflated distribution. Our model has been deployed on the ad platform in Taobao, one of the world's largest e-commerce platforms. Offline evaluation on eight large-scale real-world datasets exhibits Bid2X's superiority compared to various baselines and its generality across different scenarios. In real-world applications, Bid2X increased GMV by 4.65% and ROI by 2.44% in online A/B tests, paving the way for the bidding foundation model in computational advertising.
Prompt as a Double-Edged Sword: A Dynamic Equilibrium Gradient-Assigned Attack against Graph Prompt Learning
Authors: Ju Jia, Jingxuan Yu, Di Wu, Cong Wu, Hengjie Zhu, Lina Wang
Venue: KDD
Year: 2025

Graph prompt learning (GPL) is designed to bridge the gap between graph pretraining models and downstream graph tasks, providing advantages in terms of graph knowledge transfer. However, GPL is vulnerable to poisoned graph attacks that induce abnormal training via adversarial malicious perturbations. We observe that the prevalent meta-gradient attacks, which heavily rely on the training of surrogate graph neural networks (GNNs), fail to account for the impact of perturbations on GPL where the pretrained GNN remains frozen and graph prompt tokens are tuned. Moreover, their gradient-assigned strategies tend to corrupt the topological semantics on a few influential labeled graphs, which in turn diminishes the trustworthiness of the surrogate training. To address this issue, we propose a dynamic equilibrium gradient-assigned attack against GPL, named MetaGpro. To guarantee the transferability of MetaGpro, the surrogate GPL is utilized in our simulation across various downstream tasks. To dynamically equilibrate the relationships between the reliability of surrogate models and instable structures, the over-robust contrastive learning is integrated into the surrogate training. In this way, the gradient bias caused by excessive perturbations of labeled nodes can be effectively mitigated. Subsequently, the topology perturbation generation is exploited to assign more gradient weights to nodes that are closer to the misclassification area. The experimental results reveal that the surrogate GPL outperforms the surrogate GNN in 96% of downstream evaluations, and our MetaGpro reduces the accuracy of GPL by 2%∼20% compared to the state-of-the-art (SOTA) works mostly. The code for our MetaGpro is available here.