OpenRaiser/Intern-Atlas huggingface dataset:
Empowering Graph-based Approximate Nearest Neighbor Search with Adaptive Awareness Capabilities
Authors: Jiancheng Ruan, Tingyang Chen, Renchi Yang, Xiangyu Ke, Yunjun Gao
Venue: KDD
Year: 2025

Approximate Nearest Neighbor Search (ANNS) in high-dimensional spaces finds extensive applications in databases, information retrieval, recommender systems, etc. While graph-based methods have emerged as the leading solution for ANNS due to their superior query performance, they still face several challenges, such as struggling with local optima and redundant computations. These issues arise because existing methods (i) fail to fully exploit the topological information underlying the proximity graph G, and (ii) suffer from severe distribution mismatches between the base data and queries in practice. To this end, this paper proposes GATE, high-tier proximity Graph with Adaptive Topology and Query Awareness, as a lightweight and adaptive module atop the graph-based indexes to accelerate ANNS. Specifically, GATE formulates the critical problem to identify an optimal entry point in the proximity graph for a given query, facilitating faster online search. By leveraging the inherent clusterability of high-dimensional data, GATE first extracts a small set of hub nodes V as candidate entry points. Then, resorting to a contrastive learning-based two-tower model, GATE encodes both the structural semantics underlying G and the query-relevant features into the latent representations of these hub nodes V. A navigation graph index on V is further constructed to minimize the model inference overhead. Extensive experiments demonstrate that GATE achieves a 1.2-2.0X speed-up in query performance compared to state-of-the-art graph-based indexes.
Spatial Hierarchical Meta-Learning for Single-Point Map Matching
Authors: Sijie Ruan, Yiqing Zou, Qianyu Yang, Haoyu Han, Yeting Zhang, Ziqiang Yuan, Hanning Yuan, Shuliang Wang
Venue: KDD
Year: 2025

Inferring the actual road segment purely based on one positioning point, known as single-point map matching (SMM), is vital for many urban applications, e.g., ride-hailing and geo-tagging. However, it is challenging due to inherent positioning errors and extrinsic heterogeneous environments. Existing methods either overlook the heterogeneity of different regions, or do not exploit the commonality of different matching tasks. In this paper, we treat each region as an individual SMM task to tackle the heterogeneity, and propose Spatial Hierarchical Meta-Learning for SMM (SHSMM) to learn the shared knowledge across tasks. SHSMM is equipped with a Dual-view Map Matcher to perform the matching, which can perceive the knowledge of road segments globally. To learn the task-specific model parameters, SHSMM modulates initial parameters and scales the local update learning rate based on hierarchical geographical and semantic knowledge about spatial tasks. A local update learning rate scheduling strategy is further proposed to facilitate the meta-training. Extensive experiments as well as case studies based on two real-world datasets demonstrate the effectiveness of the proposed method.
CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation
Authors: Rabeya Tus Sadia, Md. Atik Ahamed, Qiang Cheng
Venue: KDD
Year: 2025

The integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data is crucial for understanding gene expression in spatial context. Existing methods for such integration have limited performance, with structural similarity often below 60%, We attribute this limitation to the failure to consider causal relationships between genes. We present CausalGeD, which combines diffusion and autoregressive processes to leverage these relationships. By generalizing the Causal Attention Transformer from image generation to gene expression data, our model captures regulatory mechanisms without predefined relationships. Across 10 tissue datasets, CausalGeD outperforms state-of-the-art baselines by 5- 32% in key metrics, including Pearson's correlation and structural similarity, advancing both technical and biological insights.
GeoTS: A TSC Framework for Estimating Geological Formation to Model Carbon Storage Reservoirs
Authors: Shwetha Salimath, Francesca Bugiotti, Sylvain Wlodarczyk
Venue: KDD
Year: 2025

In geoscience, it is necessary to study the lithography of the Earth's subsurface, which consists of different stratified layers called geological formations. This study performs well correlation task to model and characterize reservoirs. This operation links the beginning of specific geological formations called tops using measurements from drilled wells. Although data are abundant, the traditional algorithms used for well correlation are semi-automated, requiring significant time and high computational cost. This paper introduces GeoTS, a Python library to apply cutting-edge time series classification models to perform well correlation in a completely automated setting. As input, take the drilling trajectory depth and gamma-ray well logs, which measure the natural radioactivity across the well depth trajectory. The top depths of the formations are predicted as an output. The gamma-ray signatures are extracted around the top depths assigned by geologists. Preprocessing is performed to clean and cluster these signatures using the Dynamic Time Wrapping (DTW) distance and HDBSCAN. Implementation of existing deep learning architectures (FCN, InceptionTime, XceptionTime, XCM, LSTM-FCN) and new architecture (LSTM-2dCNN, LSTM-XCM) are performed. Our experiments demonstrate faster computation with an increase in accuracy. GradCAM has also been implemented for model explainability. Experiments were performed using Colorado oil fields and deployed on Wyoming oil fields. The deployment has provided us with critical insights regarding the improvements needed.
Mind the Gap: Delayed Label Bias-Variance Tradeoffs in Predicting Likelihood of Nonpayment
Authors: Tal Sarig, Ido Guy, Ami Tavory, Udi Weinsberg, Stratis Ioannidis
Venue: KDD
Year: 2025

The purpose of an online electronic-payment risk detection system is to prevent leakage, i.e., the loss of revenue that occurs when users fail to pay for services or when transactions are reversed.Nonpayment prediction models are trained on datasets comprising of features available when the model is triggered and the corresponding nonpayment labels.The latter are typically only observed several weeks or even months later.Furthermore, behavior indicative of future nonpayment is highly non-stationary, and the true model may drift significantly in the gap between trigger events and label collection.To address these challenges, we use post-transaction signals to generate pseudo-labels, i.e., short-term proxies [23] or surrogate-indices [33].Our framework attains a favorable tradeoff between ameliorating bias due to drift and introducing variance due to pseudo-label noise, as demonstrated by both offline and online experiments on several nonpayment-detection systems at Meta.Our deployment on live user traffic yields a statistically significant improvement in revenue, accounting also for leakage.
Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Authors: Shailik Sarkar, Raquib Bin Yousuf, Linhan Wang, Brian Mayer, Thomas Mortier, Victor Deklerck, Jakub Truszkowski, John Simeone, Marigold Norman, Jade Saunders, Chang-Tien Lu, Naren Ramakrishnan
Venue: KDD
Year: 2025

Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products trade, affecting livelihoods and communities across the globe. Stable isotope ratio analysis (SIRA) is rapidly becoming an important tool for determining the harvest location of traded, organic, products. The spatial pattern in stable isotope ratio values depends on factors such as atmospheric and environmental conditions and can thus be used for geographic origin identification. We present here the results of a deployed machine learning pipeline where we leverage both isotope values and atmospheric variables to determine timber harvest location. Additionally, the pipeline incorporates uncertainty estimation to facilitate the interpretation of harvest location determination for analysts. We present our experiments on a collection of oak (Quercus spp.) tree samples from its global range. Our pipeline outperforms comparable state-of-the-art models determining geographic harvest origin of commercially traded wood products, and has been used by European enforcement agencies to identify harvest location misrepresentation. We also identify opportunities for further advancement of our framework and how it can be generalized to help identify the origin of falsely labeled organic products throughout the supply chain.
Diffusion-Scheduled Denoising Autoencoders for Anomaly Detection in Tabular Data
Authors: Timur Sattarov, Marco Schreyer, Damian Borth
Venue: KDD
Year: 2025

Anomaly detection in tabular data remains challenging due to complex feature interactions and the scarcity of anomalous examples. Denoising autoencoders rely on fixed-magnitude noise, limiting adaptability to diverse data distributions. Diffusion models introduce scheduled noise and iterative denoising, but lack explicit reconstruction mappings. We propose the Diffusion-Scheduled Denoising Autoencoder (DDAE), a framework that integrates diffusion-based noise scheduling and contrastive learning into the encoding process to improve anomaly detection. We evaluated DDAE on 57 datasets from ADBench. Our method outperforms in semi-supervised settings and achieves competitive results in unsupervised settings, improving PR-AUC by up to 65% (9%) and ROC-AUC by 16% (6%) over state-of-the-art autoencoder (diffusion) model baselines. We observed that higher noise levels benefit unsupervised training, while lower noise with linear scheduling is optimal in semi-supervised settings. These findings underscore the importance of principled noise strategies in tabular anomaly detection.
ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting
Authors: Hyunwoo Seo, Chiehyeon Lim
Venue: KDD
Year: 2025

Forecasting complex time series is an important yet challenging problem that involves various industrial applications. Recently, masked time-series modeling has been proposed to effectively model temporal dependencies for forecasting by reconstructing masked segments from unmasked ones. However, since the semantic information in time series is involved in intricate temporal variations generated by multiple time series components, simply masking a raw time series ignores the inherent semantic structure, which may cause MTM to learn spurious temporal patterns present in the raw data. To capture distinct temporal semantics, we show that masked modeling techniques should address entangled patterns through a decomposition approach. Specifically, we propose ST-MTM, a masked time-series modeling framework with seasonal-trend decomposition, which includes a novel masking method for the seasonal-trend components that incorporates different temporal variations from each component. ST-MTM uses a period masking strategy for seasonal components to produce multiple masked seasonal series based on inherent multi-periodicity and a sub-series masking strategy for trend components to mask temporal regions that share similar variations. The proposed masking method presents an effective pre-training task for learning intricate temporal variations and dependencies. Additionally, ST-MTM introduces a contrastive learning task to support masked modeling by enhancing contextual consistency among multiple masked seasonal representations. Experimental results show that our proposed ST-MTM achieves consistently superior forecasting performance compared to existing masked modeling, contrastive learning, and supervised forecasting methods.
Towards Web-scale Recommendations with LLMs: From Quality-aware Ranking to Candidate Generation
Authors: Jaidev Shah, Iman Barjasteh, Amey Barapatre, Rana Forsati, Gang Luo, Fan Wu, Yuan Fang, Xue Deng, Blake Shepard, Ronak Shah, Linjun Yang, Hongzhi Li
Venue: KDD
Year: 2025

Explore Further @ Bing is a webpage-to-webpage recommendation product, enhancing the search experience on Bing by surfacing engaging webpage recommendations tied to the search result URLs. In this paper, we present our approach for leveraging Large Language Models (LLMs) for enhancing our web-scale recommendation system. We describe the development and validation of our LLM-powered recommendation quality metric RecoDCG. We discuss our core techniques for utilizing LLMs to make our ranking stage quality-aware. Furthermore, we detail Q' recall, a recall path that enhances our system's candidate generation stage by leveraging LLMs to produce complementary and engaging recommendation candidates. We also address how we optimize our system for multiple objectives, balancing recommendation quality with click metrics. We deploy our work to production, achieving a significant improvement in recommendation quality. We share results from offline and online experiments as well as insights and steps we took to ensure our approaches scale effectively for our web-scale needs.
An Automatic Graph Construction Framework based on Large Language Models for Recommendation
Authors: Rong Shan, Jianghao Lin, Chenxu Zhu, Bo Chen, Menghui Zhu, Kangning Zhang, Jieming Zhu, Ruiming Tang, Yong Yu, Weinan Zhang
Venue: KDD
Year: 2025

Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph construction stage. Earlier works for graph construction usually rely on specific rules or crowdsourcing, which are either too simplistic or too labor-intensive. Recent works start to utilize large language models (LLMs) to automate the graph construction, in view of their abundant open-world knowledge and remarkable reasoning capabilities. Nevertheless, they generally suffer from two limitations: (1) invisibility of global view (e.g., overlooking contextual information) and (2) construction inefficiency. To this end, we introduce AutoGraph, an automatic graph construction framework based on LLMs for recommendation. Specifically, we first use LLMs to infer the user preference and item knowledge, which is encoded as semantic vectors. Next, we employ vector quantization to extract the latent factors from the semantic vectors. The latent factors are then incorporated as extra nodes to link the user/item nodes, resulting in a graph with in-depth global-view semantics. We further design metapath-based message aggregation to effectively aggregate the semantic and collaborative information. The framework is model-agnostic and compatible with different backbone models. Extensive experiments on three real-world datasets demonstrate the efficacy and efficiency of AutoGraph compared to existing baseline methods. We have deployed AutoGraph in Huawei advertising platform, and gain a 2.69% improvement on RPM and a 7.31% improvement on eCPM in the online A/B test. Currently AutoGraph has been used as the main traffic model, serving hundreds of millions of people.
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models
Authors: Zezhi Shao, Yujie Li, Fei Wang, Chengqing Yu, Yisong Fu, Tangwen Qian, Bin Xu, Boyu Diao, Yongjun Xu, Xueqi Cheng
Venue: KDD
Year: 2025

The advent of universal time series forecasting models has revolutionized zero-shot forecasting across diverse domains, yet the critical role of data diversity in training these models remains underexplored. Existing large-scale time series datasets often suffer from inherent biases and imbalanced distributions, leading to suboptimal model performance and generalization. To address this gap, we introduce BLAST, a novel pre-training corpus designed to enhance data diversity through a balanced sampling strategy. First, BLAST incorporates 321 billion observations from publicly available datasets and employs a comprehensive suite of statistical metrics to characterize time series patterns. Then, to facilitate pattern-oriented sampling, the data is implicitly clustered using grid-based partitioning. Furthermore, by integrating grid sampling and grid mixup techniques, BLAST ensures a balanced and representative coverage of diverse patterns. Experimental results demonstrate that models pre-trained on BLAST achieve state-of-the-art performance with a fraction of the computational resources and training tokens required by existing methods. Our findings highlight the pivotal role of data diversity in improving both training efficiency and model performance for the universal forecasting task.
Towards Trajectory Anomaly Detection: a Fine-Grained and Noise-Resilient Framework
Authors: Wei Shao, Ziquan Fang, Lu Chen, Yunjun Gao
Venue: KDD
Year: 2025

Trajectory anomaly detection aims to identify patterns in trajectory data that deviate significantly from normal behavior, such as taxi detours, and plays a crucial role in urban computing. However, real-world trajectories are inherently complex, containing diverse anomalies and unavoidable noise. Existing research mainly focuses on coarse-grained trajectory anomalies, such as detour and switch anomalies, while paying limited attention to fine-grained trajectory anomalies, such as time and loop anomalies. Furthermore, they tend to disregard the impact of inherent noise in trajectories. As a result, there remains a gap in developing robust models with strong generalization capabilities to effectively detect fine-grained trajectory anomalies, even in noisy environments. Inspired by the powerful generalization and noise resistance capabilities of LLMs, we propose an LLM-driven approach, FOTraj, for fine-grained and noise-resilient trajectory anomaly detection. Specifically, FOTraj introduces a Spatio-Temporal Dependency Encoder (STE) that converts raw trajectory data into a spatio-temporal graph, equipped with a tailored adapter for the LLM to capture finer-grained trajectory features. Besides, we design a Patch-Gated Robustification (PGR) module to simulate noisy environments, enhancing the LLM's resilience to trajectory noise. Since LLMs were originally designed for sequence modeling and cannot directly process graph structured data, FOTraj incorporates a specialized graph decoder to map the LLM's output back into a graph structure, utilizing a temperature-controlled top-k sampling mechanism to improve diversity and robustness. Extensive experiments on three datasets show that FOTraj outperforms state-of-the-art methods.
Abductive Learning for Neuro-Symbolic Grounded Imitation
Authors: Jie-Jing Shao, Hao-Ran Hao, Xiao-Wen Yang, Yu-Feng Li
Venue: KDD
Year: 2025

Recent learning-to-imitation methods have shown promise in planning by imitating within the observation-action space, yet they remain constrained in open environments, especially for long-horizon tasks. In contrast, while traditional symbolic planning excels in such tasks through logical reasoning over human-defined symbolic spaces, it struggles with high-dimensional visual inputs encountered in real-world scenarios. In this work, we draw inspiration from abductive learning and introduce a novel framework ABductive Imitation Learning (ABIL) that integrates the benefits of data-driven learning and symbolic-based reasoning, enabling long-horizon planning. Specifically, we employ abductive reasoning to understand the demonstrations in symbolic space and design the principles of sequential consistency to resolve the conflicts between perception and reasoning. ABIL generates predicate candidates to facilitate the perception from raw observations to symbolic space without laborious predicate annotations, providing a groundwork for symbolic planning. With the symbolic understanding, we develop a policy ensemble with base policies designed around different logical objectives, managed through symbolic reasoning. Experiments demonstrate that our method successfully comprehends observations with task-relevant symbolics to aid imitation learning. Importantly, ABIL demonstrates improved data efficiency and generalization across various long-horizon tasks, highlighting it as a promising solution for long-horizon planning. Project website: https://www.lamda.nju.edu.cn/shaojj/KDD25_ABIL/.
GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou
Authors: Ninglu Shao, Jinshan Wang, Chenxu Wang, Qingbiao Li, Xiaoxue Zang
Venue: KDD
Year: 2025

Currently, short video platforms have become the primary place for individuals to share experiences and obtain information. To better meet users' needs for acquiring information while browsing short videos, some apps have introduced a search entry at the bottom of videos, accompanied with recommended relevant queries. This scenario is known as query recommendation in video-related search, where core task is item-to-query (I2Q) recommendation. As this scenario has only emerged in recent years, there is a notable scarcity of academic research and publicly available datasets in this domain. To address this gap, we systematically examine the challenges associated with this scenario for the first time. Subsequently, we release a large-scale dataset derived from real-world data pertaining to the query recommendation in video- related search on the Kuaishou app (KuaiRS). Presently, existing methods rely on embeddings to calculate similarity for matching short videos with queries, lacking deep interaction between the semantic content and the query. In this paper, we introduce a novel LLM-based framework named GREAT, which guides que ry g ener ation with a trie to address I2Q recommendation in related search. Specifically, we initially gather high-quality queries with high exposure and click-through rate to construct a query-based trie. During training, we enhance the LLM's capability to generate high-quality queries using the query-based trie. In the inference phase, the query-based trie serves as a guide for the token generation. Finally, we further refine the relevance and literal quality between items and queries via a post-processing module. Extensive offline and online experiments demonstrate the effectiveness of our proposed method.