OpenRaiser/Intern-Atlas huggingface dataset:
Using Instruction-Tuned LMs for Scalable Use Case-Based Shopping - Where Customers Meet Their Needs
Authors: Rajdeep Mukherjee, Sonali Singh, Sachin Farfade
Venue: KDD
Year: 2025

Products on e-commerce platforms are usually organized based on seller-provided product attributes. Customers looking for a product typically have certain needs or use cases in mind, such as headphones for gym classes, or a printer for school projects. However, they often struggle to map these use cases to product attributes, thereby failing to find the product they need. To help customers shop online confidently, we propose a Use case-Based Shopping (UBS) system that facilitates customer experiences based on use cases (Fig. 1). UBS consists of three steps: 1) use case phrase extraction from product reviews, 2) clustering the extracted use case phrases to identify the dominant ones, and 3) mapping products in a given category to one or more of these dominant use cases. In this work, we utilize instruction-tuned LMs to primarily focus on the first two steps. However, the way we design them also helps us to seamlessly solve the third step to complete the design of our proposed system. First, we define the novel task of joint Use Uase, Uentiment Uxtraction (UCSE) from product reviews which can be used for both steps 1 and 3. We harness the task adaptation capability of instruction-tuned FLAN-T5 models and gradually improve their zero-shot UCSE performance through instruction tuning, multi-task training, and few-shot iterative re-training for new categories, achieving around ~90% reduction in annotation bandwidth. We then employ Anthropic's Claude 2 LLM to propose an unsupervised approach for hierarchical use case phrase clustering that demonstrates better clustering and cluster naming capabilities when compared to K-Means and LDA. In an online experiment targeting the top 7 product categories, UBS recommendations on search, browse, and product pages resulted in a revenue lift of 0.77%, 0.94%, and 0.44% respectively, and an average click rate lift of 0.15%.
Flexible Generation of Preference Data for Recommendation Analysis
Authors: Simone Mungari, Erica Coppolillo, Ettore Ritacco, Giuseppe Manco
Venue: KDD
Year: 2025

Simulating a recommendation system in a controlled environment, to identify specific behaviors and user preferences, requires highly flexible synthetic data generation models capable of mimicking the patterns and trends of real datasets. In this context, we propose HYDRA, a novel preferences data generation model driven by three main factors: user-item interaction level, item popularity, and user engagement level. The key innovations of the proposed process include the ability to generate user communities characterized by similar item adoptions, reflecting real-world social influences and trends. Additionally, HYDRA considers item popularity and user engagement as mixtures of different probability distributions, allowing for a more realistic simulation of diverse scenarios. This approach enhances the model's capacity to simulate a wide range of real-world cases, capturing the complexity and variability found in actual user behavior. We demonstrate the effectiveness of HYDRA through extensive experiments on well-known benchmark datasets. The results highlight its capability to replicate real-world data patterns, offering valuable insights for developing and testing recommendation systems in a controlled and realistic manner. The code used to perform the experiments is publicly available: https://github.com/flexibledatageneration/HYDRA.
Data Glitches Discovery using Influence-based Model Explanations
Authors: Nikolaos Myrtakis, Ioannis Tsamardinos, Vassilis Christophides
Venue: KDD
Year: 2025

We address the problem of detecting data glitches in ML training sets, specifically mislabeled and anomalous samples. Detection of data glitches provides insights into the quality of the data sampling. Their repair may improve the reliability and the performance of the model. The proposed methodology is based on exploiting influence functions that estimate how much the loss of the model (or a given sample) is affected when a sample is removed from the training set. We introduce three novel signals for detecting, characterizing, and repairing data glitches in a training set based on sample influences. Influence-based signals form an explainable-by-design data glitch detection framework, producing intuitively explainable signals of the actual predictive model built. In contrast, specialized algorithms that are agnostic to the target ML model (e.g., anomaly detectors) replicate the work of fitting the data distribution and may detect glitches that are inconsistent with the decision boundary of the predictive model. Computational experiments on tabular and image data modalities demonstrate that the proposed signals outperform, in some cases up to a factor of 6, all existing influence-based signals, and generalize across different datasets and ML models. In addition, they often outperform specialized glitch detectors (e.g., mislabeled and anomaly detectors) and provide accurate label repairs for mislabeled samples.
Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics
Authors: Gyoung S. Na, Chanyoung Park
Venue: KDD
Year: 2025

Various representation learning methods for molecular structures have been devised to accelerate data-driven chemistry. However, the representation capabilities of existing methods are essentially limited to atom-level information, which is not sufficient to describe real-world molecular physics. Although electron-level information can provide fundamental knowledge about chemical compounds beyond the atom-level information, obtaining the electron-level information in real-world molecules is computationally impractical and sometimes infeasible. We propose a method for learning electron-informed molecular representations without additional computation costs by transferring readily accessible electron-level information about small molecules to large molecules of our interest. The proposed method achieved state-of-the-art prediction accuracy on extensive benchmark datasets containing experimentally observed molecular physics. The source code for HEDMoL is available at https://github.com/ngs00/HEDMoL.
Mitigating Source Label Dependency in Time-Series Domain Adaptation under Label Shifts
Authors: Jihye Na, Youngeun Nam, Junhyeok Kang, Jae-Gil Lee
Venue: KDD
Year: 2025

Time-series unsupervised domain adaptation (TS-UDA) is essential in fields such as healthcare and manufacturing, where data often consists of distinct entities, such as individual patients or machinery. This heterogeneity leads to discrepancies not only in feature distributions but also in label distributions, posing a significant challenge for domain adaptation. However, prior studies have mostly focused on alleviating covariate shifts, resulting in predicted target labels that are often biased toward the source domain's label distribution. To address this issue, we propose Time-series domain Adaptation for mitigating Label Shifts (TA4LS), a novel label refinement approach. TA4LS leverages the consistency between predicted labels and clustering information obtained from the unique characteristics that differentiate each label in the target domain. Furthermore, our approach as a plug-in module achieves performance improvements across diverse existing unsupervised domain adaptation methods, particularly in scenarios with significant discrepancies between source and target label distributions. In experiments on four benchmark datasets with label shifts, TA4LS demonstrates superior performance across six unsupervised domain adaptation methods and six label shift handling modules.
8th Workshop on Machine Learning in Finance
Authors: Saurabh Nagrecha, Isha Chaturvedi, Senthil Kumar, Nitesh V. Chawla, Mahashweta Das, Daksha Yadav, José A. Rodríguez-Serrano, Eren Kurshan
Venue: KDD
Year: 2025

The financial industry leverages machine learning in more ways than just finding the right alpha signal. It grapples with supply chains, business processes, marketing, churn, fraud, and money laundering, all while maintaining compliance with the various regulatory frameworks it is beholden to. Due to the sheer volume of wealth being handled by the financial industry and its critical role in everyday life, it has been a lucrative target for a wide spectrum of ever-evolving bad actors. With each successive iteration of this workshop, we have attempted to capture the breadth of these actors - fraudsters, money launderers, market manipulators, and potentially nation-state-level risks. The emerging advances in Generative AI make this a particularly exciting time to host this workshop. GenAI offers groundbreaking approaches to handling the various data types prevalent in the financial sector. From a security point of view, bad actors are actively using Generative AI creatively to thwart conventional defenses (e.g. voice cloning, better synthetic identities), and this workshop's audience would benefit from commonly applicable defenses & best practices against such threats. Last but not the least, there is now an increasing willingness from the financial industry towards deeper engagement and data sharing with academia.
Generative Information Retrieval
Authors: Marc Najork
Venue: KDD
Year: 2025

Historically, information retrieval systems have all followed the same paradigm: information seekers frame their needs in the form of a short query, the system selects a small set of relevant results from a corpus of available documents, rank-orders the results by decreasing relevance, possibly excerpts a responsive passage for each result, and returns a list of references and excerpts to the user. Retrieval systems typically did not attempt fusing information from multiple documents into an answer and displaying that answer directly. This was largely due to available technology: at the core of each retrieval system is an index that maps lexical tokens or semantic embeddings to document identifiers. Indices are designed for retrieving responsive documents; they do not support integrating these documents into a holistic answer. More recently, the coming-of-age of deep neural networks has dramatically improved the capabilities of large language models (LLMs). Trained on a large corpus of documents, these models not only memorize the vocabulary, morphology and syntax of human languages, but have shown to be able to memorize facts and relations [3]. Generative language models, when provided with a prompt, will extend the prompt with likely completions - an ability that can be used to extract answers to questions from the model. Four years ago, Metzler et al. argued that this ability of LLMs will allow us to rethink the search paradigm: to answer information needs directly rather that directing users to responsive primary sources [1]. Their vision was not without controversy; the following year Shaw and Bender argued that such a system is neither feasible nor desirable [4]. Nonetheless, the past four years have seen the emergence of such systems, with offerings from established search engines and many new entrants to the industry. This keynote (an updated version of [2]) will summarize the history of these generative information retrieval systems, and focus on the many open challenges: ensuring that answers are grounded, attributing answer passages to a primary source, providing nuanced answers to non-factoid-seeking questions, avoiding bias, and going beyond simple regurgitation of memorized facts. It will also touch on the changing nature of the content ecosystem. LLMs are starting to be used to generate web content. Should search engines treat such derived content equal to human-authored content? Is it possible to distinguish generated from original content? And how should we view hybrid authorship where humans contribute ideas and LLMs shape these ideas into prose?
Bi-Modal Learning for Networked Time Series
Authors: Youngeun Nam, Jihye Na, Susik Yoon, Hwanjun Song, Jae-Gil Lee, Byung Suk Lee
Venue: KDD
Year: 2025

Understanding human mobility patterns is a complex challenge that requires modeling both node-oriented time series (e.g., population) and edge-oriented time series (e.g., population flows) within graph topologies across time. While previous methods have focused on either node-oriented time series or interactions, the synergistic integration of these two modalities has proven difficult to achieve. In this paper, we propose BINTS (BI-modal learning for Networked Time Series), a novel bi-modal learning framework that employs soft contrastive learning along the temporal axis. BINTS captures modality similarities and temporal patterns by simultaneously learning from evolving node-oriented time series and interactions, solving the limitations of single-modality approaches. To evaluate our method, we curate comprehensive multi-modal human mobility datasets spanning diverse locations and times. Our experimental results demonstrate that BINTS significantly outperforms existing forecasting models by capturing synergies across different data modalities. Overall, we establish BINTS as a powerful technique for holistically understanding and forecasting complex mobility dynamics. For reproducibility, the source code of our framework is available at https://github.com/kaist-dmlab/BINTS.
A Hands-on Tutorial on Time Series Imputation with ImputeGAP
Authors: Quentin Nater, Mourad Khayati, Philippe Cudré-Mauroux
Venue: KDD
Year: 2025

Although missing gaps are common in time series data, most existing imputation libraries have a narrow focus. They typically rely on a limited set of techniques and make overly simplistic assumptions about the nature of missing data. Consequently, they fail to model the true intricate complexity of real-world time series. To overcome these challenges, we developed ImputeGAP, a versatile and comprehensive library for time series imputation. ImputeGAP supports a wide range of imputation algorithms and modular missing data simulation, catering to datasets with varying characteristics. It also streamlines imputation analysis with features such as automated hyperparameter tuning, benchmarking, explainability, and downstream evaluation. In this tutorial, we will provide an engaging hands-on tutorial where you will learn time series imputation using the powerful Python library, ImputeGAP. The session is divided into two parts. In the first part, we will dive into building an end-to-end imputation workflow with the library, where you will explore real-world missingness patterns simulation, leverage automated tuning for optimal imputation, and benchmark imputation techniques-all with extensive customization options. In the second part, we will unlock advanced functionalities, including assessing the impact of imputation on downstream analytics and understanding how time series features influence imputation outcomes. Whether you are a researcher or practitioner, this tutorial will provide you with the expertise to handle missing data in time series. The ImputeGAP library is accessible at: https://imputegap.readthedocs.io.
Interpretable Prediction and Feature Selection for Survival Analysis
Authors: Mike Van Ness, Madeleine Udell
Venue: KDD
Year: 2025

Survival analysis is widely used as a technique to model time-to-event data when some data is censored, particularly in healthcare for predicting future patient risk. In such settings, survival models must be both accurate and interpretable so that users (such as doctors) can trust the model and understand model predictions. While most literature focuses on discrimination, interpretability is equally as important. A successful interpretable model should be able to describe how changing each feature impacts the outcome, and should only use a small number of features. In this paper, we present DyS (pronounced "dice''), a new survival analysis model that achieves both strong discrimination and interpretability. DyS is a feature-sparse Generalized Additive Model, combining feature selection and interpretable prediction into one model. While DyS works well for all survival analysis problems, it is particularly useful for large (in n and p) survival datasets such as those commonly found in observational healthcare studies. Empirical studies show that DyS competes with other state-of-the-art machine learning models for survival analysis, while being highly interpretable.
Understanding Team Collapse via Probabilistic Graphical Models
Authors: Iasonas Nikolaou, Konstantinos Pelechrinis, Evimaria Terzi
Venue: KDD
Year: 2025

In this work, we develop a graphical model to capture team dynamics. We analyze the model and show how to learn its parameters from data. Using our model we study the phenomenon of team collapse from a computational perspective. We use simulations and real-world experiments to find the main causes of team collapse. We also provide the principles of building resilient teams, i.e., teams that avoid collapsing. Finally, we use our model to analyze the structure of NBA teams and dive deeper into games of interest.
DiMA: An LLM-Powered Ride-Hailing Assistant at DiDi
Authors: Yansong Ning, Shuowei Cai, Wei Li, Jun Fang, Naiqiang Tan, Hua Chai, Hao Liu
Venue: KDD
Year: 2025

On-demand ride-hailing services like DiDi, Uber, and Lyft have transformed urban transportation, offering unmatched convenience and flexibility. In this paper, we introduce DiMA, an LLM-powered ride-hailing assistant deployed in DiDi Chuxing. Its goal is to provide seamless ride-hailing services and beyond through a natural and efficient conversational interface under dynamic and complex spatiotemporal urban contexts. To achieve this, we propose a spatiotemporal-aware order planning module that leverages external tools for precise spatiotemporal reasoning and progressive order planning. Additionally, we develop a cost-effective dialogue system that integrates multi-type dialog repliers with cost-aware LLM configurations to handle diverse conversation goals and trade-off response quality and latency. Furthermore, we introduce a continual fine-tuning scheme that utilizes real-world interactions and simulated dialogues to align the assistant's behavior with human prefered decision-making processes. Since its deployment in the DiDi application, DiMA has demonstrated exceptional performance, achieving 93% accuracy in order planning and 92% in response generation during real-world interactions. Offline experiments further validate DiMA's capabilities, showing improvements of up to 70.23% in order planning and 321.27% in response generation compared to three state-of-the-art agent frameworks, while reducing latency by 0.72x to 5.47x. These results establish DiMA as an effective, efficient, and intelligent mobile assistant for ride-hailing services.
Utilizing Strategic Pre-training to Reduce Overfitting: Baguan - A Pre-trained Weather Forecasting Model
Authors: Peisong Niu, Ziqing Ma, Tian Zhou, Weiqi Chen, Lefei Shen, Rong Jin, Liang Sun
Venue: KDD
Year: 2025

Weather forecasting has long posed a significant challenge for humanity. While recent AI-based models have surpassed traditional numerical weather prediction (NWP) methods in global forecasting tasks, overfitting remains a critical issue due to the limited availability of real-world weather data spanning only a few decades. Unlike fields like computer vision or natural language processing, where data abundance can mitigate overfitting, weather forecasting demands innovative strategies to address this challenge with existing data. In this paper, we explore pre-training methods for weather forecasting, finding that selecting an appropriately challenging pre-training task introduces locality bias, effectively mitigating overfitting and enhancing performance. We introduce Baguan, a novel data-driven model for medium-range weather forecasting, built on a Siamese Autoencoder pre-trained in a self-supervised manner and fine-tuned for different lead times. Experimental results show that Baguan outperforms traditional methods, delivering more accurate forecasts. Additionally, the pre-trained Baguan demonstrates robust overfitting control and excels in downstream tasks, such as subseasonal-to-seasonal (S2S) modeling and regional forecasting, after fine-tuning.
Robust and Explainable Detector of Time Series Anomaly via Augmenting Multiclass Pseudo-Anomalies
Authors: Kohei Obata, Yasuko Matsubara, Yasushi Sakurai
Venue: KDD
Year: 2025

Unsupervised anomaly detection in time series has been a pivotal research area for decades. Current mainstream approaches focus on learning normality, on the assumption that all or most of the samples in the training set are normal. However, anomalies in the training set (i.e., anomaly contamination) can be misleading. Recent studies employ data augmentation to generate pseudo-anomalies and learn the boundary separating the training samples from the augmented samples. Although this approach mitigates anomaly contamination if augmented samples mimic unseen real anomalies, it suffers from several limitations. (1) Covering a wide range of time series anomalies is challenging. (2) It disregards augmented samples that resemble normal samples (i.e., false anomalies). (3) It places too much trust in the labels of training and augmented samples. In response, we propose RedLamp, which employs diverse data augmentations to generate multiclass pseudo-anomalies and learns the multiclass boundary. Such multiclass pseudo-anomalies cover a wide variety of time series anomalies. We conduct multiclass classification using soft labels, which prevents the model from being overconfident and ensures its robustness against contaminated/false anomalies. The learned latent space is inherently explainable as it is trained to separate pseudo-anomalies into multiclasses. Extensive experiments demonstrate the effectiveness of RedLamp in anomaly detection and its robustness against anomaly contamination.
Fully Quanvolutional Networks for Time Series Classification
Authors: Nabil Anan Orka, Ehtashamul Haque, Md. Abdul Awal, Mohammad Ali Moni
Venue: KDD
Year: 2025

Despite the advancements in quantum convolution or quanvolution, challenges persist in making quanvolution scalable, efficient, and applicable to multi-dimensional data. Existing quanvolutional networks heavily rely on classical layers, with minimal quantum involvement due to inherent limitations in current quanvolution algorithms. Moreover, the application of quanvolution in the domain of 1D data remains largely unexplored. To address these limitations, we propose a new quanvolution algorithm-Quanv1D-capable of processing arbitrary-channel 1D data, handling variable kernel sizes, and generating a customizable number of feature maps, along with a classification network-fully quanvolutional network (FQN)-built solely using Quanv1D layers. Quanv1D is inspired by the classical Conv1D and stands out from the quanvolution literature by being fully trainable, modular, and freely scalable with a self-regularizing feature. To evaluate FQN, we tested it on 20 UEA and UCR time series datasets, both univariate and multivariate, and benchmarked its performance against state-of-the-art convolutional models (both quantum and classical). We found FQN to outperform all compared models in terms of average accuracy while using significantly fewer parameters. Additionally, to assess the viability of FQN on real hardware, we conducted a shot-based analysis across all the datasets to simulate statistical quantum noise and found our model robust and equally efficient.