OpenRaiser/Intern-Atlas huggingface dataset:
HRSTORY: Historical News Review Based Online Story Discovery
Authors: Renjie Zhou, Haoran Ye, Jian Wan, Yong Liao
Venue: KDD
Year: 2025

Story discovery on news streams can help people quickly find story from vast amounts of news, improving the efficiency of information acquisition. Recent online story discovery methods encode text topics and then cluster articles into stories based on similarity. However, the results obtained by these methods are one-time, and clustered news cannot adaptively update in a continuous news stream. Additionally, the inadequate quality of article encoding and the presence of noise data deteriorate the performance of story discovery. To this end, we propose HRSTORY for online story discovery on news streams, which employs a historical news review method to enable news to continuously adapt to the latest environment in the stream data and make corrections and updates. Furthermore, HRSTORY captures better article embeddings through modeling multi-layer relational dependencies within the text. By using sentence-level noise masking, HRSTORY improves the relevance of news article representation to core topics and reduces the interference of noise data. Experiments on real news datasets show that HRSTORY outperforms the state-of-the-art algorithms in unsupervised online story discovery performance.
Federated Continual Graph Learning
Authors: Yinlin Zhu, Miao Hu, Di Wu
Venue: KDD
Year: 2025

Managing evolving graph data presents substantial challenges in storage and privacy, and training graph neural networks (GNNs) on such data often leads to catastrophic forgetting, impairing performance on earlier tasks. Despite existing continual graph learning (CGL) methods mitigating this to some extent, they rely on centralized architectures and ignore the potential of distributed graph databases to leverage collective intelligence. To this end, we propose Federated Continual Graph Learning (FCGL) to adapt GNNs across multiple evolving graphs under storage and privacy constraints. Our empirical study highlights two core challenges: local graph forgetting (LGF), where clients lose prior knowledge when adapting to new tasks, and global expertise conflict (GEC), where the global GNN exhibits sub-optimal performance in both adapting to new tasks and retaining old ones, arising from inconsistent client expertise during server-side parameter aggregation. To address these, we introduce POWER, a framework that preserves experience nodes with maximum local-global coverage locally to mitigate LGF, and leverages pseudo-prototype reconstruction with trajectory-aware knowledge transfer to resolve GEC. Experiments on various graph datasets demonstrate POWER's superiority over federated adaptations of CGL baselines and vision-centric federated continual learning approaches.
Tackling Federated Long-Tailed Learning via Synthetic Feature-Based Decoupled Training
Authors: Huabin Zhu, Chaochao Chen, Xinting Liao, Pengyang Zhou, Xiaolin Zheng
Venue: KDD
Year: 2025

Federated learning (FL) enables collaborative training on decentralized data while preserving privacy by avoiding direct data sharing. However, long-tailed data distributions are common in real-world applications, often resulting in biased models with degraded performance. In FL, this issue is further complicated by privacy-preserving constraints and non-IID data, highlighting the importance of federated long-tailed learning (Fed-LT). To tackle the challenges of Fed-LT, we propose Synthetic Feature-based Decoupled training (SFD) method. To improve local training, we introduce Adaptive Bi-Branch Learning (ABBL) to jointly enhance feature representation and decision boundary learning for non-IID long-tailed data. To mitigate global model bias while preserving privacy, we propose Statistically Aligned Feature Synthesis (SAFS) for global classifier fine-tuning. SAFS constructs privacy-preserving synthetic features that approximate the global feature distribution. These synthetic features enable the global classifier to be fine-tuned without requiring clients to share local training data, thereby alleviating the model bias caused by non-IID long-tailed data. Extensive experiments show that SFD effectively addresses the challenges of Fed-LT and achieves superior performance on Fed-LT datasets.
The 6th International Workshop on Talent and Management Computing (TMC 2025)
Authors: Hengshu Zhu, Yong Ge, Hui Xiong, Ee-Peng Lim
Venue: KDD
Year: 2025

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with talent and management-related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for leaders to deliver intelligence for effective management for organizations. In the past few years, talent and management computing have increasingly attracted attention from KDD communities, and a number of research/applied data science efforts have been devoted. To this end, the purpose of this workshop, i.e., the 6th International Workshop on Talent and Management Computing (TMC 2025), is to bring together researchers and practitioners to discuss critical problems faced by talent and management-related domains and potential data-driven solutions.
Contextual Generative Auction with Permutation-level Externalities for Online Advertising
Authors: Ruitao Zhu, Yangsu Liu, Dagui Chen, Zhenjia Ma, Chufeng Shi, Zhenzhe Zheng, Jie Zhang, Jian Xu, Bo Zheng, Fan Wu
Venue: KDD
Year: 2025

Online advertising has become a core revenue driver for internet industry, with ad auctions playing a crucial role in ensuring platform revenue and advertiser incentives. Classical auction mechanisms, such as GSP, rely on the independent CTR assumption and fail to account for the interplay among the displayed items, also called as externalities in economics. Recent advancements in learning-based auctions enable the encoding of high-dimensional contextual features. However, existing methods are limited by the ''prediction-before-allocation'' design paradigm, which models set-level externalities within candidate ads and fails to consider the context of the final allocation, leading to suboptimal results. In this work, we introduce Contextual Generative Auction (CGA), a novel framework that incorporates permutation-level externalities in multi-slot ad auctions. Built on the structure of our theoretically derived optimal auction, CGA decouples the optimization of allocation and payment. We construct an autoregressive generative model for allocation, and reformulate incentive compatibility (IC) constraint into minimizing ex-post regret that supports gradient computation, enabling end-to-end learning of the optimal payment rule. Extensive offline and online experiments demonstrate that CGA significantly enhances platform revenue and CTR compared to existing methods, and effectively approximates the optimal auction with nearly maximal revenue and minimal regret.
TTGL: Large-scale Multi-scenario Universal Graph Learning at TikTok
Authors: Shichao Zhu, Mufan Li, Guangmou Pan, Xixun Lin
Venue: KDD
Year: 2025

This paper presents TTGL, a large-scale, multi-scenario universal graph learning framework developed at TikTok, which generates unified embeddings that capture diverse user interests across multiple contexts. TTGraph handles over 100 billion edges and 10 billion nodes daily, linking user behaviors from various scenarios such as video, search, live streaming and e-commerce. TTGL showcases a versatile strategy designed for handling large amounts of data and practical limitations, highlighting the balance between efficiency and precision in a high-volume production setting. TTGL utilizes a heterogeneous graph to unify user behaviors across different TikTok scenarios, dynamically learning shared embeddings that capture complex data patterns. It implements advanced domain-adaptive graph sampling and unsupervised training methods to handle its scale while ensuring fair representation. The graph embeddings are fed into online serving pipelines, delivering real-time recommendations that effectively balance efficiency and revenue generation. These effective graph online serving solutions are highly versatile, demonstrating their impact not only in TikTok's Feed video recommendations but also across various business scenarios such as e-commerce and live streaming. Through extensive offline evaluations and online A/B testing, we demonstrate that TTGL significantly improves key metrics across multiple contexts, achieving a 0.35% increase in 7-day retention, a 3.27% rise in watch live duration, a 1.32% boost in gross merchandise value, a 1.07% increase in paid order count, and a 1.0% improvement in search page views.
FedAPM: Federated Learning via ADMM with Partial Model Personalization
Authors: Shengkun Zhu, Feiteng Nie, Jinshan Zeng, Sheng Wang, Yuan Sun, Yuan Yao, Shangfeng Chen, Quanqing Xu, Chuanhui Yang
Venue: KDD
Year: 2025

In federated learning (FL), the assumption that datasets from different devices are independent and identically distributed (i.i.d.) often does not hold due to user differences, and the presence of various data modalities across clients makes using a single model impractical. Personalizing certain parts of the model can effectively address these issues by allowing those parts to differ across clients, while the remaining parts serve as a shared model. However, we found that partial model personalization may exacerbate client drift (each client's local model diverges from the shared model), thereby reducing the effectiveness and efficiency of FL algorithms. We propose an FL framework based on the alternating direction method of multipliers (ADMM), referred to as FedAPM, to mitigate client drift. We construct the augmented Lagrangian function by incorporating first-order and second-order proximal terms into the objective, with the second-order term providing fixed correction and the first-order term offering compensatory correction between the local and shared models. Our analysis demonstrates that FedAPM, by using explicit estimates of the Lagrange multiplier, is more stable and efficient in terms of convergence compared to other FL frameworks. We establish the global convergence of FedAPM training from arbitrary initial points to a stationary point, achieving three types of rates: constant, linear, and sublinear, under mild assumptions. We conduct experiments using four heterogeneous and multimodal datasets with different metrics to validate the performance of FedAPM. Specifically, FedAPM achieves faster and more accurate convergence, outperforming the SOTA methods with average improvements of 12.3% in test accuracy, 16.4% in F1 score, and 18.0% in AUC while requiring fewer communication rounds.
Prices Do Matter: Modeling Price Competitiveness for Online Hotel Industry
Authors: Ruitao Zhu, Wendong Xiao, Yao Yu, Yangsu Liu, Zhenzhe Zheng, Shuqi Zhang, Dong Li, Fan Wu
Venue: KDD
Year: 2025

Broad adoption of Online Travel Platforms (OTPs) has led to increasing interest in accurately predicting users' hotel purchase behavior, with price being a key influencer in user decision-making and receiving significant focus. In examining the hotel purchasing process, we identify a pervasive trend that users make extensive price comparisons before making decisions. Existing research primarily focuses on a hotel's own price, neglecting the complex dynamics of market-driven price competition. In this paper, we propose the concept of Marketplace-oriented Hotel Price Competitiveness (MHPC) to model a hotel's pricing competitiveness within the marketplace. Being independent of specific user preferences, MHPC can be applied to and improve various downstream operations in the online hotel industry, such as hotel ranking and pricing, ultimately benefiting hoteliers, users, and OTPs. Furthermore, a novel Hotel Price Competitiveness-aware Purchase Prediction Model (HP3M) is constructed by incorporating MHPC and demand dynamics into a multi-task learning framework, featuring three distinct submodules to encompass the tri-dimensional facets of MHPC. Extensive offline and online experiments demonstrate HP3M's effectiveness in predicting hotel purchase probability and enhancing the performance of hotel ranking and pricing compared to the state-of-the-art methods. HP3M has been fully deployed on Fliggy, a leading OTP in China, serving thousands of hoteliers and tens of millions of users.
Learning Generalized and Flexible Trajectory Models from Omni-Semantic Supervision
Authors: Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Xiao Han, Qidong Liu, Xuetao Wei, Yuxuan Liang
Venue: KDD
Year: 2025

The widespread adoption of mobile devices and data collection technologies has led to an exponential increase in trajectory data, presenting significant challenges in spatio-temporal data mining, particularly for efficient and accurate trajectory retrieval. However, existing methods for trajectory retrieval face notable limitations, including inefficiencies in large-scale data, lack of support for condition-based queries, and reliance on trajectory similarity measures. To address the above challenges, we propose OmniTraj, a generalized and flexible omni-semantic trajectory retrieval framework that integrates four complementary modalities or semantics--raw trajectories, topology, road segments, and regions--into a unified system. Unlike traditional approaches that are limited to computing and processing trajectories as a single modality, OmniTraj designs dedicated encoders for each modality, which are embedded and fused into a shared representation space. This design enables OmniTraj to support accurate and flexible queries based on any individual modality or combination thereof, overcoming the rigidity of traditional similarity-based methods. Extensive experiments on two real-world datasets demonstrate the effectiveness of OmniTraj in handling large-scale data, providing flexible, multi-modality queries, and supporting downstream tasks and applications.
Exploring Feature-based Knowledge Distillation for Recommender System: A Frequency Perspective
Authors: Zhangchi Zhu, Wei Zhang
Venue: KDD
Year: 2025

In this paper, we analyze the feature-based knowledge distillation for recommendation from the frequency perspective. By defining knowledge as different frequency components of the features, we theoretically demonstrate that regular feature-based knowledge distillation is equivalent to equally minimizing losses on all knowledge and further analyze how this equal loss weight allocation method leads to important knowledge being overlooked. In light of this, we propose to emphasize important knowledge by redistributing knowledge weights. Furthermore, we propose FreqD, a lightweight knowledge reweighting method, to avoid the computational cost of calculating losses on each knowledge. Extensive experiments demonstrate that FreqD consistently and significantly outperforms state-of-the-art knowledge distillation methods for recommender systems. Our code is available at https://github.com/woriazzc/KDs.
Fine-grained Urban Heat Island Effect Forecasting: A Context-aware Thermodynamic Modeling Framework
Authors: Xingchen Zou, Weilin Ruan, Siru Zhong, Yuehong Hu, Yuxuan Liang
Venue: KDD
Year: 2025

Climate change and rapid urbanization have led to the Urban Heat Island (UHI) effect, resulting in higher temperatures in metropolitan areas and negatively impacting urban communities. Accurate UHI forecasting is crucial for identifying high-risk periods and locations, especially in cities with vulnerable populations. Current methods are limited by data granularity and inadequate modeling of regional thermodynamics, which affects both accuracy and spatio-temporal granularity. In this paper, we propose DeepUHI, a data-driven context-aware framework for modeling local thermodynamics based on the heat equation, alongside the SeoulTemp dataset, the first multi-modal dataset for UHI effect predictions at the street level. Our framework utilizes a heat decomposition method to represent urban thermodynamics through thermodynamic cycles and thermal flows, effectively integrating urban environmental data. Extensive experiments show that our framework improves accuracy in UHI effect prediction and warning tasks, outperforming leading models. We have integrated DeepUHI into our SeoUHI platform to provide hourly street-level UHI forecasting for Seoul. The code, platform, and dataset are accessible at https://github.com/CityMind-Lab/DeepUHI.
Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks
Authors: Lin Zuo, Yongqi Ding, Mengmeng Jing, Kunshan Yang, Biao Chen, Yunqian Yu
Venue: KDD
Year: 2025

This paper explores the application of spiking neural networks (SNNs), known for their low-power binary spikes, to bearing fault diagnosis, bridging the gap between high-performance AI algorithms and real-world industrial scenarios. In particular, we identify two key limitations of existing SNN fault diagnosis methods: inadequate encoding capacity that necessitates cumbersome data preprocessing, and non-spike-oriented architectures that constrain the performance of SNNs. To alleviate these problems, we propose a Multi-scale Residual Attention SNN (MRA-SNN) to simultaneously improve the efficiency, performance, and robustness of SNN methods. By incorporating a lightweight attention mechanism, we have designed a multi-scale attention encoding module to extract multiscale fault features from vibration signals and encode them as spatio-temporal spikes, eliminating the need for complicated preprocessing. Then, the spike residual attention block extracts high-dimensional fault features and enhances the expressiveness of sparse spikes with the attention mechanism for end-to-end diagnosis. In addition, the performance and robustness of MRA-SNN is further enhanced by introducing the lightweight attention mechanism within the spiking neurons to simulate the biological dendritic filtering effect. Extensive experiments on MFPT, JNU, Bearing, and Gearbox benchmark datasets demonstrate that MRA-SNN significantly outperforms existing methods in terms of accuracy, energy consumption, and noise robustness, and is more feasible for deployment in real-world industrial scenarios. Our codes are available at https://github.com/yqding326/MRA-SNN.
A File Organization for Cluster-Based Retrieval
Authors: W. Bruce Croft
Venue: SIGIR
Year: 1978

A file organization for cluster-based retrieval is presented and tested. This file organization is based on the bottom-up search which, in contrast to the more usual top-down search, starts at the lowest level of a cluster hierarchy (the documents) and looks at progressively larger clusters. This approach enables most of the efficiency problems previously associated with clustered file organizations to be avoided. There are two parts to this file organization - a compact cluster hierarchy representation which does not store cluster representatives and a compact inverted file which is used to provide a starting point for the bottom-up search.
A Tree Algorithm For Nearest Neighbor Searching In Document Retrieval Systems
Authors: Caroline M. Eastman, Stephen F. Weiss
Venue: SIGIR
Year: 1978

The problem of finding nearest neighbors to a query in a document collection is a special case of associative retrieval, in which searches are performed using more than one key. A nearest neighbors associative retrieval algorithm, suitable for document retrieval using similarity matching, is described. The basic structure used is a binary tree, at each node a set of keys (concepts) is tested to select the most promising branch. Backtracking to initially rejected branches is allowed and often necessary.
Analysis of An Inverted Data Base Structure
Authors: Edward Hill Jr.
Venue: SIGIR
Year: 1978

An inverted data base organization is analyzed. The inverted directory is viewed realistically as another large data base. Algorithms and formulations are derived to estimate the average number of accesses for insertion, retrieval and deletion of items from the data base. An average load time is also presented for the inverted data base.
Inquire System Overview
Venue: SIGIR
Year: 1978

article Free Access Share on INQUIRE system overviewACM SIGIR ForumVolume 13Issue 1May 1978 pp 171–174https://doi.org/10.1145/1013234.803142Online:01 May 1978Publication History 0citation31DownloadsMetricsTotal Citations0Total Downloads31Last 12 Months9Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
INSPECTOR
Venue: SIGIR
Year: 1978

article INSPECTOR Share on ACM SIGIR ForumVolume 13Issue 1May 1978 pp 175–176https://doi.org/10.1145/1013234.803143Online:01 May 1978Publication History 0citation19DownloadsMetricsTotal Citations0Total Downloads19Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
MAGIC
Venue: SIGIR
Year: 1978

article Free Access Share on MAGICACM SIGIR ForumVolume 13Issue 1May 1978 https://doi.org/10.1145/1013234.803144Online:01 May 1978Publication History 0citation30DownloadsMetricsTotal Citations0Total Downloads30Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
SIRE
Venue: SIGIR
Year: 1978

SIRE (Syracuse Information Retrieval Experiment) is an interactive bibliographic retrieval system. It was developed at the School of Information Studies at Syracuse University. It is implemented in SAIL (Stanford Artificial Intelligence Language), an ALGOL like language, on a DEC KL-10. SIRE presently has two data bases; 1) one issue of Physics Abstracts with 7146 documents; and 2) a sign language linguistics data base from Gallaudet College with 490 documents.
Mediator: An Integrated Approach to Information Retrieval
Authors: Jacob Slonim, Fred J. Maryanski, Paul S. Fisher
Venue: SIGIR
Year: 1978

Mediator: An Integrated Approach to Information Retrieval
Augmented Transition Networks as a Design Tool for Personalized Data Base Systems
Authors: Alan L. Tharp
Venue: SIGIR
Year: 1978

This paper illustrates the use of Augmented Transition Networks (ATNs) as a design tool for constructing document retrieval systems for those personalized applications which are too small or specialized to attract a commercial vendor. ATNs, which are explained in the context of this application, are used not only to improve the human/computer interface with the retrieval system but also to conceptually organize its structure.
Does Relevance Feedback Improve Document Retrieval Performance?
Authors: Robert L. Williams
Venue: SIGIR
Year: 1978

Many authors (1, 2, 3, 5, 6, 7) have suggested that overall performance of a document retrieval system is improved by relevance feedback. Relevance feedback denotes the last three steps in the following process: 1) the searcher enters a query, 2) the system prepares a ranked list of suggested documents, 3) the searcher judges some of the documents for relevancy, 4) the searcher informs the system of these documents judged and of the judgement, 5) the system constructs a new query based on the descriptors used in the original query and the descriptors used in the documents judged, 6) the system prepares a second ranked list of suggested documents.