OpenRaiser/Intern-Atlas huggingface dataset:
Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models
Authors: Yunjia Xi, Muyan Weng, Wen Chen, Chao Yi, Dian Chen, Gaoyang Guo, Mao Zhang, Jian Wu, Yuning Jiang, Qingwen Liu, Yong Yu, Weinan Zhang
Venue: KDD
Year: 2025

Recommender systems (RSs) often suffer from the feedback loop phenomenon, i.e., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that reinforces homogeneous content and reduces user satisfaction. To this end, serendipity recommendations, which offer unexpected yet relevant items, are proposed. Recently, large language models (LLMs) have shown potential in serendipity prediction due to their extensive world knowledge and reasoning capabilities. However, they still face challenges in aligning serendipity judgments with human assessments, handling long user behavior sequences, and meeting the latency requirements of industrial RSs. To address these issues, we propose SERAL (Serendipity Recommendations with Aligned Large Language Models), a framework comprising three stages: (1) Cognition Profile Generation to compress user behavior into multi-level profiles; (2) SerenGPT Alignment to align serendipity judgments with human preferences using enriched training data; and (3) Nearline Adaptation to integrate SerenGPT into industrial RSs pipelines efficiently. Online experiments demonstrate that SERAL improves exposure ratio (PVR), clicks, and transactions of serendipitous items by 5.7%, 29.56%, and 27.6%, enhancing user experience without much impact on overall revenue. Now, it has been fully deployed in ''Guess What You Like'' on the Taobao App homepage.
A Transferable Spatio-temporal Learning Framework for Cross-city Logistics Demand Prediction
Authors: Kaiwen Xia, Li Lin, Xinrui Zhang, Haotian Wang, Shuai Wang, Tian He
Venue: KDD
Year: 2025

In logistic systems, demand prediction is an essential task providing the basis for improving the quality of terminal services, such as pick-up and delivery efficiency. However, the geographical scope of operations across multiple cities brings challenges due to the sparsity of user behavior data, hindering accurate predictions. Despite cross-city prediction methods potentially solving this problem by relying on the label of overlapping users in different cities, annotating these overlapping users is expensive. Additionally, the dynamic and diverse nature of user behaviors complicates feature transfer between cities. In this work, we define the logistics demand prediction problem as forecasting pick-up and delivery demand for zones, the smallest operational units in logistics systems, in different cities. To address the challenge, we propose TSTL, a Transferable Spatio-Temporal Learning framework for cross-city logistics prediction with sparse user data. TSTL advances existing methods from two aspects: (1) User-level invariant representation module extracts consistent user representations for overlapping and non-overlapping users across cities. (2) User-zone graph aggregation module enhances user embeddings by integrating dynamic interactions, such as logistics behaviors, into inherent user relations. Finally, the multi-city transfer module fine-tunes model parameters for city-invariant knowledge adoption and predicts future logistics demand. We implement and evaluate TSTL on one of the largest logistics systems. Extensive offline experiments and real-world deployment demonstrate the effectiveness of TSTL.
Maximum Degree-Based Quasi-Clique Search via an Iterative Framework
Authors: Hongbo Xia, Kaiqiang Yu, Shengxin Liu, Cheng Long, Xun Zhou
Venue: KDD
Year: 2025

Cohesive subgraph mining is a fundamental problem in graph theory with numerous real-world applications, such as social network analysis and protein-protein interaction modeling. Among various cohesive subgraphs, the γ-quasi-clique is widely studied for its flexibility in requiring each vertex to connect to at least a γ proportion of other vertices in the subgraph. However, solving the maximum γ-quasi-clique problem is NP-hard and further complicated by the lack of the hereditary property, which makes designing efficient pruning strategies challenging. Existing algorithms, such as DDA and FastQC, either struggle with scalability or exhibit significant performance declines for small values of γ. In this paper, we propose a novel algorithm, IterQC, which reformulates the maximum γ-quasi-clique problem as a series of k-plex problems that possess the hereditary property. IterQC introduces a non-trivial iterative framework and incorporates two key optimization techniques: (1) the pseudo lower bound (pseudo LB) technique, which leverages information across iterations to improve the efficiency of branch-and-bound searches, and (2) the preprocessing technique that reduces problem size and unnecessary iterations. Extensive experiments demonstrate that IterQC achieves up to four orders of magnitude speedup and solves significantly more graph instances compared to state-of-the-art algorithms DDA and FastQC.
Hyperparametric Influence Minimization: Feature-Driven Intervention Beyond Blocking
Authors: Bin Xiang, Bogdan Cautis, Xiaokui Xiao, Laks V. S. Lakshmanan
Venue: KDD
Year: 2025

In this paper, we investigate the diffusion containment problem through a novel hyperparametric influence minimization model. This model integrates a hyperparametric diffusion framework into the classical influence minimization paradigm, enabling practical, flexible, and fine-grained control over diffusion dynamics via feature interventions on nodes. The objective is to minimize the diffusion from initial seeds, by optimizing the interventions on node feature values. We analyze the challenges and intrinsic properties of hyperparametric influence minimization and derive an upper-bound on the spread, which quantifies the total uncertainty of nodes remaining inactive during the diffusion process. We prove that it exhibits supermodularity in the context of the node selection problem. Based on that, we further design greedy-based algorithms to solve the problem, which outperform the state-of-the-art methods.
Effective AOI-level Parcel Volume Prediction: When Lookahead Parcels Matter
Authors: Yinfeng Xiang, Jiangyi Fang, Chao Li, Haitao Yuan, Yiwei Song, Jiming Chen
Venue: KDD
Year: 2025

Last-mile Delivery Parcel Volume (LDPV) quantifies the number of parcels destined for a specific region, particularly a manually divided Area-Of-Interest (AOI). Accurate prediction of AOI-level LDPV is crucial for the efficient management of logistics resources. However, the straightforward adaptation of existing prediction models often falls short, primarily due to (I) a lack of consideration for the intuition behind AOI divisions, and (II) a reliance solely on fully observed historical data, which may not inform future trends. To overcome the above pitfalls, leveraging rich AOI data and advanced parcel travel time estimation services in JD Logistics, this paper introduces a novel framework called Dual-view Prediction Networks (DualPNs). It combines a Vector-Quantified AutoEncoder (VQ-AE) and a Template-Augmented Zero-Inflated Poisson (TA-ZIP), enabling both point and probabilistic distribution predictions of AOI-level LDPV. Specifically, VQ-AE utilizes a vector quantization technique to distill a large number of AOIs into representative templates, thereby addressing the first pitfall. Subsequently, TA-ZIP dynamically integrates fully observed and lookahead features, aligning them with template-specific decoders to parameterize the probabilistic distributions, thus resolving the second pitfall. We conduct extensive experiments in two cities, comprising over 47,000 and 126,000 AOIs respectively, to demonstrate the superiority of our DualPNs over other baselines. Moreover, a real-world case study highlights the effectiveness of DualPNs for enhancing downstream courier allocation by yielding an average improvement of 1.51% in the on-time delivery rate.
Truss-based Why-not Community Search
Authors: Huan Xie, Qing Liu, Chengyang Luo, Yuhan Zhou, Yunjun Gao
Venue: KDD
Year: 2025

In this paper, we investigate a new problem of truss-based why-not community search. Given a k-truss community C in a graph G and a why-not vertex w ∉ C , the goal is to insert the minimum number of new edges into G to ensure that w becomes part of the k-truss community. This problem has a wide range of applications, such as friends recommendation and transportation planning. We prove that the truss-based why-not community search problem is NP-hard and propose two efficient heuristic algorithms: the expansion-based algorithm and the simulation-based algorithm. Specifically, the expansion-based algorithm incrementally inserts the edges one by one, guided by a carefully designed edge goodness function that quantifies edge quality to ensure optimal selection. In contrast, the simulation-based algorithm firstly inserts a sufficient number of edges into G to immediately include w in a k-truss community and then removes redundant edges to minimize insertions. Furthermore, we implement a set of optimizations to further enhance the efficiency of both algorithms. Extensive experiments on real-world graphs demonstrate the efficiency and effectiveness of our proposed methods and optimizations.
M2-MFP: A Multi-Scale and Multi-Level Memory Failure Prediction Framework for Reliable Cloud Infrastructure
Authors: Hongyi Xie, Min Zhou, Qiao Yu, Jialiang Yu, Zhenli Sheng, Hong Xie, Defu Lian
Venue: KDD
Year: 2025

As cloud services become increasingly integral to modern IT infrastructure, ensuring hardware reliability is essential to sustain high-quality service. Memory failures pose a significant threat to overall system stability, making accurate failure prediction through the analysis of memory error logs (i.e., Correctable Errors) imperative. Existing memory failure prediction approaches have notable limitations: rule-based expert models suffer from limited generalizability and low recall rates, while automated feature extraction methods exhibit suboptimal performance. To address these limitations, we propose M2-MFP: a Multi-scale and Multi-Level Memory Failure Prediction framework designed to enhance the reliability and availability of cloud infrastructure. M2-MFP converts correctable errors (CEs) into multi-level binary matrix representations and introduces a Binary Spatial Feature Extractor (BSFE) to automatically extract high-order features at both DIMM-level and bit-level. Building upon the BSFE outputs, we develop a dual-path temporal modeling architecture: 1) a time-patch module that aggregates multi-level features within observation windows, and 2) a time-point module that employs interpretable rule-generation trees trained on bit-level patterns. Experiments on both benchmark datasets and real-world deployment show the superiority of M2-MFP as it outperforms existing state-of-the-art methods by significant margins. Code and data are available at this repository: https://github.com/hwcloud-RAS/M2-MFP.
Breaking the Bottleneck: User-Specific Optimization and Real-Time Inference Integration for Sequential Recommendation
Authors: Wenjia Xie, Hao Wang, Minghao Fang, Ruize Yu, Wei Guo, Yong Liu, Defu Lian, Enhong Chen
Venue: KDD
Year: 2025

Sequential recommendation (SR), as an important branch of recommendation systems, has garnered significant attention due to its substantial commercial value. This has inspired some researchers to draw from the successful experiences of large language models to develop scaling laws for SR. However, the improvements brought by parameter expansion often reach a limit when the data scale is fixed. We have observed that existing deep learning sequence methods are typically seen as learning a unified pattern of user interactions, as they apply the same model for inference across different users, which often leads to the neglect of individual user behavior patterns. To address this, we propose conducting an independent analysis of each user's interaction sequence in SR. We initially developed the PCRec-simple, which uses KL divergence to perform a one-time optimization on each sequence after training, demonstrating that optimizing individual sequences can provide additional insights and overcome the performance bottleneck after scaling laws. Subsequently, we introduce PCRec, a sequential recommendation model that integrates real-time inference of hidden states into the model. It applies KL divergence optimization during the forward process, allowing for end-to-end optimization and addressing issues of robustness, parallelism, and optimization stability. Extensive experiments on real-world datasets show that PCRec significantly outperforms the current state-of-the-art methods. The code can be found at https://github.com/USTC-StarTeam/PCRec.
Scalable Area Difficulty Assessment with Knowledge-enhanced AI for Nationwide Logistics Systems
Authors: Zejun Xie, Wenjun Lyu, Yiwei Song, Haotian Wang, Guang Yang, Yunhuai Liu, Tian He, Desheng Zhang, Guang Wang
Venue: KDD
Year: 2025

Logistics services have become a core business in online-to-offline e-commerce like Amazon, Alibaba, and JD. In logistics services, a city is partitioned into distinct geographical areas, and each area is assigned a worker, responsible for all delivery tasks within it. Due to varying geographic conditions (e.g., high-rise buildings, buildings without elevators), the difficulty of completing tasks can differ significantly between areas, which results in unbalanced workloads and salaries for workers. The necessity for scalable data-driven methods to assess area difficulty in logistics is well-recognized. However, the significant expenses associated with ground truth data collection limit the capabilities of current machine learning methods. In this paper, we consider a frequently overlooked resource, i.e., the workers' firsthand knowledge of areas, to address this problem in a human-AI collaboration fashion. In particular, we design RAICA (Ranking-Aggregated Isotonic Calibration Assessment) framework, which includes two key modules: (i) a Judgment Rank Aggregation module, which aggregates individual workers' judgment rankings collected from surveys into an overall ranking to mitigate personal biases and inconsistency between different workers; (ii) an Isotonic Calibration module, which calibrates the assessment from existing machine learning models with the aggregated ranking through Isotonic regression to enhance the accuracy of area difficulty assessment with theoretical guarantees. Extensive evaluation based on real-world data including over 2 million orders collected from 97 areas during 6 months by one of the largest logistics companies in the world shows that RAICA outperforms existing methods, increasing F1 score by 0.25. More importantly, RAICA has been deployed by this logistics company, which significantly improved crowdsourcing couriers' salary fairness with a 0.2 decrease in the Gini coefficient across over 1,200 delivery stations nationwide and increased the on-time delivery rates for full-time couriers by 1.67%.
Co-Evolution of Large Language Models and Configuration Strategies to Enhance Surrogate-Assisted Evolutionary Algorithm
Authors: Lindong Xie, Yang Zhang, Zhixian Tang, Edward Chung, Genghui Li, Zhenkun Wang
Venue: KDD
Year: 2025

Surrogate-assisted evolutionary algorithms (SAEAs) are well-suited for optimizing computationally expensive black-box problems in diverse real-world scenarios. The sample efficiency of SAEAs depends largely on the configuration of the surrogate model and sampling criteria. However, configuring these core components requires substantial manual effort and expert knowledge, limiting the broader applicability of SAEAs. To address these challenges, we propose CoE-SAEA, a novel paradigm that co-evolves large language models (LLMs) and configuration strategies to enhance SAEAs. Specifically, the paradigm consists of three populations with distinct roles: one evolves LLM prompts to generate robust configuration strategy instructions, another optimizes the configuration strategies, and the third solves the optimization problem using the selected algorithm configuration. Additionally, an exploration-exploitation module is incorporated to decide whether to explore new configuration strategies via LLMs or exploit existing ones. We empirically validate the efficacy of CoE-SAEA by comparing it to state-of-the-art algorithms across various benchmark problems and a real-world traffic signal optimization task. The source code of the proposed CoE-SAEA is publicly available at: https://github.com/ForrestXie9/CoE-SAEA.
AtomR: Atomic Operator-Empowered Large Language Models for Heterogeneous Knowledge Reasoning
Authors: Amy Xin, Jinxin Liu, Zijun Yao, Zhicheng Lee, Shulin Cao, Lei Hou, Juanzi Li
Venue: KDD
Year: 2025

Despite the outstanding capabilities of large language models (LLMs), knowledge-intensive reasoning still remains a challenging task due to LLMs' limitations in compositional reasoning and the hallucination problem. A prevalent solution is to employ chain-of-thought (CoT) with retrieval-augmented generation (RAG), which first formulates a reasoning plan by decomposing complex questions into simpler sub-questions, and then applies iterative RAG at each sub-question. However, prior works exhibit two crucial problems: inadequate reasoning planning and poor incorporation of heterogeneous knowledge. In this paper, we introduce AtomR, a framework for LLMs to conduct accurate heterogeneous knowledge reasoning at the atomic level. Inspired by how knowledge graph query languages model compositional reasoning through combining predefined operations, we propose three atomic knowledge operators, a unified set of operators for LLMs to retrieve and manipulate knowledge from heterogeneous sources. First, in the reasoning planning stage, AtomR decomposes a complex question into a reasoning tree where each leaf node corresponds to an atomic knowledge operator, achieving question decomposition that is highly fine-grained and orthogonal. Subsequently, in the reasoning execution stage, AtomR executes each atomic knowledge operator, which flexibly selects, retrieves, and operates atomic level knowledge from heterogeneous sources. We also introduce BlendQA, a challenging benchmark specially tailored for heterogeneous knowledge reasoning. Experiments on three single-source and two multi-source datasets show that AtomR outperforms state-of-the-art baselines by a large margin, with absolute F1 score improvements of 9.4% on 2WikiMultihop and 9.5% on BlendQA. We release our code and data https://github.com/THU-KEG/AtomR.git.
MoDiff - Graph Generation with Motif-aware Diffusion Model
Authors: Yuwei Xu, Chenhao Ma
Venue: KDD
Year: 2025

Temporal graphs, widely used in social network modeling, are valuable for research but pose challenges due to data accessibility and privacy concerns. High-quality graph generation models can produce surrogate data for sharing and training, benefiting tasks such as behavior analysis, anomaly detection, and data augmentation. However, existing deep learning and probabilistic approaches often struggle to balance global statistical properties with local structural details. To overcome this limitation, we leverage motifs, small subgraphs that serve as the building blocks of complex networks, to encode local information. Based on a spectral analysis of motifs, we propose MoDiff, a novel motif-aware diffusion model for temporal graph generation. MoDiff integrates motifs into a diffusion framework by employing motif-enhanced Hermitian matrices that capture local structures and edge orientations, while the spectral diffusion model efficiently generates graphs. Moreover, MoDiff supports controllable graph generation by adjusting density parameters to simulate the evolution of temporal graphs. Experimental results demonstrate that MoDiff outperforms existing approaches, reducing degree discrepancies by 10-50% and clustering discrepancies by 50-90%, while better preserving higher-order structural features. Our code is available at: https://github.com/Yuwe1XU/MoDiff.
Enhancing Digital Forensics Evidence Analysis with Large Language Models
Authors: Eric Xu, Lin Deng
Venue: KDD
Year: 2025

In an era where justice and accountability increasingly depend on digital evidence, Large Language Models (LLMs) offer transformative potential for digital forensics. This three-hour Hands-on tutorial explores how LLMs can automate investigations, reveal hidden insights, and enhance evidence analysis. Through real-world case studies, interactive exercises, and hands-on labs, participants will learn to leverage LLMs for tasks such as entity identification, evidence processing, and knowledge graph reconstruction. Designed for professionals, researchers, and students, this collaborative learning experience equips attendees with practical skills to innovate in digital forensics. As LLMs reshape the field, this tutorial underscores their role in improving justice outcomes, strengthening accountability, and advancing the future of digital investigations.
Agentic AI for Enterprise: Emerging Applications and Real-world Challenges
Authors: Anbang Xu, Min Du, Tan Yu, Meghana Puvvadi, Tao Yu, Yufan Guo, Xinyun Chen, Justin Gottschlich
Venue: KDD
Year: 2025

Large language models (LLMs) have revolutionized natural language processing, enabling unprecedented capabilities in reasoning, planning, and tool utilization. Enterprises are increasingly adopting LLM-powered agents to automate complex workflows, from meeting summarization (e.g., Microsoft Copilot) to supply chain optimization and customer service orchestration. However, deploying agentic AI systems in enterprise settings introduces unique challenges, including decision making under uncertainty, multi-agent collaboration, security vulnerabilities, and trust gaps in mission-critical applications. This workshop aims to bridge the gap between academia and industry to explore LLM-driven agentic systems tailored for enterprise needs. We focus on three pillars: 1) emerging architectures that enable dynamic task decomposition and tool invocation; 2) domain-specific applications such as case studies in supply chain and employee productivity domain; 3) evaluation and governance such as the AAEF (Agentic Application Evaluation Framework) and security strategies.
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators
Authors: Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui
Venue: KDD
Year: 2025

In this paper, we present the Proportional Payoff Allocation Game (PPA-Game), which characterizes situations where agents compete for divisible resources. In the PPA-game, agents select from available resources, and their payoffs are proportionately determined based on heterogeneous weights attributed to them. Such dynamics simulate content creators on online recommender systems like YouTube and TikTok, who compete for finite consumer attention, with content exposure reliant on inherent and distinct quality. We first conduct a game-theoretical analysis of the PPA-Game. While the PPA-Game does not always guarantee the existence of a pure Nash equilibrium (PNE), we identify prevalent scenarios ensuring its existence. Simulated experiments further prove that the cases where PNE does not exist rarely happen. Beyond analyzing static payoffs, we further discuss the agents' online learning about resource payoffs by integrating a multi-player multi-armed bandit framework. We propose an online algorithm facilitating each agent's maximization of cumulative payoffs over T rounds. Theoretically, we establish that the regret of any agent is bounded by O(log^1 + η T) for any η > 0. Empirical results further validate the effectiveness of our online learning approach.