OpenRaiser/Intern-Atlas huggingface dataset:
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation
Authors: Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim
Venue: WSDM
Year: 2025

Message-passing neural networks are widely employed in various graph mining applications. However, these methods are susceptible to the scarcity of labeled data, which often leads to overfitting. Our observations suggest that sparse initial vectors further exacerbate this issue by failing to fully represent the range of learnable parameters. This sparsity can hinder the optimization of specific dimensions in the initial projection matrix, as the training samples may not adequately span these parameters. To overcome this challenge, we propose a novel perturbation technique that introduces variability to the initial features and the projection hyperplane. Notably, even without employing grid search, we demonstrate that shifting with a small estimated value mitigates this problem more effectively than other perturbation methods. Experimental results on real-world datasets reveal that our technique significantly enhances node classification accuracy in semi-supervised scenarios.
Review-Based Hyperbolic Cross-Domain Recommendation
Authors: Yoonhyuk Choi, Jiho Choi, Taewook Ko, Chong-Kwon Kim
Venue: WSDM
Year: 2025

The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target) has emerged recently. Nevertheless, existing methodologies assume an Euclidean embedding space, encountering difficulties in accurately representing richer text information and managing complex user-item interactions. This paper advocates a hyperbolic CDR approach for modeling review-based user-item relationships. We first emphasize that conventional distance-based domain alignment techniques may cause problems because small modifications in hyperbolic geometry result in magnified perturbations, ultimately leading to the collapse of hierarchical structures. To address this challenge, we propose hierarchy-aware embedding and domain alignment schemes that adjust the scale to extract domain-shareable information without disrupting structural forms. Extensive experiments substantiate the efficiency, robustness, and scalability of the proposed model. The source code is given here https://github.com/ChoiYoonHyuk/HEAD.
Maintaining k-MinHash Signatures over Fully-Dynamic Data Streams with Recovery
Authors: Andrea Clementi, Luciano Gualà, Luca Pepè Sciarria, Alessandro Straziota
Venue: WSDM
Year: 2025

We consider the task of performing Jaccard similarity queries over a large collection of items that are dynamically updated according to a streaming input model. An item here is a subset of a large universe U of elements. A well-studied approach to address this important problem in data mining is to design fast-similarity data sketches. In this paper, we focus on global solutions for this problem, i.e., a single data structure which is able to answer both Similarity Estimation and All-Candidate Pairs queries, while also dynamically managing an arbitrary, online sequence of element insertions and deletions received in input. We introduce and provide an in-depth analysis of a dynamic, buffered version of the well-known k-MinHash sketch. This buffered version better manages critical update operations thus significantly reducing the number of times the sketch needs to be rebuilt from scratch using expensive recovery queries. We prove that the buffered k-MinHash uses O(k łog |U|) memory words per subset and that its amortized update time per insertion/deletion is O(k łog |U|) with high probability. Moreover, our data structure can return the k-MinHash signature of any subset in O(k) time, and this signature is exactly the same signature that would be computed from scratch (and thus the quality of the signature is the same as the one guaranteed by the static k-MinHash). Analytical and experimental comparisons with the other, state-of-the-art global solutions for this problem given in [Bury et al.,WSDM'18] show that the buffered k-MinHash turns out to be competitive in a wide and relevant range of the online input parameters.
Unifying Bias and Unfairness in Information Retrieval: New Challenges in the LLM Era
Authors: Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu
Venue: WSDM
Year: 2025

With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a paradigm shift due to their integration. However, integrating LLMs into the IR pipelines has also introduced new challenges, particularly in the form of biases and unfairness that may disrupt the information ecosystem. This tutorial will offer a comprehensive overview of emerging and pressing bias and unfairness issues associated with integrating LLMs into IR systems. Specifically, this tutorial first unifies bias and unfairness issues as problems of distribution mismatch and further categorizes the mitigation strategies under the umbrella of distribution alignment. Then, we summarize several types of bias and unfairness issues emerging from three critical stages of LLM integration into IR systems: data collection, model development, and result evaluation. We will systematically review and analyze their definitions, characteristics, and corresponding mitigation strategies in recent literature. Finally, we will highlight some open problems and future research directions. We hope this tutorial can raise the awareness of researchers and stakeholders in the IR field and beyond regarding bias and unfairness issues in this LLM era.
Hyperdimensional Representation Learning for Node Classification and Link Prediction
Authors: Abhishek Dalvi, Vasant G. Honavar
Venue: WSDM
Year: 2025

We introduce Hyperdimensional Graph Learner (HDGL), a novel method for node classification and link prediction in graphs. HDGL maps node features into a very high-dimensional space (hyperdimensional or HD space for short) using the injectivity property of node representations in a family of Graph Neural Networks (GNNs) and then uses HD operators such as bundling and binding to aggregate information from the local neighborhood of each node yielding latent node representations that can support both node classification and link prediction tasks. HDGL, unlike GNNs that rely on computationally expensive iterative optimization and hyperparameter tuning, requires only a single pass through the data set. We report results of experiments using widely used benchmark datasets which demonstrate that, on the node classification task, HDGL achieves accuracy that is competitive with that of the state-of-the-art GNN methods at substantially reduced computational cost; and on the link prediction task, HDGL matches the performance of DeepWalk and related methods, although it falls short of computationally demanding state-of-the-art GNNs.
Towards Ethical and Personalized Web Navigation Agents: A Framework for User-Aligned Task Execution
Authors: Preetam Prabhu Srikar Dammu
Venue: WSDM
Year: 2025

Generative AI has advanced the capabilities of autonomous agents, enabling autonomous execution of complex web navigation tasks that can reshape digital interactions across various domains. Yet, to reach their full potential, these agents must be ethically aligned and personalized to individual user needs-a challenge complicated by privacy concerns and the risk of reinforcing biases. This work introduces a novel framework that enables responsible, user-guided personalization of web navigation agents, ensuring alignment with ethical standards and user preferences. By developing agents capable of perceiving, reasoning, and adapting in alignment with user preferences, this work proposes an approach that transcends generic task execution. Employing a structured representation of user-specific tasks, the agent utilizes interactive and reasoning actions to personalize workflows, adapting responsively to individual contexts. Evaluation through task success metrics and user satisfaction scores further assesses the ethical alignment and utility of personalized interactions. This research lays the groundwork for responsible agents that offer personalized assistance while adhering to ethical and privacy standards, with implications for information retrieval, e-commerce, and other knowledge-intensive applications.
A Shopping Agent for Addressing Subjective Product Needs
Authors: Preetam Prabhu Srikar Dammu, Omar Alonso, Barbara Poblete
Venue: WSDM
Year: 2025

In e-commerce, customers often struggle to find relevant items when their needs involve subjective properties characterized by personal or collective perception, tastes, and opinions, which are typically not captured in catalog data. This challenge is particularly pronounced in event-based scenarios like gifting, where selecting the right product involves complex subjective reasoning. Customer reviews can be a valuable source of subjective information to bridge this gap. Consequently, customers often spend significant amount of time navigating multiple products and reading numerous reviews to find suitable gifts that meet their needs. In order to reduce the effort involved, we propose an agentic approach driven by large language models to streamline this process by autonomously executing various user actions. These include computational tasks like vagueness detection and subjective product needs extraction, conversational interactions to gather missing user information, and web browsing actions that search for product details, reviews, and review images. Additionally, the agent employs generative actions to synthesize gifting ideas and explanations, helping users discover suitable products more efficiently. The proposed approach not only reduces the cognitive burden on users but also facilitates the exploration of a wider range of products. Our solution highlights the potential of autonomous agents to handle subjective queries in e-commerce, enhancing personalization, product exploration, and selection in a user-centric manner.
Tutorial on Recommendation with Generative Models (Gen-RecSys)
Authors: Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, Arnau Ramisa, René Vidal, Maheswaran Sathiamoorthy, Atoosa Kasirzadeh, Silvia Milano
Venue: WSDM
Year: 2025

This intermediate-level tutorial, titled "Gen-RecSys", merges both industrial and academic perspectives on recent advances in Generative AI for recommender systems (beyond LLMs). It aims to highlight the transformative role of generative models in modern recommender systems, which have significantly impacted the AI field-particularly with the rise of large language models (LLMs) like ChatGPT-and have contributed to a rapid convergence of the fields of search, data mining, and recommendation. By providing attendees with a modern perspective on GenAI applications in recommendation, the tutorial will emphasize how generative models can drive recommendation by unlocking and interacting with rich data representations, including behavioral, textual, and multi-modal data-knowledge highly transferable across many applications of interest to the WSDM community. Participants will learn about the categorization of generative models in recommender systems based on underlying data modalities: (i) ID-based collaborative models, (ii) text-driven models such as LLMs, and (iii) multi-modal models. Within each category, various deep generative model paradigms (e.g., AR, GAN, diffusion models) will be introduced, along with insights into their application areas. The tutorial will also cover evaluation aspects, including benchmarks, metrics, and assessments of social and ethical impacts and harms. This tutorial presents a condensed version of the industrial and academic work featured in the forthcoming book at FntIR 2024-25, titled "Recommendation with Generative Models [7]," and a shorter version prepared, and presented by the team, see GenRecSys-Survey [6].
Enhancing Code Search Intent with Programming Context Exploration
Authors: Yanmin Dong, Zhenya Huang, Zheng Zhang, Guanhao Zhao, Likang Wu, Hongke Zhao, Binbin Jin, Qi Liu
Venue: WSDM
Year: 2025

An intelligent code search engine tries to find and suggest a code piece given a developer's query quickly from a large-scale program database, which can significantly promote software development efficiency. Existing solutions can search the relevant codes to some extent. However, most of them fail to precisely understand the search intent of developers since they only mine their natural language queries, while ignoring the valuable programming context (e.g., the code written by the developer). In this paper, we study the novel problem of context-aware code search. To promote a step forward, we first provide the CodeSearchNet-C dataset with constructing sufficient programming context from the GitHub website for each query-code instance. The dataset is supplemented on the CodeSearchNet benchmark, ensuring both generality and comparability for relevant research. Then, by analyzing the characteristics of programming context, we propose a novel two-stage Context-aware Code Retrieval (ConCR) framework. In the first stage, we propose a Context Walking algorithm, which simulates the programming habits of different developers. The generated programming context could ensure the diversity of search intent among developers. In the second stage, imitating the reading habits of developers, we introduce a novel Context Hierarchical Encoder, to understand the search intent with contextual information from local to global. Our ConCR framework is general, and we give three implementations on the basis of typical code search models as backbones. Extensive experimental results clearly prove that our ConCR significantly enhances the code search performance, effectively fulfilling developers' needs for efficient code resource searching on the web. These results also verify the necessity of introducing programming context to understand developers' intent.
UniGLM: Training One Unified Language Model for Text-Attributed Graphs Embedding
Authors: Yi Fang, Dongzhe Fan, Sirui Ding, Ninghao Liu, Qiaoyu Tan
Venue: WSDM
Year: 2025

Representation learning on text-attributed graphs (TAGs), where nodes are associated with textual descriptions, is crucial for textual and relational knowledge systems, such as social media and recommendation scenarios. However, state-of-the-art embedding methods for TAGs primarily focus on fine-tuning pre-trained language models (PLMs) using structure-aware training objectives. While effective, these methods are tailored for individual TAG and cannot generalize across various graph scenarios. Given the shared textual space, leveraging multiple TAGs for joint fine-tuning, aligning text and graph structure from different aspects, would be more beneficial. Therefore, we propose the Unified Graph Language Model (UniGLM), a novel foundation model pretrained over multiple TAGs from a variety of domains, which can generalize well to both in-domain and cross-domain graph scenarios. Specifically, UniGLM fine-tunes well-established PLMs (e.g., Sentence-BERT) using a domain-aware contrastive learning objective that unifies structure heterogeneity and node statistics across various domains with an adaptive and learnable positive sample selection scheme. Additionally, a lazy updating module is introduced to speed up training by reducing repetitive encoding of positive samples. Extensive datasets across multiple domains, downstream tasks (node classification and link prediction), and a spectrum of graph backbones (supervised and self-supervised graph models) are conducted to compare UniGLM with state-of-the-art baselines. Our empirical observations suggest that UniGLM can generate informative representations for cross-domain graphs observed in the training. More importantly, UniGLM also exhibits competitive transfer ability in encoding unseen TAGs that are not used for training. This study provides deep insights into how to adapt PLMs to graph data and demonstrates the potential of building foundation model for graph representation learning.
Cluster Insight: A Weighted Clustering Tool for Large Textual Data Exploration
Authors: Amine Ferdjaoui, Séverine Affeldt, Mohamed Nadif
Venue: WSDM
Year: 2025

In unsupervised learning, the exploration of large volumes of textual data is a topic of significant interest. In this article, we present our compact and easy-to-use application to explore large volumes of textual data using clustering and generative models. We demonstrate how to adapt the Lasso weighted k-means algorithm to handle textual data. In addition, we present in detail a user-friendly package that shows how to use LLMs effectively to describe document classes.
1st Workshop on Detecting Trust, Authority, Sense and Knowledge in Online News Media Production
Authors: Giovanni Fulantelli, Davide Taibi, Sergio Splendore, Marco Fisichella
Venue: WSDM
Year: 2025

This workshop explores the intersection of media trust, journalistic authority and the role of Artificial Intelligence (AI) within platform-based media ecosystems. As social media becomes a primary source of news for global audiences media uses are changing public discourses and trust in journalism. The workshop includes papers that use innovative methods to analyze media trust and intertwining role of platforms (e.g. Facebook, Instagram, TikTok, and X). Key topics include AI-powered news curation, personalized news feeds and the impact of algorithm-driven content. Advanced techniques such as natural language processing, sentiment analysis and semantically enriched entity models are central to understanding and visualizing interactions between users, news providers and content. By integrating these approaches, the workshop aims to foster interdisciplinary discussions, propose new analytical frameworks and contribute to the development of balanced, transparent and fair news ecosystems in the digital age.
ProCC: Programmatic Reinforcement Learning for Efficient and Transparent TCP Congestion Control
Authors: Yin Gu, Kai Zhang, Qi Liu, Runlong Yu, Xin Lin, Xinjie Sun
Venue: WSDM
Year: 2025

Transmission Control Protocol (TCP) congestion control is a fundamental mechanism in the Internet that maintains network stability and performance by adjusting the sending rate of connections. Recently, Deep Reinforcement Learning (DRL) methods have shown superior performance over traditional expert-designed solutions. However, the DRL policies are often represented by black-box neural networks, they lack interpretability, making verification challenging and requiring excessive floating-point computation. This work introduces a novel approach, Programmatic reinforcement learning for Congestion Control (ProCC), designed to autonomously discover a program as a control policy from scratch. Programs in ProCC include branching structures (e.g., if blocks and if-else blocks), conditions and actions. However, directly optimizing such program structures is challenging due to their discrete non-differentiable nature, and the program space grows exponentially as the depth increases. To address this issue, ProCC defines a Domain-Specific Language (DSL) and program transformation rules, enabling the construction of a program search graph where similar programs are closer in proximity. Subsequently, ProCC employs Monte Carlo Tree Search (MCTS) to efficiently explore the discrete space and obtain promising programs. Extensive experiments conducted in multiple simulated environments demonstrate that ProCC is adaptive and consistently performs well under varying network conditions. The learned program's performance surpasses that of state-of-the-art DRL agents, and more importantly, the generated policies are concise, transparent, and computationally efficient.
MCRanker: Generating Diverse Criteria On-the-Fly to Improve Pointwise LLM Rankers
Authors: Fang Guo, Wenyu Li, Honglei Zhuang, Yun Luo, Yafu Li, Le Yan, Qi Zhu, Yue Zhang
Venue: WSDM
Year: 2025

The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results. However, these rankers are hindered by two major drawbacks: (1) they fail to follow a standardized comparison guidance during the ranking process, and (2) they struggle with comprehensive considerations when dealing with diverse semantics of the query and complicated info in the passages. To address these shortcomings, we propose to build a zero-shot pointwise ranker that first recruits a virtual annotation team to generate query-based criteria from various perspectives and then uses these criteria to conduct an ensemble passage evaluation. Additionally, we are among the first to explore how criteria can be generated automatically and used in text ranking tasks. Our method, tested on eight datasets from the BEIR benchmark, demonstrates that incorporating this multi-perspective criteria ensemble approach significantly enhanced the performance of pointwise LLM rankers.
Self-supervised Time-aware Heterogeneous Hypergraph Learning for Dynamic Graph-level Classification
Authors: Malik Khizar Hayat, Shan Xue, Jia Wu, Bilal Khan, Jian Yang
Venue: WSDM
Year: 2025

Graph-level learning has gained significant importance in understanding complex systems, such as social and biological networks, but often fails to capture evolving and multi-entity interaction. Current dynamic graph-level classification methods struggle with evolving structures, global properties, and high-order interactions. To address these challenges, we propose DyH2GNet, a novel time-aware self-supervised heterogeneous hypergraph neural network that models complex, non-pairwise interactions and their temporal dynamics by integrating k-hop nodes and attribute correlations into a heterogeneous hypergraph. It features a temporal embedding method that captures high-order proximity and the dynamic context of heterogeneous interactions through intra- and inter-snapshot attention. Furthermore, a graph-level pooling layer aggregates node features with temporal context, supported by self-supervised learning that leverages temporal contrastive learning to maximize the use of unlabeled data. Experiments on real-world datasets demonstrated significant improvements in capturing dynamic graph-level features through unsupervised graph-level tasks, including graph similarity ranking, anomaly detection, and trend analysis.
Demystify Epidemic Containment in Directed Networks: Theory and Algorithms
Authors: Yinhan He, Chen Chen, Song Wang, Guanghui Min, Jundong Li
Venue: WSDM
Year: 2025

Epidemic containment has long been a crucial task in many high-stake application domains, ranging from public health to misinformation dissemination. Existing studies for epidemic containment are primarily focused on undirected networks, assuming that the infection rate is constant throughout the contact network regardless of the strength and direction of contact. However, such an assumption can be unrealistic given the asymmetric nature of the real-world infection process. To tackle the epidemic containment problem in directed networks, simply grafting the methods designed for undirected network can be problematic, as most of the existing methods rely on the orthogonality and Lipschitz continuity in the eigensystem of the underlying contact network, which do not hold for directed networks. In this work, we derive a theoretical analysis on the general epidemic threshold condition for directed networks and show that such threshold condition can be used as an optimization objective to control the spread of the disease. Based on the epidemic threshold, we propose an asymptotically greedy algorithm DINO (DIrected NetwOrk epidemic containment) to identify the most critical nodes for epidemic containment. The proposed algorithm is evaluated on real-world directed networks, and the results validate its effectiveness and efficiency. The code is available at https://github.com/YinhanHe123/DINO/.