OpenRaiser/Intern-Atlas huggingface dataset:
How Does Memorization Impact LLMs' Social Reasoning? An Assessment using Seen and Unseen Queries
Authors: Maryam Amirizaniani, Maryna Sivachenko, Adrian Lavergne, Chirag Shah, Afra Mashhadi
Venue: WSDM
Year: 2025

As Large Language Models (LLMs) have rapidly advanced in social reasoning tasks, their applications have expanded to domains such as healthcare and psychology. Given the direct interaction of users with these applications, it is essential to evaluate the performance of LLMs, particularly in human-like social reasoning capabilities. While previous studies have explored human-aligned social reasoning in LLMs, they have not adequately assessed whether the generated reasoning answers stem from the LLMs' memorization of training data or their natural language understanding. In this study, we aim to address this gap by assessing the impact of training data memorization on the human-aligned social reasoning capabilities of LLMs. We introduce IR+CoT (Information Retrieval (IR) + Chain of Thought (CoT)), a framework that leverages retrieved information from input questions to fine-tune prompt templates and employs CoT methods. IR+CoT mitigates the effects of memorization and enhances the LLMs' social reasoning performance. Experiments on three LLMs, using seen (present during the training of the LLMs) and unseen (introduced post-training) questions from Reddit and Lemmy, show that IR+CoT enhances social reasoning and reduces memorization effects. This research's novelty lies in using old and new questions to assess memorization's impact on social reasoning.
Query Performance Prediction: Theory, Techniques and Applications
Authors: Negar Arabzadeh, Chuan Meng, Mohammad Aliannejadi, Ebrahim Bagheri
Venue: WSDM
Year: 2025

Query performance prediction (QPP) is a key task in information retrieval (IR), focusing on estimating the retrieval quality of a given query without relying on human-labeled relevance judgments. Over the decades, QPP has gained increasing significance, with a surge in research activity in recent years. It has proven to benefit various aspects of retrieval, such as optimizing retrieval effectiveness by selecting the most appropriate ranking function for each query. Despite its critical role, there were only a few tutorials that cover the QPP techniques. The topic is even playing a more important role in the new era of pre-trained and large language models (LLMs), and the emerging fields of multi-agent intelligent systems and conversational search (CS ). Moreover, while research in QPP has yielded promising outcomes, studies on its practical application and integration into real-world search engines remain limited. This tutorial has four main objectives. First, it aims to cover both the fundamentals and the latest advancements in QPP methods. Second, it broadens the scope of QPP beyond ad-hoc search to various search scenarios, e.g., CS and image search. Third, this tutorial provides a comprehensive review of QPP applications across various aspects of IR, providing insights on where and how to apply QPP in practice. Fourth, we equip participants with hands-on materials, enabling them to apply QPP implementation in practice. This tutorial seeks to benefit both researchers and practitioners in IR, encouraging further exploration and innovation in QPP.
BAKER: Bayesian Kernel Uncertainty in Domain-Specific Document Modelling
Authors: Ubaid Azam, Imran Razzak, Shelly Vishwakarma, Hakim Hacid, Dell Zhang, Shoaib Jameel
Venue: WSDM
Year: 2025

In critical domains such as healthcare and law, accurately modelling the uncertainty of automatic computational models is essential. For instance, healthcare models must produce reliable estimates to guide human decision-making. However, modelling uncertainty remains challenging, particularly for models handling low-resource datasets and complex, domain-specific vocabulary. Most existing predictive models model point estimates rather than probability distributions, limiting our ability to quantify model uncertainty. This paper introduces a novel model, BAKER, designed to address these limitations. BAKER combines the strengths of Bayesian inference, known for its effectiveness in modelling uncertainty, and kernel methods, which excel at capturing complex data relationships. Incorporating kernel functions enhances model performance, particularly by reducing overfitting in data-limited scenarios. Our experimental analysis shows that BAKER significantly improves uncertainty reasoning compared to existing models.
Adaptive Loss-based Curricula for Neural Team Recommendation
Authors: Reza Barzegar, Marco Nikola Kurepa, Hossein Fani
Venue: WSDM
Year: 2025

Neural team recommendation models have excelled at recommending collaborative teams of experts who, more likely than not, can solve complex tasks. Yet, they suffer from popularity bias due to the disproportionate distribution of popular experts over many teams and the sparse long-tailed distribution of non-popular ones in training datasets, overlooking the difficulty of recommending hard non-popular vs. easy popular experts. To bridge the gap, we propose three curriculum-based learning strategies to empower neural team recommenders sifting through easy popular and hard non-popular experts and to mitigate popularity bias and improve upon them. We propose (1) a parametric curriculum that assigns a learnable parameter to each expert enabling the model to learn an expert's levels of difficulty (or conversely, levels of popularity) during training, (2) a parameter-free (non-parametric) curriculum that presumes the worst-case difficulty for each expert based on the model's loss, and (3) a static curriculum to provide a minimum base for comparison amongst curriculum-based learning strategies and lack thereof. Our experiments on two benchmark datasets with distinct distributions of teams over skills showed that our parameter-free curriculum improved the performance of non-variational models across different domains, outperforming its parametric counterpart, and the static curriculum was the poorest. Moreover, among neural models, variational models obtain little to no gain from our proposed curricula, urging further research on more effective curricula for them. The code to reproduce our experiments is publically available at https://github.com/fani-lab/OpeNTF/tree/cl-wsdm25.
Using Photon-Counting CT Images for Lung Nodule Classification
Authors: Leonie Basso, Zahra Ahmadi, Steffen Oeltze-Jafra, Eike Petersen, Hoen-oh Shin, Andrea Schenk
Venue: WSDM
Year: 2025

An automatic classification of the malignancy of lung nodules in computed tomography (CT) scans can support early detection of lung cancer, which is crucial for the treatment success. The novel photon-counting CT (PCCT) technology enables high image quality with a low radiation dose and provides additional spectral information. This research focuses on whether PCCT scans offer a benefit in the automatic classification of lung nodules. Establishing a dataset of PCCT images poses several challenges, such as the extraction of annotations or the data imbalance.
A Sublinear Algorithm for Approximate Shortest Paths in Large Networks
Authors: Sabyasachi Basu, Nadia Koshima, Talya Eden, Omri Ben-Eliezer, C. Seshadhri
Venue: WSDM
Year: 2025

Computing distances and finding shortest paths in massive real-world networks is a fundamental algorithmic task in network analysis. There are two main approaches to solving this task. On one end are traversal-based algorithms like bidirectional breadth-first search (BiBFS), which have no preprocessing step but are slow on individual distance inquiries. On the other end are indexing-based approaches, which create and maintain a large index. This allows for answering individual inquiries very fast; however, index creation is prohibitively expensive. We seek to bridge these two extremes: quickly answer distance inquiries without the need for costly preprocessing. We propose a new algorithm and data structure, WormHole, for approximate shortest path computations. WormHole leverages structural properties of social networks to build a sublinearly sized index, drawing upon the core-periphery decomposition of Ben-Eliezer et al. [10]. Empirically, WormHole's preprocessing time improves upon index-based solutions by orders of magnitude: indexing billion edges graphs takes only a few minutes. Real time performance is consistently much faster than in BiBFS. The acceleration comes at the cost of a minor accuracy trade-off. We complement these empirical results with provable theoretical guarantees.
CRS Arena: Crowdsourced Benchmarking of Conversational Recommender Systems
Authors: Nolwenn Bernard, Hideaki Joko, Faegheh Hasibi, Krisztian Balog
Venue: WSDM
Year: 2025

We introduce CRS Arena, a research platform for scalable benchmarking of Conversational Recommender Systems (CRS) based on human feedback. The platform displays pairwise battles between anonymous conversational recommender systems, where users interact with the systems one after the other before declaring either a winner or a draw. CRS Arena collects conversations and user feedback, providing a foundation for reliable evaluation and ranking of CRSs. We conduct experiments with CRS Arena on both open and closed crowdsourcing platforms, confirming that both setups produce highly correlated rankings of CRSs and conversations with similar characteristics. We release CRSArena-Dial, a dataset of 474 conversations and their corresponding user feedback, along with a preliminary ranking of the systems based on the Elo rating system. The platform is accessible at https://iai-group-crsarena.hf.space/.
Beyond Relevance: A Demand Balancer Model for Rental Platforms with Single-Unit Inventory
Authors: Guilherme G. Bonaldo, Pedro F. Nogueira, Tetiana Torovets, Thays F. da Silva
Venue: WSDM
Year: 2025

House rental marketplace platforms face unique challenges due to their single-unit inventory nature, where each property is distinct and can only be rented to one tenant. Traditional ranking systems typically optimize for user-item fit [1--5]. For the rental market, this optimization creates demand bottlenecks by continuously directing users to already popular properties. This approach results in high-competition scenarios where multiple users are drawn to the same high-relevance properties, generating excessive competing offers. This can lead to user frustration, as only one individual can ultimately secure the property, leaving others dissatisfied despite their high compatibility with the listing. At the same time, given the nature of property technology businesses being inherently supply-constrained, demand concentration negatively impacts landlords as well, creating challenges for those struggling to rent out their properties, facing longer vacancy periods and increasing the risk of renting elsewhere. To redistribute demand across our house rental marketplace, our solution incorporates the likelihood of successful conversion based on historical user-house interaction signals, including visits, offers, and others. By dynamically adjusting property visibility based on predicted rental probability, we effectively redistribute user attention to low demand properties while minimizing losses in relevance. The implementation of this system in a large-scale rental marketplace through an online controlled experiment resulted in 4% increase in unique houses receiving offers from users and a 3% improvement in contract conversion rates. These results suggest that incorporating availability predictions into ranking systems can lead to more efficient marketplace dynamics while maintaining user satisfaction, without impacting user engagement. Our approach provides a framework for balancing demand in marketplaces with unique inventory constraints.
Compliant Personalization for Recommended Documents in Microsoft 365 with L-Profile as an Exemplary Feature
Authors: Matthias Braunhofer, Grzegorz Kukla, Abhishek Arun
Venue: WSDM
Year: 2025

Collecting and utilizing user data is essential for effective recommender systems to personalize content. However, privacy and compliance regulations protect personal user data. With strict regulations such as the General Data Protection Regulation (GDPR) or California Privacy Rights Act (CPRA) in effect, one may ask: how can a recommender system be both compliant and effective? This paper aims to answer this question, demonstrating privacy-compliant personalization for the Recommended Documents service within Microsoft 365 (M365), particularly Microsoft Feed. It outlines the development of an exemplary L-Profile personalization feature from conception to productionization, covering offline and online evaluations.
Advances in Vector Search
Authors: Sebastian Bruch
Venue: WSDM
Year: 2025

Whether a text document is freed from the rules of grammar, stripped of word order, and thereby turned into a bag of words, or whether its semantic nuances learnt and condensed into an embedding space, its final representation is the same mathematical object: a vector. In fact, vectors represent much more than just text documents. Any object, be it a document or query, that contains text, images, speech, or a mix of these modalities, is often represented as a vector. Collect a large enough quantity of these vectors and the fundamental question of retrieval from the Information Retrieval (IR) discipline becomes urgently relevant: Finding k vectors that are more similar to a query. This full-day tutorial is concerned with the question above and intends to cover foundational concepts and advanced algorithms for vector retrieval or vector search. The tutorial begins with a focus on foundational concepts, including a brief history from space partitioning, to locality-sensitive hashing, graph-based, and clustering-based methods. As we discuss each class of solutions, we show failure scenarios and explain why they prove insufficient. We conclude the tutorial by turning our attention in the second half to recent developments for maximum inner product search over dense and sparse vectors, as well as open questions that need further research. Through this tutorial, we wish to recap the fascinating topic of retrieval in modern IR for the community, lower barriers of entry into this rich area of research, and inspire interest in conducting research on the underlying theoretical and empirical questions that are specific to IR.
Q-DISCO: Query-Centric Densest Subgraphs in Networks with Opinion Information
Authors: Tianyi Chen, Atsushi Miyauchi, Charalampos E. Tsourakakis
Venue: WSDM
Year: 2025

Given a network G=(V,E), where each node v is associated with a vector 𝒩 𝔿 ∈ ℝ d representing its opinion about d different topics, how can we uncover subsets of nodes that not only exhibit exceptionally high density but also possess positively aligned opinions on multiple topics? In this paper we focus on this novel algorithmic question, that is essential in an era where digital social networks are hotbeds of opinion formation and dissemination. We introduce a novel methodology anchored in the well-established densest subgraph problem. We analyze the computational complexity of our formulation, indicating that our problem is NP-hard and eludes practically acceptable approximation guarantees. To navigate these challenges, we design two heuristic algorithms: the first is predicated on the Lagrangian relaxation of our formulation, while the second adopts a peeling algorithm based on the dual of a Linear Programming relaxation. We elucidate the theoretical underpinnings of their performance and validate their utility through empirical evaluation on real-world datasets. Among others, we delve into Twitter datasets we collected concerning timely issues, such as the Ukraine conflict and the discourse surrounding COVID-19 mRNA vaccines, to gauge the effectiveness of our methodology. Our empirical investigations verify that our algorithms are able to extract valuable insights from networks with opinion information.
Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
Authors: Jiazhen Chen, Mingbin Feng, Tony S. Wirjanto
Venue: WSDM
Year: 2025

Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications. Recent advances in multivariate time series anomaly detection (TSAD) have increasingly leveraged graph structures to model inter-variable relationships, typically employing Graph Neural Networks (GNNs). Despite their promising results, existing methods often rely on a single graph representation, which are insufficient for capturing the complex, diverse relationships inherent in multivariate time series. To address this, we propose the Prospective Multi-Graph Cohesion (PMGC) framework for multivariate TSAD. PMGC exploits spatial correlations by integrating a long-term static graph with a series of short-term instance-wise dynamic graphs, regulated through a graph cohesion loss function. Our theoretical analysis shows that this loss function promotes diversity among dynamic graphs while aligning them with the stable long-term relationships encapsulated by the static graph. Additionally, we introduce a "prospective graphing" strategy to mitigate the limitations of traditional forecasting-based TSAD methods, which often struggle with unpredictable future variations. This strategy allows the model to accurately reflect concurrent inter-series relationships under normal conditions, thereby enhancing anomaly detection efficacy. Empirical evaluations on real-world datasets demonstrate the superior performance of our method compared to existing TSAD techniques.
MedTransTab: Advancing Medical Cross-Table Tabular Data Generation
Authors: Yuyan Chen, Qingpei Guo, Shuangjie You, Zhixu Li
Venue: WSDM
Year: 2025

In medical research, clinical trials are pivotal. While prospective clinical research provides a systematic approach to collecting patient data, it grapples with challenges like long durations, increased costs, and most crucially, data scarcity. To address above-mentioned challenge, this paper introduces a novel approach: using cross-table generation to create relevant data. Unlike existing work focused on single-table operations, our method leverages data from multiple sources across various tables, integrating diverse data types and ensuring data consistency across multiple tables. We develop a new framework, MedTransTab, tailored for cross-table tabular data generation in the medical context. This framework extends our previous efforts and is built upon the newly constructed PMC-Struct, derived from an unstructured PMC-patient dataset. Our MedTransTab can generate high-quality patient records, synthesizing detailed biomedical information to align with real or simulated tables from multiple sources. The experiments show that the proposed method significantly improves performance in cross-table tasks. On the PMC-Struct-Plus dataset, we observe an average improvement of 28.85% in data generation and prediction. Similarly, on the Out-Of-Domain (OOD) dataset, there's an average improvement of 22.56%, indicating substantial progress in medical data analysis.
LightGNN: Simple Graph Neural Network for Recommendation
Authors: Guoxuan Chen, Lianghao Xia, Chao Huang
Venue: WSDM
Year: 2025

Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation through their ability to conduct high-order representation smoothing, effectively capturing structural information within users' interaction patterns. However, existing GNN paradigms face significant challenges in scalability and robustness when handling large-scale, noisy real-world datasets. To address these challenges, we present LightGNN, a lightweight and distillation-based GNN pruning framework designed to substantially reduce model complexity while preserving essential collaboration modeling capabilities. Our LightGNN framework introduces a computationally efficient pruning module that adaptively identifies and removes adverse edges and embedding entries for model compression. The framework is guided by a resource-friendly hierarchical knowledge distillation objective, whose intermediate layer augments the observed graph to maintain performance, particularly in high-rate compression scenarios. Extensive experiments on public datasets demonstrate LightGNN's effectiveness, significantly improving both computational efficiency and recommendation accuracy. Notably, LightGNN achieves an 80% reduction in edge count and 90% reduction in embedding entries while maintaining performance comparable to more complex state-of-the-art baselines. The implementation of our LightGNN model is available at the github repository: https://github.com/HKUDS/LightGNN.
Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Authors: Mingyue Cheng, Xiaoyu Tao, Qi Liu, Hao Zhang, Yiheng Chen, Defu Lian
Venue: WSDM
Year: 2025

Pre-training universal models across multiple domains to enhance downstream tasks is a prevalent learning paradigm. However, there has been minimal progress in pre-training transferable models across domains for time series representation. This dilemma is incurred by two key factors: the limited availability of training set within each domain and the substantial differences in data characteristics between domains. To address these challenges, we present a novel framework, namely CrossTimeNet, designed to perform cross-domain self-supervised pre-training to benefit target tasks. Specifically, to address the issue of data scarcity, we utilize a pre-trained language model as the backbone network to effectively capture the sequence dependencies of the input time series. Meanwhile, we adopt the recovery of corrupted region inputs as a self-supervised optimization objective, taking into account the locality of the time series. To address discrepancies in data characteristics, we introduce a novel tokenization module that converts continuous time series inputs into discrete token sequences using vector quantization techniques. This approach facilitates the learning of transferable time series models across different domains. Extensive experimental results on diverse time series tasks, including classification and forecasting, demonstrate the effectiveness of our approach. Our codes are publicly available at https://github.com/Mingyue-Cheng/CrossTimeNet.
Edge-Centric Network Analytics
Authors: Xueqi Cheng
Venue: WSDM
Year: 2025

Network analysis has evolved substantially, with notable advancements in node-centric and graph-centric tasks, yet the exploration of edge-centric analytics has been notably limited. This oversight is significant given the crucial role of edges in elucidating the complex relationships within networks, particularly in fields such as social network analysis, cybersecurity, and bioinformatics, where the dynamics of connections between entities are often pivotal. My doctoral research aims to address this gap by delving into the under-explored domain of edge-centric analytics, providing a foundational background that is crucial for advancing the field and enhancing the application of network theory in real-world scenarios. The significance of this research lies in its potential to open new avenues for inquiry and application across diverse disciplines where understanding the nuances of relational dynamics is essential.