OpenRaiser/Intern-Atlas huggingface dataset:
IWILDS'25: The 5th International Workshop on Investigating Learning During Web Search
Authors: Anett Hoppe, Ran Yu, Jiqun Liu, Nilavra Bhattacharya
Venue: WSDM
Year: 2025

Web-based learning is evolving rapidly as traditional search engines are complemented by Large Language Models (LLMs) and other AI technologies. This evolution offers new opportunities, such as automated information synthesis and personalized learning experiences. However, this also presents new challenges, including the need for learners to be aware of potential biases and misinformation in AI-generated content, and to maintain focus and depth in their learning journeys. The Search as Learning (SAL) field investigates how individuals learn through web interactions, examining the interplay between search behaviors, information synthesis, and learning outcomes. SAL research aims to measure, predict, and support effective learning strategies in modern web environments. IWILDS'25 provides an interdisciplinary platform to explore these dynamics, bringing together researchers and practitioners from information retrieval, education, psychology, and related fields. This full-day workshop will feature keynotes, paper presentations, and discussions on advancing SAL research and practice.
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data
Authors: Binbin Hu, Zhicheng An, Zhengwei Wu, Ke Tu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Yufei Feng, Jiawei Chen
Venue: WSDM
Year: 2025

Estimating individual treatment effects (ITE) from observational data is a critical task across various domains. However, many existing works on ITE estimation overlook the influence of hidden confounders, which remain unobserved at the individual unit level. To address this limitation, researchers have utilized graph neural networks to aggregate neighbors' features to capture the hidden confounders and mitigate confounding bias by minimizing the discrepancy of confounder representations between the treated and control groups. Despite the success of these approaches, practical scenarios often treat all features as confounders and involve substantial differences in feature distributions between the treated and control groups. Confusing the adjustment and confounder and enforcing strict balance on the confounder representations could potentially undermine the effectiveness of outcome prediction. To mitigate this issue, we propose a novel framework called the Graph Disentangle Causal model (GDC) to conduct ITE estimation in the network setting. GDC utilizes a causal disentangle module to separate unit features into adjustment and confounder representations. Then we design a graph aggregation module consisting of three distinct graph aggregators to obtain adjustment, confounder, and counterfactual confounder representations. Finally, a causal constraint module is employed to enforce the disentangled representations as true causal factors. The effectiveness of our proposed method is demonstrated by conducting comprehensive experiments on two networked datasets.
Proposal for Workshop on Trust and Responsibility in Recommendation Systems at WSDM 2025
Authors: Xinghai Hu, Robin Burke, Xu Liu
Venue: WSDM
Year: 2025

This workshop aims to empower participants to design and audit recommendation systems that prioritize user trust and safety. Atten- dees will explore best practices and innovations in fairness, explain- ability, content safety, algorithm transparency, and the societal im- pacts of recommendation systems. The workshop will address tech- nical and ethical challenges, offering practical insights into risk mit- igation, social concerns, and technical innovations. Participants will leave equipped with actionable frameworks, methodologies, and tools to build responsible systems aligned with societal values and social good. Key activities include keynotes, paper presentations, panel discussions, and interactive sessions, covering topics such as transparency, security, fairness, robustness, and participatory AI. The website of the conference is https://sites.google.com/view/t-rrs.
Exploring the Explainability of Time Series Clustering: A Review of Methods and Practices
Authors: Zheng Huang, Hao Hao, Lun Du
Venue: WSDM
Year: 2025

With the increasing use of time series data, particularly in critical applications and high-risk decision-making contexts, understanding and improving the explainability of time-series clustering(TSC) techniques is essential. While machine learning models excel in processing time series data, their explainability often needs enhancement, challenging human comprehension and trust. Time series data clustering, as an unsupervised learning method, extracts valuable patterns from complex datasets without prior knowledge, spanning various domains like biology and finance. However, the complexity of clustering models and their opaque decision-making processes raise concerns about understanding and trusting the results. Research in this area aims to enhance the explainability of TSC by developing new interpretation methods that not only ensure the accuracy of clustering results but also make them user-friendly and comprehensible to human users. This is crucial for overcoming challenges related to understanding and trusting the decision-making processes and outcomes of the model. In this study, we embarked on two significant endeavors: (a) We explored the use of explainable artificial intelligence (XAI) for TSC for the first time, conducting a comprehensive literature review. (b) We subdivided the research field through innovative classification methods, categorizing the explainability methods of TSC into three main categories: data preprocessing techniques based on time series data, single or hybrid methods based model training, and instance-based visualization algorithm applications. This analytical framework aims to elucidate how the explainability of TSC can be enhanced across these three dimensions, thereby improving its credibility. Our work not only opens new research avenues but also provides robust strategies for enhancing the explainability and credibility of TSC methods.
HyKG-CF: A Hybrid Approach for Counterfactual Prediction using Domain Knowledge
Authors: Hao Huang, Maria-Esther Vidal
Venue: WSDM
Year: 2025

Predictive models are gaining attention as powerful tools for aiding clinicians in diagnosis, prognosis, and treatment recommendations. However, their reliance on associative patterns may raise concerns about reliability of decision support, as association does not necessarily imply causation. To address this limit, we propose HyKG-CF, a hybrid approach to counterfactual prediction that leverages data and domain knowledge encoded in knowledge graph (KG). HyKG-CF integrates symbolic reasoning (on knowledge) with numerical learning (on data) using large language models (LLMs) and statistical models to learn causal Bayesian networks (CBNs) for accurate counterfactual prediction. Using data and knowledge, HyKG-CF improves the accuracy of causal discovery and counterfactual prediction. We evaluate HyKG-CF on a non-small cell lung cancer (NSCLC) KG, demonstrating that it outperforms other baselines. The results highlight the promise of combining domain knowledge with causal models to improve counterfactual prediction.
Personalised Outfit Recommendation via History-aware Transformers
Authors: Myong Chol Jung, Julien Monteil, Philip Schulz, Volodymyr Vaskovych
Venue: WSDM
Year: 2025

We present the history-aware transformer (HAT), a transformer-based model that uses shoppers' purchase history to personalise outfit predictions. The aim of this work is to recommend outfits that are internally coherent while matching an individual shopper's style and taste. To achieve this, we stack two transformer models, one that produces outfit representations and another one that processes the history of purchased outfits for a given shopper. We use these models to score an outfit's compatibility in the context of a shopper's preferences as inferred from their previous purchases. During training, the model learns to discriminate between purchased and random outfits using 3 losses: the focal loss for outfit compatibility typically used in the literature, a contrastive loss to bring closer learned outfit embeddings from a shopper's history, and an adaptive margin loss to facilitate learning from weak negatives. Together, these losses enable the model to make personalised recommendations based on a shopper's purchase history. Our experiments on the IQON3000 and Polyvore datasets show that HAT outperforms strong baselines on the outfit Compatibility Prediction (CP) and the Fill In The Blank (FITB) tasks. The model improves AUC for the CP hard task by 15.7% (IQON3000) and 19.4% (Polyvore) compared to previous SOTA results. It further improves accuracy on the FITB hard task by 6.5% and 9.7%, respectively. We provide ablation studies on the personalisation, constrastive loss, and adaptive margin loss that highlight the importance of these modelling choices.
Improving Scientific Document Retrieval with Concept Coverage-based Query Set Generation
Authors: SeongKu Kang, Bowen Jin, Wonbin Kweon, Yu Zhang, Dongha Lee, Jiawei Han, Hwanjo Yu
Venue: WSDM
Year: 2025

In specialized fields like the scientific domain, constructing large-scale human-annotated datasets poses a significant challenge due to the need for domain expertise. Recent methods have employed large language models to generate synthetic queries, which serve as proxies for actual user queries. However, they lack control over the content generated, often resulting in incomplete coverage of academic concepts in documents. We introduce Concept Coverage-based Query set Generation (CCQGen) framework, designed to generate a set of queries with comprehensive coverage of the document's concepts. A key distinction of CCQGen is that it adaptively adjusts the generation process based on the previously generated queries. We identify concepts not sufficiently covered by previous queries, and leverage them as conditions for subsequent query generation. This approach guides each new query to complement the previous ones, aiding in a thorough understanding of the document. Extensive experiments demonstrate that CCQGen significantly enhances query quality and retrieval performance.
Sigmoid Similarity in Semantic HCP Networks: An Approach for Context Aware Search and Recommendations
Authors: Pallavi Karanth
Venue: WSDM
Year: 2025

Large datasets in silos are available to be harnessed for potential benefits in terms of insights for better recommendations, search and clustering. Healthcare datasets include health care professionals' (HCP) data about their affiliations, publications, participation in conferences, events, clinical trials, specialty and much more. Such datasets in silos are integrated based on the different dimensions such as specialty, participation in various healthcare related events and clinical trials. Integration of healthcare related datasets provide various benefits like better search results, better recommendations of healthcare professionals based on their specialty and needs of patients and better insights to determine key opinion leaders in various therapy areas. In this work, we apply the Sigmoid similarity algorithm to find similar health care professionals based on the HCP Ontology we have developed. Sigmoid similarity is a feature based semantic similarity measure which outperforms the other hierarchy based approaches for computing semantic similarity. This semantic similarity measure enables us to compare and evaluate the semantic similarity amongst various health care professionals to accurately retrieve and recommend healthcare professionals based on the context of search.
Efficient Creative Selection in Online Advertising using Top-Two Thompson Sampling
Authors: Daiki Katsuragawa, Yusuke Kaneko, Kaito Ariu, Kenshi Abe
Venue: WSDM
Year: 2025

In online advertising, identifying the optimal creative is critical to maximizing performance. This study examines the application of top-two Thompson sampling (TTTS), an adaptive experimental design method, as an efficient alternative to traditional A/B testing for identifying the optimal creative. Our experiments on an online advertising platform highlight the effectiveness of TTTS in both accurately identifying the optimal creative and minimizing experimental costs, underscoring its potential as a promising approach to creative selection.
SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendation
Authors: Chaejeong Lee, Jeongwhan Choi, Hyowon Wi, Sung-Bae Cho, Noseong Park
Venue: WSDM
Year: 2025

Graph-based collaborative filtering (CF) has emerged as a promising approach in recommender systems. Despite its achievements, graph-based CF models face challenges due to data sparsity and negative sampling. In this paper, we propose a novel Stochastic sampling for i) COntrastive views and ii) hard NEgative samples (SCONE) to overcome these issues. SCONE generates dynamic augmented views and diverse hard negative samples via a unified stochastic sampling approach based on score-based generative models. Our extensive experiments on 6 benchmark datasets show that SCONE consistently outperforms state-of-the-art baselines. SCONE shows efficacy in addressing user sparsity and item popularity issues, while enhancing performance for both cold-start users and long-tail items. Furthermore, our approach improves the diversity of the recommendation and the uniformity of the representations. The code is available at https://github.com/jeongwhanchoi/SCONE.
HaGAR: Hardness-aware Generative Adversarial Recommender
Authors: Yuan-Heng Lee, Josh Jia-Ching Ying, Vincent S. Tseng
Venue: WSDM
Year: 2025

Implicit Collaborative filtering is a fundamental technique in recommendation systems, leveraging implicit user interactions to suggest items of interest. A significant challenge in this domain is the absence of explicit negative feedback, limiting the recommendation performance. Previous researchers have tried to tackle the challenge through the Generative Adversarial Network (GAN). The generator produces increasingly challenging samples for the discriminator, driving the optimization of the discrimination objective. Although GAN-style recommender systems can achieve decent performance by generating harder negative samples, the negatives selected by the generator may not always be ideal for training the discriminator. In this study, we focus on two types of undesirable negatives that persist in modern GAN-style recommenders: false negatives and uninformative negatives. In response to these issues, we propose a novel Hardness-aware Generative Adversarial Recommender (HaGAR). To the best of our knowledge, it is the first adversarial recommender that explicitly aims to alleviate the adverse impact of false and uninformative negatives. Our approach incorporates a relevance monitoring module and a hardness-aware weighting module to identify and address false and uninformative negatives during training with minimal additional computational cost. Our experimental results demonstrate that HaGAR significantly improves recommendation performance, achieving over a 21% increase in terms of NDCG@10 compared to the state-of-the-art GAN-style recommender. These findings highlight the efficacy of our improvement in providing more robust negative samples, leading to better-performing recommendation systems.
ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning
Authors: Zhongnian Li, Meng Wei, Peng Ying, Xinzheng Xu
Venue: WSDM
Year: 2025

Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible. In this paper, to alleviate the shifting of minimum risk problem, we propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier. Specifically, we sieve out some examples by utilizing the Certain Loss (CL) value of each example in the training stage and analyze the consistency of the proposed risk estimator. Besides, we show that the estimation error of proposed ESA obtains the optimal parametric convergence rate. Extensive experiments on various real-world datasets show the proposed approach outperforms previous methods.
Density-aware and Cluster-based Federated Anomaly Detection on Data Streams
Authors: Bin Li, Li Cheng, Zheng Qin, Yunlong Wu
Venue: WSDM
Year: 2025

Federated active anomaly detection on data streams becomes a crucial research problem, since it attempts to discover anomalous data with protecting data privacy and avoiding extensive data labeling. Although extensive work has been conducted on anomaly detection, distinguishing similar anomalies of different categories still remains quite a challenging issue. The requirement of privacy protection in federated settings aggravates the difficulties for instance query and scoring in active anomaly detection when solving this issue. To the best of our knowledge, limited work has focused on this research area. Therefore, we propose Density-aware and cluster-based Federated Active anomaly detection on data Streams, called DFAS. We design a novel lightweight federated anomaly detection clusters with density-aware hash cells, which successfully capture evolving data distribution. The federated anomaly detection clusters are incrementally updated with an acceptable theoretical reconstruction error guarantee. In addition, we propose a straightforward but effective metric divergences accompanied by a greedy search algorithm, which takes both global aggregation bias mitigation and efficiency into account. At last, DFAS detects anomalies and queries the instances for manual labels by measuring the density in hash cells of each cluster, effectively distinguishing closely distributed anomaly classes while maintaining data privacy in the federated setting. Comprehensive experiments on several real-world data sets show that DFAS outperforms previous methods, improving F1 scores by up to 26.7%.
Teach Me How to Denoise: A Universal Framework for Denoising Multi-modal Recommender Systems via Guided Calibration
Authors: Hongji Li, Hanwen Du, Youhua Li, Junchen Fu, Chunxiao Li, Ziyi Zhuang, Jiakang Li, Yongxin Ni
Venue: WSDM
Year: 2025

The surge in multimedia content has led to the development of Multi-Modal Recommender Systems (MMRecs), which use diverse modalities-like text, images, videos, and audio-for more personalized recommendations. However, MMRecs struggle with the challenge of noisy data from the misalignment among modal content and the difference between modal and recommendation semantics, while traditional denoising methods fall short in addressing these issues due to the complexity of multi-modal data. To overcome this, we propose a universal guided in-sync distillation denoising framework for multi-modal recommndation (GUIDER), aimed at improving MMRecs by denoising user feedbacks. Specifically, GUIDER employs a re-calibration strategy to identify clean and noisy interactions from modal content. Furthermore, it incorporates a Denoising Bayesian Personalized Ranking (DBPR) loss function to denoise implicit user feedback. Finally, it utilizes a denoising knowledge distillation objective based on Optimal Transport (OT) distance to guide the mapping from modality representations to recommendation semantics spaces. GUIDER can be seamlessly integrated into existing MMRecs methods as a plug-and-play solution for recommendation denoising. Experiment results on four public datasets show its effectiveness and universality across various MMRecs.
HTEA: Heterogeneity-aware Embedding Learning for Temporal Entity Alignment
Authors: Jiayun Li, Wen Hua, Fengmei Jin, Xue Li
Venue: WSDM
Year: 2025

Temporal entity alignment (TEA), which identifies equivalent entities across temporal knowledge graphs (TKGs), plays a vital role in integrating multiple TKGs.Simply adapting traditional EA models to TKGs cannot achieve satisfactory results, driving the need for dedicated studies in TEA. However, existing TEA models often fail to effectively capture the importance of temporal features and the richness of temporal context during embedding learning. Moreover, the challenge of temporal heterogeneity, which is prevalent in real-world TKGs, has not been adequately studied. In this work, we propose a HTEA framework to address these limitations. Specifically, we introduce a frequency-based temporal embedding module that incorporates the importance of temporal features for each entity, along with a temporal attention mechanism that prioritizes more informative context based on temporal richness. We further design an iterative module to detect temporal heterogeneity and refine the related facts accordingly. In this way, entity embeddings can be improved progressively, yielding more accurate and consistent alignment outcomes.Extensive experiments showcase the efficacy of our HTEA model, especially under the existence of temporal heterogeneity in real-world TKGs.
Incomplete Multi-view Clustering via Local Reasoning and Correlation Analysis
Authors: Xiaocui Li, Guoliang Li, Xinyu Zhang, Yangtao Wang, Qingyu Shi, Wei Liang
Venue: WSDM
Year: 2025

In recent years, incomplete multi-view clustering (IMVC) has attracted considerable attention for its ability to acheieve effective clustering results through the integration of key information amidst missing view. However, the existing IMVC methods are still faced with 3 limitations: (1) They exhibit deficiencies in considering the weight distribution within views, (2) they ignore the varying contributions of different views to the common consistent representation, and (3) they struggle to sufficiently extract and recover the vital information within incomplete views. To address these limitations, we incorporates local reasoning and correlation analysis to design an incomplete multi-view clustering method(IMVCLRCA), which introduces a new strategy of feature learning and missing view recovery, fully exploiting local similarity and structural continuity within views and performing precise local reasoning recovery on missing data. By maximizing mutual information between views through contrastive learning, we achieve the consistent representation learning of multiple views. Furthermore, based on semantic consistency, we comprehensively consider the correlation between views, utilized a weight matrix to fuse cross-view data, and constructed a view with a correlation structure, ultimately obtaining a common consistent representation. We conduct extensive experiments on 4 public datasets including Caltech101-20, BBCSport, Scene-15, and LandUse-21. Experimental results demonstrate that IMVCLRCA has higher accuracy and robustness compared to the state-of-the-art IMVC methods. The anonymous code of this project is available on GitHub at https://github.com/ggg2111/2025WSDM-IMVCLRCA.