OpenRaiser/Intern-Atlas huggingface dataset:
Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation
Authors: Rongqing Kenneth Ong, Andy W. H. Khong
Venue: WSDM
Year: 2025

Incorporating multi-modal features as side information has recently become a trend in recommender systems. To elucidate user-item preferences, recent studies focus on fusing modalities via concatenation, element-wise sum, or attention mechanisms. Despite having notable success, existing approaches do not account for the modality-specific noise encapsulated within each modality. As a result, direct fusion of modalities will lead to the amplification of cross-modality noise. Moreover, the variation of noise that is unique within each modality results in noise alleviation and fusion being more challenging. In this work, we propose a new Spectrum-based Modality Representation (SMORE) fusion graph recommender that aims to capture both uni-modal and fusion preferences while simultaneously suppressing modality noise. Specifically, SMORE projects the multi-modal features into the frequency domain and leverages the spectral space for fusion. To reduce dynamic contamination that is unique to each modality, we introduce a filter to attenuate and suppress the modality noise adaptively while capturing the universal modality patterns effectively. Furthermore, we explore the item latent structures by designing a new multi-modal graph learning module to capture associative semantic correlations and universal fusion patterns among similar items. Finally, we formulate a new modality-aware preference module, which infuses behavioral features and balances the uni- and multi-modal features for precise preference modeling. This empowers SMORE with the ability to infer both user modality-specific and fusion preferences more accurately. Experiments on three real-world datasets show the efficacy of our proposed model. The source code for this work has been made publicly available at https://github.com/kennethorq/SMORE.
Combating Heterogeneous Model Biases in Recommendations via Boosting
Authors: Jinhao Pan, James Caverlee, Ziwei Zhu
Venue: WSDM
Year: 2025

Collaborative Filtering (CF) based recommenders often exhibit model biases, delivering strong recommendation utility to certain users or items at the expense of others. Prior research approaches these biases as isolated and standalone issues, ignoring their interconnected nature and developing separate methods, thereby compromising the specialized debiasing efforts. Thus, we introduce a boosting-based framework designed to alleviate a broad spectrum of biases. This framework employs a series of sub-models, each tailored for different user and item subgroups. Theoretically, our model ensures an exponentially decreasing upper bound on the training loss across all user and item types with increasing boosting iterations. Extensive experiments demonstrate its superior debiasing capabilities against state-of-the-art methods across four model bias types. Appendix, data and code are available at https://github.com/JP-25/CFBoost
Progressive Tasks Guided Multi-Source Network for Customer Lifetime Value Prediction in Online Advertising
Authors: Zheng Pan, Xingyu Lou, Xiao Jin, Chiye Ou, Feng Liu, Tieyong Zeng, Chengwei He, Xiang Liu, Lilong Wei, Jun Wang
Venue: WSDM
Year: 2025

Customer lifetime value (LTV) is crucial to companies who are intending to adopt personalized promoting strategies to optimize the profits. However, LTV prediction in the scenario of online App advertising usually suffers from label sparsity issue, towards which existing methods designed complex model structures but ignored the information contained in intermediate user behaviors. Moreover, previous works mainly focus on fitting the overall LTV distribution, overlooking the fact that LTV in online App advertising consists of sources with diverse data distributions and thus resulting in sub-optimal solutions. In this paper, we propose a novel Progressive Tasks guided Multi-Source Network (PTMSN) to tackle the aforementioned problems. Specifically, a Cascaded Sub-task Module (CSM) is introduced to alleviate data sparsity by modeling reliance between explicit interactions and implicit monetization. In addition, as the overall LTV is assembled from multiple sources, we propose a divide-and-conquer scheme named Multi-source Integrating Module (MIM) to disentangle the original single target into several source distributions and model in a fine-grained manner. Extensive offline experiments on real-world industrial datasets compared to state-of-the-art baseline models validate the effectiveness of our approach. PTMSN has been successfully deployed in industrial online advertising system, serving various business scenarios and acquiring 2.97% absolute ROI gains.
CIMAGE: Exploiting the Conditional Independence in Masked Graph Auto-encoders
Authors: Jongwon Park, Heesoo Jung, Hogun Park
Venue: WSDM
Year: 2025

Recent Self-Supervised Learning (SSL) methods encapsulating relational information via masking in Graph Neural Networks (GNNs) have shown promising performance. However, most existing approaches rely on random masking strategies in either feature or graph space, which may fail to capture task-relevant information fully. We posit that this limitation stems from an inability to achieve minimum redundancy between masked and unmasked components while ensuring maximum relevance of both to potential downstream tasks. Conditional Independence (CI) inherently satisfies the minimum redundancy and maximum relevance criteria, but its application typically requires access to downstream labels. To address this challenge, we introduce CIMAGE, a novel approach that leverages Conditional Independence to guide an effective masking strategy within the latent space. CIMAGE utilizes CI-aware latent factor decomposition to generate two distinct contexts, leveraging high- confidence pseudo-labels derived from unsupervised graph clustering. In this framework, the pretext task involves reconstructing the masked second context solely from the information provided by the first context. Our theoretical analysis further supports the superiority of CIMAGE's novel CI-aware masking method by demonstrating that the learned embedding exhibits approximate linear separability, which enables accurate predictions for the downstream task. Comprehensive evaluations across diverse graph benchmark illustrate the advantage of CIMAGE, with notably higher average rankings on node classification and link prediction tasks. Notably, our proposed model highlights the under-explored potential of CI in enhancing graph SSL methodologies and offers enriched insights for effective graph representation learning.
Temporal Linear Item-Item Model for Sequential Recommendation
Authors: Seongmin Park, Mincheol Yoon, Minjin Choi, Jongwuk Lee
Venue: WSDM
Year: 2025

In sequential recommendation (SR), neural models have been actively explored due to their remarkable performance, but they suffer from inefficiency inherent to their complexity. Linear SR models exhibit high efficiency and achieve competitive or superior accuracy compared to neural models. However, they solely deal with the sequential order of items (i.e., sequential information) and overlook the actual timestamp (i.e., temporal information). It is limited to effectively capturing various user preference drifts over time. To address this issue, we propose a novel linear SR model, named TemporAl LinEar item-item model (TALE), incorporating temporal information while preserving training/inference efficiency. It consists of three key components. (i) Single-target augmentation concentrates on a single target item, enabling us to learn the temporal correlation for the target item. (ii) Time interval-aware weighting utilizes the actual timestamp to discern the item correlation depending on time intervals. (iii) Trend-aware normalization reflects the dynamic shift of item popularity over time. Our empirical studies show that TALE outperforms ten competing SR models by up to 18.71% gains across five benchmark datasets. It also exhibits remarkable effectiveness for evaluating long-tail items by up to 30.45% gains. The source code is available at https://github.com/psm1206/TALE.
Polaris: Sampling from the Multigraph Configuration Model with Prescribed Color Assortativity
Authors: Giulia Preti, Matteo Riondato, Aristides Gionis, Gianmarco De Francisci Morales
Venue: WSDM
Year: 2025

We introduce Polaris, a network null model for colored multi-graphs that preserves the Joint Color Matrix. Polaris is specifically designed for studying network polarization, where vertices belong to a side in a debate or a partisan group, represented by a vertex color, and relations have different strengths, represented by an integer-valued edge multiplicity. The key feature of Polaris is preserving the Joint Color Matrix (JCM) of the multigraph, which specifies the number of edges connecting vertices of any two given colors. The JCM is the basic property that determines color assortativity, a fundamental aspect in studying homophily and segregation in polarized networks. By using Polaris, network scientists can test whether a phenomenon is entirely explained by the JCM of the observed network or whether other phenomena might be at play. Technically, our null model is an extension of the configuration model: an ensemble of colored multigraphs characterized by the same degree sequence and the same JCM. To sample from this ensemble, we develop a suite of Markov Chain Monte Carlo algorithms, collectively named Polaris-C. It includes Polaris-B, an adaptation of a generic Metropolis-Hastings algorithm, and Polaris-C, a faster, specialized algorithm with higher acceptance probabilities. This new null model and the associated algorithms provide a more nuanced toolset for examining polarization in social networks, thus enabling statistically sound conclusions.
Enhancing Medical Knowledge Discovery: A Neuro-symbolic System for Inductive Learning over Medical KGs
Authors: Disha Purohit, Yashrajsinh Chudasama, Maria-Esther Vidal
Venue: WSDM
Year: 2025

Medical knowledge graphs (KGs) excel at integrating heterogeneous healthcare data with domain knowledge, but face challenges due to incompleteness. While Knowledge Graph Embedding (KGE) models show promise in link prediction, they often fail to incorporate crucial semantic constraints from medical ontologies and clinical guidelines. We propose a neuro-symbolic system that enhances medical knowledge discovery by combining symbolic learning from medical ontologies, inductive learning through KGE, and semantic constraint validation. Applied to lung cancer care, our system demonstrates enhanced performance in predicting novel medical relationships while maintaining semantic consistency with medical knowledge. Experimental results show our approach enhances the KGE model's performance while ensuring clinical validity and the implementation is publicly accessible on GitHub https://github.com/SDM-TIB/KOSMOS.
Personalization At Doordash: From Conversion Modeling To Multi-objective Long-term Value Optimization
Authors: Qilin Qi
Venue: WSDM
Year: 2025

Doordash is one of the largest platform in the world to connect millions of local business with customers. We use advanced machine learning technologies to build a personalized customer experience and help customers discover a variant of local businesses they love. In this talk, we will introduce a few technologies we used to build our personalized homepage experience and the lessons learned during the process. Customers use our platform in different ways, they can browse on homepage, search on search bar or respond to a push notification or an email sent to them. There are also different types of actions they can take during their shopping journeys, included but not limited to views, (good) clicks, add-to-cart, and checkout. We will first introduce how we leverage customers various action sequence and transformer to build our user interest model to understand customer interests. Doordash homepage has a very vivid design containing different components and complex layout to serve our customers. The stores are organized with themes into an UI component that we call carousel. The stores, carousels and other UI components are mixed on our homepage to showcase a diverse set of options and deals customers can choose from. The complex homepage design poses challenges for homepage ranking. We build a heterogeneous ranking system to rank different type of components in a 2-D layout. Traditionally, our ranking model is optimized for conversion. However, as our business grows, we have multiple business objectives to care about. In the meanwhile, we also want to optimize for customers long term satisfaction so we can sustain and grow our platform. We will describe how do we model customers long term value and build a multi-objective ranking and optimization system to optimize and balance multiple business objectives.
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
Authors: Yiran Qiao, Xiang Ao, Yang Liu, Jiarong Xu, Xiaoqian Sun, Qing He
Venue: WSDM
Year: 2025

Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the superior performance of GNNs on different graphs. In this paper, we aim to streamline the GNN design process and leverage the advantages of Large Language Models (LLMs) to improve the performance of GNNs on downstream tasks. We formulate a new paradigm, coined "LLMs-as-Consultants", which integrates LLMs with GNNs in an interactive manner. A framework named LOGIN (LLM cOnsulted GNN traINing) is instantiated, empowering the interactive utilization of LLMs within the GNN training process. First, we attentively craft concise prompts for spotted nodes, carrying comprehensive semantic and topological information, and serving as input to LLMs. Second, we refine GNNs by devising a complementary coping mechanism that utilizes the responses from LLMs, depending on their correctness. We empirically evaluate the effectiveness of Lalebox1 [0.8]O Galebox1 [0.8]IN on node classification tasks across both homophilic and heterophilic graphs. The results illustrate that even basic GNN architectures, when employed within the proposed LLMs-as-Consultants paradigm, can achieve comparable performance to advanced GNNs with intricate designs. Our code is available at https://github.com/QiaoYRan/LOGIN.
Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
Authors: Jiawen Qin, Pengfeng Huang, Qingyun Sun, Cheng Ji, Xingcheng Fu, Jianxin Li
Venue: WSDM
Year: 2025

Graph is a prevalent data structure employed to represent the relationships between entities, frequently serving as a tool to depict and simulate numerous systems, such as molecules and social networks. However, real-world graphs usually suffer from the size-imbalanced problem in the multi-graph classification, i.e., a long-tailed distribution with respect to the number of nodes. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would compromise model performance under the long-tailed settings. We investigate this phenomenon and discover that the long-tailed graph distribution greatly exacerbates the discrepancies in structural features. To alleviate this problem, we propose a novel energy-based size-imbalanced learning framework named SIMBA, which smooths the features between head and tail graphs and re-weights them based on the energy propagation. Specifically, we construct a higher-level graph abstraction named Graphs-to-Graph according to the correlations between graphs to link independent graphs and smooths the structural discrepancies. We further devise an energy-based message-passing belief propagation method for re-weighting lower compatible graphs in the training process and further smooth local feature discrepancies. Extensive experimental results over five public size-imbalanced datasets demonstrate the superior effectiveness of the model for size-imbalanced graph classification tasks.
Neo-TKGC: Enhancing Temporal Knowledge Graph Completion with Integrated Node Weights and Future Information
Authors: Zihan Qiu, Xiaoling Zhou, Chunyan An, Qiang Yang, Zhixu Li
Venue: WSDM
Year: 2025

Temporal Knowledge Graph Completion (TKGC) involves predicting and filling in missing facts within time series data, a crucial task with wide-ranging applications across various domains. The dynamic evolution of Temporal Knowledge Graphs (TKGs) adds complexity to this task, making it inherently challenging. Existing research predominantly relies on historical data to complete the missing facts. However, these approaches often overlook the potential of future information and the significance of node weights.To address these challenges, we propose Neo-TKGC, a novel temporal knowledge graph completion model that integrates a graph structure encoding module and a temporal encoding module. The graph structure encoding module introduces node weights to enhance the capabilities of graph neural networks (GNNs) for entity and relation representation learning, implemented using CompGCN. This module can be easily extended to any GNN models utilizing node and edge aggregation. The temporal encoding module leverages both future and historical information to capture relevant contexts and temporal dependencies among entities and relations.By combining node weights and future information, Neo-TKGC achieves more accurate entity and relation representations, thereby improving the model's ability to infer unknown entities. Extensive experiments on three real-world TKGC datasets demonstrate the superior performance of our model compared to existing approaches, achieving at least a 1.7% relative improvement in Hits@1 across most metrics.
IMPO: Interpretable Memory-based Prototypical Pooling
Authors: Alessio Ragno, Roberto Capobianco
Venue: WSDM
Year: 2025

Graph Neural Networks (GNNs) have proven their effectiveness in various graph-structured data applications. However, one of the significant challenges in the realm of GNNs is representation learning, a critical concept that bridges graph pooling, aimed at creating compressed graph representations, and explainable artificial intelligence, which focuses on building models with transparent reasoning mechanisms. This research paper introduces a novel approach called Interpretable Memory-based Prototypical Pooling (IMPO) to address this challenge. IMPO is a graph pooling layer designed to enhance the interpretability of GNNs while maintaining high performance in graph classification tasks. It builds upon the MemPool algorithm and incorporates prototypical components to cluster nodes around class-aware centroids. This approach allows IMPO to selectively aggregate relevant substructures, paving the way for generating more interpretable graph representations. The experimental results in our study underscore the potential of pooling architectures in constructing inherently explainable GNNs. Notably, IMPO achieves state-of-the-art results in both classification and explanatory capacities across a diverse set of graph classification datasets.
LLM4Eval@WSDM 2025: Large Language Model for Evaluation in Information Retrieval
Authors: Hossein A. Rahmani, Clemencia Siro, Mohammad Aliannejadi, Nick Craswell, Charles L. A. Clarke, Guglielmo Faggioli, Bhaskar Mitra, Paul Thomas, Emine Yilmaz
Venue: WSDM
Year: 2025

Large language models (LLMs) have demonstrated increasing task-solving abilities not present in smaller models. Utilizing the capabilities and responsibilities of LLMs for automated evaluation (LLM4Eval) has recently attracted considerable attention in multiple research communities. For instance, LLM4Eval models have been studied in the context of automated judgments, natural language generation, and retrieval augmented generation systems. We believe that the information retrieval community can significantly contribute to this growing research area by designing, implementing, analyzing, and evaluating various aspects of LLMs with applications to LLM4Eval tasks. The main goal of LLM4Eval workshop is to bring together researchers from industry and academia to discuss various aspects of LLMs for evaluation in information retrieval, including automated judgments, retrieval-augmented generation pipeline evaluation, altering human evaluation, robustness, and trustworthiness of LLMs for evaluation in addition to their impact on real-world applications. We also plan to run an automated judgment challenge prior to the workshop, where participants will be asked to generate labels for a given dataset while maximising correlation with human judgments. The format of the workshop is interactive, including roundtable and keynote sessions and tends to avoid the one-sided dialogue of a mini-conference. This is the second iteration of the workshop. The first version was held in conjunction with SIGIR 2024, attracting over 50 participants.
SpecialtyScribe: Enhancing SOAP note Scribing for Medical Specialties using LLMs
Authors: Eti Rastogi, Sagar Goyal, Fen Zhao, Dong Yuan
Venue: WSDM
Year: 2025

The healthcare industry has accumulated vast amounts of unstructured clinical data, including medical records, patient communications, and visit notes. Clinician-patient conversations are central to medical records, with the clinician's final summary (the medical note) serving as the key reference for future interactions and treatments. Creating a concise and accurate medical SOAP note is crucial for quality patient care and is especially challenging for specialty care, which requires added focus on relevance to the specialty, clarity, absence of hallucinations, and adherence to doctor preferences. This makes it very challenging for a general-purpose LLM to create satisfactory notes. Some recent LLMs, like GPT-4, have shown promise in medical note generation; however, the high cost, size, latency, and privacy concerns associated with closed models make them impractical for many healthcare facilities. In this talk, we will present our method ''SpecialtyScribe'', which is a modular pipeline for generating specialty-specific medical notes. It consists of three main components: an Information Extractor module that captures relevant specialty data, a Context Retriever module that retrieves and verifies the relevant context from the transcript, and a Note Writer module that generates medically acceptable notes based on the extracted information. Our framework outperforms any naively prompt-engineered model by more than 32% on expert scoring, and our in-house models surpass similarly sized open-source models by more than 100% on ROUGE based metrics. The in-house models also match the overall performance of the best closed-source LLMs while being less than 1% the estimated size of them. We'll showcase multiple ablations across our pipeline, mitigation of hallucinations, the role of retrievers, and the importance of scalable pipelines for multiple specialties. We'll also discuss the design of our human-expert scoring mechanism for various language model use cases.