OpenRaiser/Intern-Atlas huggingface dataset:
RetriEVAL: Evaluating Text Generation with Contextualized Lexical Match
Authors: Zhen Li, Xinchi Li, Chongyang Tao, Jiazhan Feng, Tao Shen, Can Xu, Hao Wang, Dongyan Zhao, Shuai Ma
Venue: WSDM
Year: 2025

Pre-trained language models have made significant advancements in text generation tasks. Nevertheless, evaluating the generated text with automatic metrics is still challenging. Compared with supervised metrics, unsupervised metrics which are known for generality and robustness, are frequently employed to assess the quality of generated text efficiently. The representative unsupervised metric BERTScore uses pretrained embedding to calculate the word-to-word similarity across all tokens as evaluation scores, which can introduce potential noise due to the inclusion of tokens that do not contribute significantly to the semantics of the text. Furthermore, its heavy reliance on dense embeddings may lead to lower accuracy when evaluating text outside the common contexts represented in the training data, making it less effective in handling uncommon linguistic patterns Additionally, BERTScore treats all tokens with equal importance and lacks the ability to perform meaningful contextual expansion, which can result in less accurate similarity measurements, particularly when dealing with paraphrased or semantically rich text. To address this problem, we propose an unsupervised automatic evaluation metric inspired by the concept of lexical match in information retrieval. Our method leverages contextualized lexical matching to measure exact matches between identical tokens and dynamically matches different tokens based on their contextualized representations. Experiments on SummEval and Topical-Chat demonstrate our proposed RetriEVAL can correlate better with human judgments than previous unsupervised metrics.
Inductive Graph Few-shot Class Incremental Learning
Authors: Yayong Li, Peyman Moghadam, Can Peng, Nan Ye, Piotr Koniusz
Venue: WSDM
Year: 2025

Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well studied, while Graph Few-Shot Class Incremental Learning (GFSCIL), which involves learning a GNN classifier as graph nodes and classes growing over time sporadically, has received much less attention despite its importance. We introduce inductive GFSCIL that continually learns novel classes with newly emerging nodes while maintaining performance on old classes without accessing previous data. This addresses the practical concern of transductive GFSCIL, which requires storing the entire graph with historical data. Compared to the transductive GFSCIL, the inductive setting exacerbates catastrophic forgetting due to inaccessible previous data during incremental training, in addition to the overfitting issue caused by label sparsity. Thus, we propose a novel method, called Topology-based class Augmentation and Prototype calibration (TAP). To be specific, it first performs a topology-based class augmentation method, helping replicate the setting of disjoint subgraphs with nodes of novel classes received in incremental sessions, to enhance backbone versatility. In incremental learning, given the limited number of novel class samples, we propose an iterative prototype calibration to improve the separation of class prototypes. Furthermore, as backbone fine-tuning poses the feature distribution drift, prototypes of old classes start failing over time, we propose the prototype shift method for old classes to compensate for the drift. We showcase the proposed method on four datasets.
DiffGraph: Heterogeneous Graph Diffusion Model
Authors: Zongwei Li, Lianghao Xia, Hua Hua, Shijie Zhang, Shuangyang Wang, Chao Huang
Venue: WSDM
Year: 2025

Recent advances in Graph Neural Networks (GNNs) have revolutionized graph-structured data modeling, yet traditional GNNs struggle with complex heterogeneous structures prevalent in real-world scenarios. Despite progress in handling heterogeneous interactions, two fundamental challenges persist: noisy data significantly compromising embedding quality and learning performance, and existing methods' inability to capture intricate semantic transitions among heterogeneous relations, which impacts downstream predictions. To address these fundamental issues, we present the Heterogeneous Graph Diffusion Model (DiffGraph), a pioneering framework that introduces an innovative cross-view denoising strategy. This advanced approach transforms auxiliary heterogeneous data into target semantic spaces, enabling precise distillation of task-relevant information. At its core, DiffGraph features a sophisticated latent heterogeneous graph diffusion mechanism, implementing a novel forward and backward diffusion process for superior noise management. This methodology achieves simultaneous heterogeneous graph denoising and cross-type transition, while significantly simplifying graph generation through its latent-space diffusion capabilities. Through rigorous experimental validation on both public and industrial datasets, we demonstrate that DiffGraph consistently surpasses existing methods in link prediction and node classification tasks, establishing new benchmarks for robustness and efficiency in heterogeneous graph processing. The model implementation is publicly available at: https://github.com/HKUDS/DiffGraph.
Robust Information Retrieval
Authors: Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke
Venue: WSDM
Year: 2025

Beyond effectiveness, the robustness of an information retrieval (IR) system is increasingly attracting attention. When deployed, a critical technology such as IR should not only deliver strong performance on average but also have the ability to handle a variety of exceptional situations. In recent years, research into the robustness of IR has seen significant growth, with numerous researchers offering extensive analyses and proposing myriad strategies to address robustness challenges. In this tutorial, we first provide background information covering the basics and a taxonomy of robustness in IR. Then, we examine adversarial robustness and out-of-distribution (OOD) robustness within IR-specific contexts, extensively reviewing recent progress in methods to enhance robustness. The tutorial concludes with a discussion on the robustness of IR in the context of large language models (LLMs), highlighting ongoing challenges and promising directions for future research. This tutorial aims to generate broader attention to robustness issues in IR, facilitate an understanding of the relevant literature, and lower the barrier to entry for interested researchers and practitioners.
Balancing Revenue and Privacy with Signaling Schemes in Online Ad Auctions
Authors: Hongtao Liu, Luxi Chen, Yiming Ding, Changcheng Li, Han Li, Peng Jiang, Weiran Shen
Venue: WSDM
Year: 2025

In online ad auctions, when an Internet user's certain actions trigger an auction, the auctioneer (the platform) usually sends the information about the user to help the buyers better estimate their valuations. However, by strategically revealing only partial information, we cannot only improve the revenue of the auction, but also help protect the privacy of the user. In this paper, we propose a privacy measure in the online ad auction setting, and seek to maximize a convex combination of revenue and privacy. We formulate the problem as a convex optimization program and derive structural results and properties of the program. We prove that any combination coefficient achieves a certain fraction of the optimal revenue gain and privacy gain, and that we can trade-off between revenue and privacy by simply tuning the combination coefficient. We also show that the gap between the optimal revenue and the revenue achieved by revealing no information can be bounded by a certain valuation discrepancy between the buyers. We also conduct extensive experiments (on both synthetic and real data) to show the effectiveness of our method.
An Aspect Performance-aware Hypergraph Neural Network for Review-based Recommendation
Authors: Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang
Venue: WSDM
Year: 2025

Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, items, aspects, and sentiment polarity by systematically constructing an aspect hypergraph based on user reviews. In addition, APH aggregates aspects representing users and items by employing an aspect performance-aware hypergraph aggregation method. It aggregates the sentiment polarities from multiple users by jointly considering user preferences and the semantics of their sentiments, determining the weights of sentiment polarities to infer the performance of items on various aspects. Such performances are then used as weights to aggregate neighboring aspects. Experiments on six real-world datasets demonstrate that APH improves MSE, Precision@5, and Recall@5 by an average of 2.30%, 4.89%, and 1.60% over the best baseline. The source code and data are available at https://github.com/dianziliu/APH.
Improving CTR Prediction with Graph-Enhanced Interest Networks for Sparse Behavior Sequences
Authors: Xuanzhou Liu, Zhibo Xiao, Luwei Yang, Hansheng Xue, Jianxing Ma, Yujiu Yang
Venue: WSDM
Year: 2025

Predicting click-through rates is crucial in various fields, including online advertising and recommendation systems. The key to improving the performance of CTR prediction lies in learning a robust user representation, particularly by analyzing their historical behaviors. Previous studies usually model behavior sequences through attention-based sequence models or graph-based methods, which usually struggle to explore diverse latent interests or accurately model user behaviors. Moreover, this challenge is exacerbated when users' historical behaviors are sparse, a common issue in real-world business-to-business (B2B) e-commerce scenarios. In this paper, we propose a novel Graph-Enhanced Interest Network (GEIN) to capture users' latent intents and facilitate the sequential learning of sparse behavior sequences. Specifically, we first construct a hierarchical item-intent heterogeneous graph to enrich the representation of sparse behaviors using diverse information from graphs. Next, we build a user-level behavior interest factor graph to accurately capture user interests. Additionally, a contrastive learning mechanism is incorporated to mitigate the negative robustness impacts caused by sparsity. Extensive experiments on real-world datasets demonstrate that our proposed GEIN outperforms a wide range of state-of-the-art methods. Furthermore, online A/B testing also confirms the superiority of GEIN over competing baselines in a real-world production environment.
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation
Authors: Mingrui Liu, Sixiao Zhang, Cheng Long
Venue: WSDM
Year: 2025

Sequential recommendation (SR) systems excel at capturing users' dynamic preferences by leveraging their interaction histories. Most existing SR systems assign a single embedding vector to each item to represent its features, and various types of models are adopted to combine these item embeddings into a sequence representation vector to capture the user intent. However, we argue that this representation alone is insufficient to capture an item's multi-faceted nature (e.g., movie genres, starring actors). Besides, users often exhibit complex and varied preferences within these facets (e.g., liking both action and musical films in the facet of genre), which are challenging to fully represent. To address the issues above, we propose a novel structure called Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation (FAME). We leverage sub-embeddings from each head in the last multi-head attention layer to predict the next item separately. A gating mechanism integrates recommendations from each head and dynamically determines their importance. Furthermore, we introduce a Mixture-of-Experts (MoE) network in each attention head to disentangle various user preferences within each facet. Each expert within the MoE focuses on a specific preference. A learnable router network is adopted to compute the importance weight for each expert and aggregate them. We conduct extensive experiments on four public sequential recommendation datasets and the results demonstrate the effectiveness of our method over existing baseline models.
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks
Authors: Junliang Luo, Xue Liu
Venue: WSDM
Year: 2025

Blockchain technology, with implications in the financial domain, offers data in the form of large-scale transaction networks. Analyzing transaction networks facilitates fraud detection, market analysis, and supports government regulation. Despite many graph representation learning methods for transaction network analysis, we pinpoint two salient limitations that merit more investigation. Existing methods predominantly focus on the snapshots of transaction networks, sidelining the evolving nature of blockchain transaction networks. Existing methodologies may not sufficiently emphasize efficient, incremental learning capabilities, which are essential for addressing the scalability challenges in ever-expanding large-scale transaction networks. To address these challenges, we employed an incremental approach for random walk-based node representation learning in transaction networks. Further, we proposed a Metropolis-Hastings-based random walk mechanism for improved efficiency. The empirical evaluation conducted on blockchain transaction datasets reveals comparable performance in node classification tasks while reducing computational overhead. Potential applications include transaction network monitoring, the efficient classification of blockchain addresses for fraud detection or the identification of specialized address types within the network.
Zero-Shot Image Moderation in Google Ads with LLM-Assisted Textual Descriptions and Cross-modal Co-embeddings
Authors: Enming Luo, Wei Qiao, Katie Warren, Jingxiang Li, Eric Xiao, Krishna Viswanathan, Yuan Wang, Yintao Liu, Jimin Li, Ariel Fuxman
Venue: WSDM
Year: 2025

We present a scalable and agile approach for ads image content moderation at Google, addressing the challenges of moderating massive volumes of ads with diverse content and evolving policies. The proposed method utilizes human-curated textual descriptions and cross-modal text-image co-embeddings to enable zero-shot classification of policy violating ads images, bypassing the need for extensive supervised training data and human labeling. By leveraging large language models (LLMs) and user expertise, the system generates and refines a comprehensive set of textual descriptions representing policy guidelines. During inference, co-embedding similarity between incoming images and the textual descriptions serves as a reliable signal for policy violation detection, enabling efficient and adaptable ads content moderation. Evaluation results demonstrate the efficacy of this framework in significantly boosting the detection of policy violating content.
AMLCDR: An Adaptive Meta-Learning Model for Cross-Domain Recommendation by Aligning Preference Distributions
Authors: Fanqi Meng, Zhiyuan Zhang
Venue: WSDM
Year: 2025

The issue of data sparsity poses a formidable challenge in the field of recommender systems. Encouragingly, leveraging the interactions among overlapping users in the source domain can enhance item recommendation in the target domain. The transfer of user preferences across domains is a crucial concern in the cross-domain recommendation and represents a hopeful method to address data sparsity. Most existing methods transfer users' preference information by building a preference transfer network. These methods focus on the cross-domain mapping of preference features and ignore the inherent data distribution differences between the source domain and target domain. Consequently, the mapped user embeddings do not align with the item embeddings in the target domain and the recommendation quality decreases. On this basis, we propose a new method called Adaptive Meta-Learning for Cross-Domain Recommendation (AMLCDR). The method includes a meta-learning network for fully extracting user characteristics and generating a transfer network to reduce the user preference loss, as well as a domain adaptation network to align user preference distributions. We perform comprehensive experiments to assess the efficacy of AMLCDR by utilizing a substantial real-world dataset. We validate the effectiveness of data distribution alignment in domain adaptation. For diverse cross-domain recommendation tasks under different start conditions, AMLCDR outperforms state-of-the-art models in multiple evaluation metrics.
DDualSE: Decoupled Dual-head Squeeze and Excitation Attention for Sequential Recommendation
Authors: Nijia Mo, Jianxiang Zang, Zhan Wang, Hui Liu
Venue: WSDM
Year: 2025

Sequential recommendation is one of the key tasks in recommendation systems. Current approaches employed context-aware models with enhanced expressive capabilities, typically incorporating input with temporal and spatial information --- specifically, historical interaction items and their attributes. However, these models often represented the input as a 1D or 2D matrix, performing coarse-grained fusion of item attributes. This approach did not explicitly model the relationships between spatial and temporal information, consequently impeding the model's ability to acquire intricate local details. In this study, we propose the Decoupled Dual -headed Squeeze and Excitation attention module (DDualSE), which constructs a fine-grained 3D input by separately modeling temporal and spatial information. Additionally, DDualSE decouples embedding dimensions and the number of attention heads from both the sequence length and the number of attributes simultaneously, solving the low-rank bottleneck issue without introducing extra parameters or specifying additional hyper-parameters. We integrate DDualSE into BERT and conduct extensive experiments on three benchmark datasets to validate the effectiveness of the proposed model.
Untapping the Power of Indirect Relationships in Entity Summarization
Authors: Atefeh Moradan, Mohammad Sorkhpar, Atsushi Miyauchi, Davide Mottin, Ira Assent
Venue: WSDM
Year: 2025

Knowledge graphs form large networks of millions of entities (e.g., Michelle Obama, Barack Obama) and relationships (e.g., married). To obtain an overview of the entity, we need to inspect a potentially large number of relationships to other entities. For this reason, entity summarization aims to extract succinct but expressive descriptions of each entity. Yet, existing methods build their summaries only based on the immediate connections of an entity, disregarding how indirect relationships contain essential information for describing the entity (e.g., understanding Michelle Obama also via her husband's role as former president). We propose IRES, an unsupervised entity summarization method built on graph theoretical principles. We draw a notable connection between the informativeness of a summary and graph partitioning, and devise an effective approach to learn diverse aspects that characterize an entity. In a comprehensive experimental study, IRES shows superior summary quality. In particular, when full neighborhood information is available, IRES outperforms existing methods by 6 percentage points F1 while maintaining competitive computational efficiency.
Navigating the Hype and Embracing the Hope: The Future of Generative AI in Retail Product Discovery
Authors: Darshan Nagaraja
Venue: WSDM
Year: 2025

Generative AI is poised to revolutionize the retail industry, offering transformative potential to enhance customer experiences and operational efficiencies. Despite immense benefits -- such as personalized search, shopping assistance, product content enrichment, and dynamic recommendations -- the industry faces challenges distinguishing genuine value from overhyped applications. According to McKinsey & Company, Generative AI could unlock $240 billion to $390 billion in economic value for retailers, potentially increasing margins by up to 1.9 percentage points [1]. This paper navigates the delicate balance between the hype and the tangible promise of Generative AI in retail. We explore practical applications that deliver real value, critically examine overhyped use cases, and share strategies for successful AI integration. Readers will gain insights into effectively leveraging Generative AI while avoiding common pitfalls.
An Edge-Based Decomposition Framework for Temporal Networks
Authors: Lutz Oettershagen, Athanasios L. Konstantinidis, Giuseppe F. Italiano
Venue: WSDM
Year: 2025

A temporal network is a dynamic graph where every edge is assigned an integer time label that indicates at which discrete time step the edge is available. We consider the problem of hierarchically decomposing the network and introduce an edge-based decomposition framework that unifies the core and truss decompositions for temporal networks while allowing us to consider the network's temporal dimension. Based on our new framework, we introduce the (k,∆)-core and (k,∆)-truss decompositions, which are generalizations of the classic k-core and k-truss decompositions for multigraphs. Moreover, we show how (k,∆)-cores and (k,∆)-trusses can be efficiently further decomposed to obtain spatially and temporally connected components. We evaluate the characteristics of our new decompositions and the efficiency of our algorithms. Moreover, we demonstrate how our (k,∆)-decompositions can be applied to analyze malicious content in a Twitter network to obtain insights that state-of-the-art baselines cannot obtain.