OpenRaiser/Intern-Atlas huggingface dataset:
Enabling Real-Time Inference in Online Continual Learning via Device-Cloud Collaboration
Authors: Haibo Liu, Chen Gong, Zhenzhe Zheng, Shengzhong Liu, Fan Wu
Venue: WWW
Year: 2025

Online continual learning (CL) is becoming a mainstream paradigm to learn incrementally from task streams without forgetting previously learned knowledge. However, the current online CL primarily focuses on learning performance, such as avoiding catastrophic forgetting, neglecting the critical demands of system performance, such as real-time inference. As a result, the performance of real-time inference in online CL degrades significantly due to frequent data distribution variations and time-consuming model adaptation. In this work, we propose ELITE, an online CL framework with device-cloud collaboration, to realize on-device real-time inference on time-varying task streams with performance guarantee. To realize on-device real-time inference in online CL, ELITE features a new design of the model zoo comprising various pre-trained models with the assistance of the cloud, and proposes a task-oriented on-device model selection to quickly retrieve the best-fit models instead of performing time-consuming model retraining. To prevent performance degradation on new tasks not available in the cloud, we introduces a latency-aware on-device model fine-tuning strategy to adapt to new tasks with an accuracy-latency trade-off, and dynamically updates the model zoo to enhance ELITE. Extensive evaluations on five real-world datasets have been conducted, and the results demonstrate that ELITE consistently outperforms the state-of-art solutions, improving the accuracy by 16.3% on average and reducing the response latency by up to 1.98 times.
ModalSync: Synchronizing User Behavior with Multimodal Features for Multimodal Pre-training Recommendation
Authors: Shiqin Liu, Chaozhuo Li, Minjun Zhao, Litian Zhang, Jiajun Bu
Venue: WWW
Year: 2025

In the continually evolving landscape of online platforms, integrating multimodal information into recommender systems offers a promising avenue to enhance our understanding of user preferences and product insights. Traditional models primarily rely on user-item interactions, but the advent of multimodal systems utilizes additional data modalities-text, images, audio, and video-to enhance recommendation accuracy. However, existing multimodal recommendation architectures often fail to fully exploit the potential synergy between multimodal feature extraction and recommendation processes, leading to domain bias and false positives. In this work, we introduce ModalSync, a novel multimodal pre-training framework designed to synchronize multimodal features with user behaviors, closely aligning with human perceptual processes. Unlike previous approaches that utilize pre-trained generic encoders, ModalSync incorporates a pre-training method that integrates both unsupervised and supervised strategies, thereby fostering a harmonious relationship between interaction graphs and multimodal data. Our framework uniquely addresses domain bias by infusing recommendation-specific interaction data into the feature extraction process and reduces false positives by directing encoder attention towards crucial attributes. Furthermore, ModalSync introduces a staged co-training module that strategically adjusts the training dynamics of the feature extractors and GNNs, promoting an effective and seamless fusion of multimodal information. Extensive experiments across three public datasets demonstrate that ModalSync significantly outperforms existing methods, achieving state-of-the-art results.
The AI Revolution in Time Series: Challenges and Opportunites
Authors: Yan Liu
Venue: WWW
Year: 2025

Recent advancements in deep learning and artificial intelligence have driven significant progress in time series modeling and analysis. On one hand, researchers seek breakthroughs in performance on classical tasks such as forecasting, anomaly detection, classification, etc. On the other hand, it is intriguing to explore the potential for answering more complex inference and reasoning tasks from time series. In this keynote, I will examine the pathways toward foundation models for time series and discuss future research directions in this rapidly evolving field. The remarkable success of foundation models in natural language processing - exemplified by Generative Pre-trained Transformers (GPT) - suggests their potential to revolutionize time series analysis. I will introduce our recent efforts along this direction, including TEMPO, a novel framework designed to learn effective time series representations by leveraging two key inductive biases: one is explicit decomposition of trend, seasonal, and residual components, and the second is prompt-based distribution adaptation for diverse time series types. Beyond representation learning, practical applications demands advanced reasoning capabilities with multi-step time series inference task, requiring both compositional reasoning and computational precision. To tackle this challenge, I will discuss TS-reasoner, a program-aided inference agent that integrates large language models (LLMs) with structured execution pipelines, in-context learning, and self-correction mechanisms. I will discuss a new benchmark dataset and evaluation framework to systematically assess multi-step time series reasoning. By bridging deep learning advances with structured reasoning, I will highlight the next frontier in time series research, i.e., developing foundation models that enhance forecasting performance, generative models, and reasoning capabilities from time series across diverse applications.
The First Early Evidence of the Use of Browser Fingerprinting for Online Tracking
Authors: Zengrui Liu, Jimmy Dani, Yinzhi Cao, Shujiang Wu, Nitesh Saxena
Venue: WWW
Year: 2025

While advertising has become commonplace in today's online interactions, there is a notable dearth of research investigating the extent to which browser fingerprinting is harnessed for user tracking and targeted advertising. Prior studies only measured whether fingerprinting-related scripts are being run on the websites but that in itself does not necessarily mean that fingerprinting is being used for the privacy-invasive purpose of online tracking because fingerprinting might be deployed for the defensive purposes of bot/fraud detection and user authentication. It is imperative to address the mounting concerns regarding the utilization of browser fingerprinting in the realm of online advertising. This paper introduces "FPTrace" (fingerprinting-based tracking assessment and comprehensive evaluation framework), a framework to assess fingerprinting-based user tracking by analyzing ad changes from browser fingerprinting adjustments. Using FPTrace, we emulate user interactions, capture ad bid data, and monitor HTTP traffic. Our large-scale study reveals strong evidence of browser fingerprinting for ad tracking and targeting, shown by bid value disparities and reduced HTTP records after fingerprinting changes. We also show fingerprinting can bypass GDPR/CCPA opt-outs, enabling privacy-invasive tracking. In conclusion, our research unveils the widespread employment of browser fingerprinting in online advertising, prompting critical considerations regarding user privacy and data security within the digital advertising landscape.
Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support
Authors: Qijiong Liu, Lu Fan, Xiao-Ming Wu
Venue: WWW
Year: 2025

We present Legommenders, a unique library designed for content-based recommendation that enables the joint training of content encoders alongside behavior and interaction modules, thereby facilitating the seamless integration of content understanding directly into the recommendation pipeline. Legommenders allows researchers to effortlessly create and analyze over 1,000 distinct models across 15 diverse datasets. Further, it supports the incorporation of contemporary large language models, both as feature encoder and data generator, offering a robust platform for developing state-of-the-art recommendation models and enabling more personalized and effective content delivery.
Exploring Multimodal Pre-trained Models for Speech Emotion Recognition
Authors: Zhiyu Liu, Junchen Fu, Kaiwen Zheng, Joemon M. Jose
Venue: WWW
Year: 2025

Multimodal pre-trained models have demonstrated remarkable capabilities in processing diverse data types, including text and audio. However, their potential for Speech Emotion Recognition (SER) remains relatively underexplored. In this work, we investigate the effectiveness of such models for SER tasks and evaluate the state-of-the-art IISAN framework for efficient fine-tuning. We further improved IISAN by incorporating a dynamic gating mechanism, referred to as IISAN-MOE, to enhance adaptability and performance. Experimental results confirm that multimodal approaches consistently outperform their single-modal counterparts, with IISAN significantly boosting both effectiveness and efficiency. Moreover, the newly proposed IISAN-MOE refines IISAN's original static fusion mechanism, offering a more flexible and advanced solution for multimodal speech emotion recognition.
Multimodal Intent Recognition in E-Commerce: Challenges, Innovations, and Lightweight Solutions
Authors: Junwen Liu, Haikuan Huang, Gang Huang, Shuang Ge, Jinlian Hu
Venue: WWW
Year: 2025

The "Multimodal Dialogue System Intent Recognition Challenge", jointly organized by Alibaba Taobao and Tmall Group and the World Wide Web Conference (WWW), focuses on multimodal intent recognition in e-commerce scenarios, aiming to address technical challenges in joint understanding of images and text for customer service applications. During the preliminary round, over 1,500 teams participated, with 11 advancing to the semi-finals and 9 ultimately presenting in the final. Innovative approaches from participants centered on three key directions: multimodal data augmentation (e.g., synthetic sample generation, image-text co-augmentation), model optimization (discriminative fine-tuning, model soup fusion, etc.), and prompt engineering. Significantly, the top three teams elevated the weighted F1-score from a baseline of 0.78 to above 0.9. This improvement was achieved by incorporating a diverse set of techniques, including but not limited to vision-language models, structure-aware retrieval, and hierarchical label optimization. The competition outcomes validate the potential of lightweight models in data-scarce scenarios and provide open-source technical pathways for applying multimodal large language models to e-commerce customer service. These advancements drive progress in fine-grained semantic comprehension, domain adaptation, and efficient inference, offering valuable insights for the industrial deployment of intelligent customer service systems.
Subgraph Federated Unlearning
Authors: Fan Liu, Hao Liu
Venue: WWW
Year: 2025

Subgraph federated learning aims to collaboratively train a global model over distributed subgraphs stored in multiple local clients with strict privacy constraints, which is crucial to a wide range of applications such as healthcare, recommendation systems, and financial crime detection. With the increasing emphasis on the "right to be forgotten," the issue of machine unlearning of subgraph federated models has gained significant importance. However, existing federated unlearning approaches largely focused on unstructured data, overlooking the impact of structural dependency and cross-client interferences in graph-based data. To this end, in this paper, we propose ReGEnUnlearn, a subgraph federated unlearning framework for efficient and comprehensive unlearning of multiple target clients. Specifically, we first propose the Reinforced Federated Policy Sampler (RFPS) to learn optimal sampling strategies that minimize the interference among cross-client subgraphs. By interacting with the federated graph sampling environment, the agent learns to selectively forget an optimal subgraph of target clients, thus preserving the global model utility. Moreover, we propose the Parameter-free Graph Prompt Knowledge Distillation (PGPKD) module, which retains the unique graph knowledge contributed by the target clients, thereby facilitating comprehensive unlearning via a tailored gradient ascent objective. Extensive experiments in various federated settings demonstrate ReGEnUnlearn's superiority over existing federated unlearning methods, offering a speedup of 3.6× to 9× compared to traditional retraining while maintaining model utility within a range of 100%-102%.
Optimizing Revenue through User Coupon Recommendations in Truthful Online Ad Auctions
Authors: Xiaodong Liu, Xiao Lin, Yiming Ding, Changcheng Li, Peng Jiang, Weiran Shen
Venue: WWW
Year: 2025

Online advertising serves as the primary revenue source for numerous Internet companies, which typically sell advertising slots through auctions. Conventional online ad auctions assume constant click-through rates (CTRs) and conversion rates (CVRs) for ads during the auction process. However, this paper studies a new scenario where advertisers can offer coupons to users, thereby influencing both CTRs and CVRs and consequently, the platform's revenue. We study how to recommend user coupons to advertisers in truthful auction systems. We model the interaction between the platform and the advertisers as an extensive-form game, where advertisers first report coupon bids to the platform to receive coupon recommendations, and then participate in auctions by reporting their auction bids. Our research identifies a sufficient condition under which the advertisers' optimal strategy is to report their valuations truthfully in both the recommendation and auction stages. We construct two mechanisms based on these findings. The first mechanism is a distribution-free mechanism, which is easily implementable in industrial systems; and the second is a revenue-optimal mechanism that offers simpler implementation compared to existing work Liu et al. Both synthetic and industrial experiments show that our mechanisms improve the platform's revenue. Notably, our revenue-optimal mechanism achieves the same outcome compared to existing work by Liu et al., while offering a simpler implementation.
Unraveling Spatial-Temporal and Out-of-Distribution Patterns for Multivariate Time Series Classification
Authors: Xiao Liu, Yan Li, Haishuai Wang
Venue: WWW
Year: 2025

Time series in the real world exhibit complexity due to their diverse features and temporal patterns evolving over time. The evolving nature represents a multidimensional temporal out-of-distribution (MT-OOD) phenomenon. Nevertheless, the majority of current research fail to account for the MT-OOD phenomenon. Although particular methods (i.e, DIVERSIFY) acknowledge the OOD phenomenon, they merely address the single temporal dimension OOD. To tackle the intricate and demanding MT-OOD, we propose a novel method by leveraging Spatial-Temporal dynamics and Out-of-distribution Patterns, named STOP, to unravel the complex interplay of spatial and temporal factors to discern patterns that lie beyond conventional distributions. By converting each variable of a multivariate time series into a subgraph and fusing these subgraphs, STOP is able to capture both the inter-correlations within each variable and intra-correlations across different variables. To model the MT-OOD property, we further extract out-of-distribution patterns from the spatial-temporal subgraphs, which are then seamlessly integrated into the constructed graph that fortifies the framework for more resilient classification outcomes. Extensive experiments on real-world datasets validates the effectiveness of STOP, asserting its superiority in multivariate time series classification.
Fitting Into Any Shape: A Flexible LLM-Based Re-Ranker With Configurable Depth and Width
Authors: Zheng Liu, Chaofan Li, Shitao Xiao, Chaozhuo Li, Chen Jason Zhang, Hao Liao, Defu Lian, Yingxia Shao
Venue: WWW
Year: 2025

Large language models (LLMs) provide powerful foundations to perform fine-grained text re-ranking. However, they are often prohibitive in reality due to constraints on computation bandwidth. In this work, we propose a flexible architecture called Matroyshka Re-Ranker, which is designed to facilitate runtime customization of model layers and sequence lengths at each layer based on users' configurations. Consequently, the LLM-based re-rankers can be made applicable across various real-world situations. The increased flexibility may come at the cost of precision loss. To address this problem, we introduce a suite of techniques to optimize the performance. First, we propose cascaded self-distillation, where each sub-architecture learns to preserve a precise re-ranking performance from its super components, whose predictions can be exploited as smooth and informative teacher signals. Second, we design a factorized compensation mechanism, where two collaborative LoRA modules, vertical and horizontal, are jointly employed to compensate for the precision loss resulted from arbitrary combinations of layer and sequence compression. We perform comprehensive experiments using passage and document retrieval datasets from MSMARCO, along with all public datasets from BEIR. In our experiments, Matryoshka Re-Ranker substantially outperforms existing methods, while effectively preserving its superior performance across various compression forms and application scenarios. We have publicly released our method at this https://github.com/FlagOpen/FlagEmbedding repo.
Hyperbolic Variational Graph Auto-Encoder for Next POI Recommendation
Authors: Yuwen Liu, Lianyong Qi, Xingyuan Mao, Weiming Liu, Fan Wang, Xiaolong Xu, Xuyun Zhang, Wanchun Dou, Xiaokang Zhou, Amin Beheshti
Venue: WWW
Year: 2025

Next Point-of-Interest (POI) recommendation has become a crucial task in Location-Based Social Networks (LBSNs), which provide personalized recommendations by predicting the user's next check-in locations. Commonly used models including Recurrent Neural Networks (RNNs) and Graph Convolutional Networks (GCNs) have been widely explored. However, these models face significant challenges, including the difficulty of capturing the hierarchical and tree-like structure of POIs in Euclidean space and the sparsity problem inherent in POI recommendations. To address these challenges, we propose a Hyperbolic Variational Graph Auto-Encoder (HVGAE) for next POI recommendation. Specifically, we utilize a Hyperbolic Graph Convolutional Network (Hyperbolic GCN) to model hierarchical structures and tree-like relationships by converting node embeddings from euclidean space to hyperbolic space. Then we use Variational Graph Auto-Encoder (VGAE) to convert node embeddings to probabilistic distributions, enhancing the capture of deeper latent features and providing a more robust model structure. Furthermore, we combine the Mamba4Rec recommender and Rotary Position Embedding (RoPE) and propose Rotary Position Mamba (RPMamba) to effectively utilize POI embeddings rich in sequential information, which improves the accuracy of the next POI recommendation. Extensive experiments on three public datasets demonstrate the superior performance of the HVGAE model.
Multimodal Semantic Retrieval for Product Search
Authors: Dong Liu, Esther Lopez Ramos
Venue: WWW
Year: 2025

Semantic retrieval (also known as dense retrieval) based on textual data has been extensively studied for both web search and product search application fields, where the relevance of a query and a potential target document is computed by their dense vector representation comparison. Product image is crucial for e-commerce search interactions and is a key factor for customers at product explorations. However, its impact on semantic retrieval has not been well studied yet. In this research, we build a multimodal representation for product items in e-commerce search in contrast to pure-text representation of products, and investigate the impact of such representations. The models are developed and evaluated on e-commerce datasets. We demonstrate that a multimodal representation scheme for a product can show improvement either on purchase recall or relevance accuracy in semantic retrieval. Additionally, we provide numerical analysis for exclusive matches retrieved by a multimodal semantic retrieval model versus a text-only semantic retrieval model, to demonstrate the validation of multimodal solutions.
Multi-Granularity Augmented Graph Learning for Spoofing Transaction Detection
Authors: Xin Liu, Haojun Rui, Dawei Cheng, Li Han, Zhongyun Zhou, Guoping Zhao
Venue: WWW
Year: 2025

Spoofing is a deceptive trading strategy where fraudsters place a large number of fake orders to manipulate market prices, severely distorting market fairness and threatening market stability. With the advancement of fraudulent tactics, spoofing patterns span across various levels of interaction, involving not only the local structure of individual spoofing transactions but also spoofing groups and global patterns. Relying solely on local context makes it challenging to capture multi-granularity risk signals, especially for organized and covert spoofing.Additionally, existing methods fail to consider the differences and relative importance between features of varying granularity, leading to feature distortion and noise. Therefore, we propose a multi-granularity augmented graph learning method that differentially captures fraud signals at local, group, and global levels. It utilizes multi-hop differential aggregation and community-augmented strategy to capture information from local to global perspectives, adaptively distinguishing the contributions of different granularity. To avoid excessive fusion of multi-granularity information, we combine contrastive loss and cross-entropy loss for joint optimization, preserving key features while enhancing the method's robustness and accuracy. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed approach in spoofing detection, providing a robust solution for regulatory agencies. Our work will help financial institutions enhance their regulatory capabilities, protect investors' interests, and promote the healthy development of financial markets.
Beyond Node Condensation: Learning Tiny Graphs via Joint Graph Condensation
Authors: Yezi Liu, Yanning Shen
Venue: WWW
Year: 2025

Training graph neural networks (GNNs) on large-scale graphs is challenging due to the high computational cost of numerous nodes and high-dimensional features. Existing studies tackle this problem only by reducing the number of nodes in the graph. However, the resulting condensed graph data can still be cumbersome. To address this issue, we introduce a novel approach called joint condensation, and propose a novel framework, TinyGraph, which condenses both features and nodes simultaneously while preserving the critical feature and graph information of the original graph. Specifically, TinyGraph formulates the problem as matching the gradients of the GNN weights trained on the condensed graph to the gradients derived from training on the original graph. The condensed graph obtained by minimizing the matching loss along the training trajectory can henceforth retain critical information in the original graph. Extensive experiments were carried out to demonstrate the effectiveness of the proposed TinyGraph. For example, a GNN trained with TinyGraph retains 98.5% and 97.5% of the original test accuracy on the Cora and Citeseer datasets, respectively, while significantly reducing the number of nodes by 97.4% and 98.2%, and the number of features by 90.0% on both datasets.