OpenRaiser/Intern-Atlas huggingface dataset:
MIT: A Multi-Tower Information Transfer Framework Based on Hierarchical Task Relationship Modeling
Authors: Rui Zhou, Hao Wang, Wei Guo, Qinglin Jia, Wenjia Xie, Xiang Xu, Yong Liu, Defu Lian, Enhong Chen
Venue: WWW
Year: 2025

With the advancement of e-commerce platforms and online recommendation systems, conversion objectives have evolved from singular to diverse goals. To simultaneously enhance the performance of multiple conversion objectives, multi-task learning has become a classic solution. However, existing multi-task models either neglect task dependencies or impose manually defined sequential dependencies, subsequently transferring information based on task output vectors. Such methods fail to capture the intricate relationships between tasks in real-world scenarios and underutilize the logits outputs of tasks. In this paper, we propose the Multi-tower Information Transfer(MIT) framework, which introduces hierarchical modeling of task relationships for the first time. Specifically, we employ a data-driven approach to abstract tasks and their dependencies as nodes and edges in a graph, thereby constructing a Bayesian network of task relationships. To enhance information transfer, MIT not only refines existing vector-based methods but also pioneers a novel logits-based transfer mechanism. Extensive experiments on public and industrial datasets demonstrate the effectiveness of MIT. Furthermore, online A/B tests conducted in Huawei's online advertising platform reveal that MIT achieves RPM and CPM improvements of +2.00% and +8.87%, respectively. MIT has been fully deployed in Huawei's online advertising platform, delivering superior services to hundreds of millions of users.Our implementation is available at https://github.com/USTC-StarTeam/MIT.
Refining Confusion and Ignorance in Trusted Multi-View Classification
Authors: Xujing Zhou, Xiaodong Yue, Yufei Chen, Linye Li
Venue: WWW
Year: 2025

Trusted multi-view classification has been widely applied in safety-critical domains by integrating single-view information and providing reliable uncertainty estimates. However, using a unified uncertainty measure overlooks the differences between various types of uncertainty, which impedes the adoption of appropriate learning strategies for different types of uncertain samples to enhance model performance. To address this issue, we propose a novel uncertainty measure based on Dempster-Shafer Theory, which refines uncertainty into confusion caused by multi-view classification conflicts and ignorance arising from unknown classes. Based on the refined uncertainty measure, we implement a trusted multi-view classification algorithm, where the learning objective consists of the loss terms of prediction error, ignorance and confusion. Through optimizing the hybrid learning objective, we can improve the prediction accuracy and identify the unknown and conflicted samples in multi-view classification. Extensive experiments on authorized multi-view datasets validate the superiority of the proposed method. The codes have been released at https://github.com/muxixi727/RTMC.
Dataset for Industrial Question Answering with Explanation and Scalable Ensemble Generation
Authors: Yan Zhou, Baifan Zhou, Huajian Li, Qianhang Lyu, Yuanwei Qu, Arild Waaler, Ingrid C. Yu
Venue: WWW
Year: 2025

The digital and green transition under Industry 4.0 has accelerated the adoption of AI in industries such as manufacturing, energy, and mining. Question Answering with Explanation (QAE), as a way of human interaction with AI, is crucial for enhancing transparency and trust in high-stakes industrial applications. However, industrial QAE remains underexplored due to the lack of publicly available, high-quality datasets, hindered by the need for expert effort and corporate restrictions. To this end, we introduce PANDAX ( https://doi.org/10.5281/zenodo.14510798 ), the first open-source industrial QAE dataset, and SEG, a scalable method for generating high-quality QAE datasets using LLMs. PANDAX focuses on three key topics of industrial system information: partonomy, functionality, and parameters, across critical domains such as green technology and cooling systems. SEG ensures scalability and quality through ensemble generation, majority voting, expert ranking, etc. The human evaluation validates PANDAX's high quality, positioning it as a valuable resource for advancing QAE techniques, benchmarking language technologies, and supporting research in explainable AI for industrial systems.
Multimodal Graph-Based Variational Mixture of Experts Network for Zero-Shot Multimodal Information Extraction
Authors: Baohang Zhou, Ying Zhang, Yu Zhao, Xuhui Sui, Xiaojie Yuan
Venue: WWW
Year: 2025

Multimodal information extraction on social media is a series of fundamental tasks to construct the multimodal knowledge graph. The tasks aim to extract the structural information in free texts with the incorporate images, including: multimodal named entity typing and multimodal relation extraction. However, the growing number of multimodal data implies a growing category set and the newly emerged entity types or relations should be recognized without additional training. To address the aforementioned challenges, we focus on the zero-shot multimodal information extraction tasks which require using textual and visual modalities for recognizing unseen categories. Compared with text-based zero-shot information extraction models, the existing multimodal ones make the textual and visual modalities aligned directly and exploit various fusion strategies to improve their performances. But the existing methods ignore the fine-grained semantic correlation of text-image pairs and samples. Therefore, we propose the multimodal graph-based variational mixture of experts network (MG-VMoE) which takes the MoE network as the backbone and exploits it for aligning multimodal representations in a fine-grained way. Considering to learn informative representations of multimodal data, we design each expert network as a variational information bottleneck to process two modalities in a uni-backbone. Moreover, we also propose the multimodal graph-based virtual adversarial training to learn the semantic correlation between the samples. The experimental results on the two benchmark datasets demonstrate the superiority of MG-VMoE over the baselines.
Unveiling the Potential of LLMs in Simulated Society: A Knowledge-Driven LLM Agent Framework for User Modeling
Authors: Shengmao Zhu, Bingbing Xu, Yige Yuan, Bin Xie, Yunfan Li, Huawei Shen
Venue: WWW
Year: 2025

User modeling serves as the cornerstone of modern recommendation systems, focusing on the precise identification of user preferences and behavioral signatures to enable personalized service delivery. Existing recommendation methods face issues like sparse data and lack of transparency. The advent of large language models (LLMs) has brought new possibilities for inferring user preferences, which provides natural-language-based recommendation rationales and affective expressions. Motivated by AgentSociety Challenge @ WWW 2025 focused on simulating user-item interactions, we propose a knowledge-driven LLM agent framework for simulating user-item review interactions, which includes three modules: preference refinement, dual-signal injection, and category distinguisher. Specifically, preference refinement aims to model user and item profiles, enabling LLMs to perceive users and items comprehensively. Dual-signal Injection aims to incorporate external knowledge, such as collaborative filtering and distribution knowledge. Category distinguisher is designed to analyze the differences between true data and simulated data. Both online and offline experimental results demonstrate the effectiveness of this framework and prove the great potential of LLMs in user modeling.
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
Authors: Tianyu Zhu, Myong Chol Jung, Jesse Clark
Venue: WWW
Year: 2025

Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular training frameworks typically learn from binary (positive/negative) relevance, making them ineffective at incorporating desired rankings. As a result, the poor ranking performance of these models forces systems to employ a re-ranker, which increases complexity, maintenance effort and inference time. In this paper, we introduce Generalized Contrastive Learning (GCL), a novel approach designed to learn from fine-grained rankings beyond binary relevance scores. During training, we convert the desired ranking scores into weights that scale the loss function. GCL encodes both relevance and ranking information into a unified embedding space, enabling single-stage retrieval and significantly streamlining the retrieval system. During our research, we identified a lack of public multi-modal datasets that measure both retrieval and ranking capabilities. To address this gap and facilitate future research in ranked retrieval, we curated a large-scale marqo-gs-10m dataset using GPT-4 and Google Shopping, providing ranking scores for each of the 10 million query-document pairs. Our results show that GCL achieves a 29.3% increase in NDCG@10 for in-domain evaluations and 6.0% to 10.0% increases for cold-start evaluations compared to the finetuned CLIP baseline with marqo-gs-10m. Additionally, we evaluated GCL offline on a proprietary user interaction dataset, which showed an 11.2% gain for in-domain evaluations. The dataset and the method are available at: https://github.com/marqo-ai/GCL
Long-Term Interest Clock: Fine-Grained Time Perception in Streaming Recommendation System
Authors: Yongchun Zhu, Guanyu Jiang, Jingwu Chen, Feng Zhang, Qi Wu, Zuotao Liu
Venue: WWW
Year: 2025

User interests manifest a dynamic pattern within the course of a day, e.g., a user usually favors soft music at 8 a.m. but may turn to ambient music at 10 p.m. To model dynamic interests in a day, hour embedding is widely used in traditional daily-trained industrial recommendation systems. However, its discreteness can cause periodical online patterns and instability in recent streaming recommendation systems. Recently, Interest Clock has achieved remarkable performance in streaming recommendation systems. Nevertheless, it models users' dynamic interests in a coarse-grained manner, merely encoding users' discrete interests of 24 hours from short-term behaviors. In this paper, we propose a fine-grained method for perceiving time information for streaming recommendation systems, named Long-term Interest Clock (LIC). The key idea of LIC is adaptively calculating current user interests by taking into consideration the relevance of long-term behaviors around current time (e.g., 8 a.m.) given a candidate item. LIC consists of two modules: (1) Clock-GSU retrieves a sub-sequence by searching through long-term behaviors, using query information from a candidate item and current time, (2) Clock-ESU employs a time-gap-aware attention mechanism to aggregate sub-sequence with the candidate item. With Clock-GSU and Clock-ESU, LIC is capable of capturing users' dynamic fine-grained interests from long-term behaviors. We conduct online A/B tests, obtaining +0.122% improvements on user active days. Besides, the extended offline experiments show improvements as well. Long-term Interest Clock has been integrated into Douyin Music App's recommendation system.
HEAL: Healthcare Emergency Assistants Leveraging Large Language Models
Authors: Zihan Zhu, Qiongkai Xu, Amin Beheshti
Venue: WWW
Year: 2025

Emergency Department (ED) is an essential component of health-care systems, providing 24-hour care to patients who require urgent medical treatment and attention. Over time, ED activity has increased rapidly while the number of staff has often remained constrained, consequently leading to overcrowding and potentially compromised patient care. The emergence of advanced Large Language Models (LLMs) presents a unique opportunity to address such a challenge. In this paper, we introduce an innovative system, HEAL, to automate and optimize the conversations and information extraction components involved in the triage process in EDs. HEAL operates in two distinct modes: i) Registration & Triage Mode for collecting initial patient information and categorizing triage levels, and ii) Monitoring Mode for continuous evaluation of conditions of patients in waiting rooms. Furthermore, we demonstrate the effectiveness of our system in patient information collection and triage categorization on our collected dataset. Video illustration and more materials can be found in https://github.com/Zihan-Zhu/HEAL.
Bridging the Gap: Teacher-Assisted Wasserstein Knowledge Distillation for Efficient Multi-Modal Recommendation
Authors: Ziyi Zhuang, Hanwen Du, Hui Han, Youhua Li, Junchen Fu, Joemon M. Jose, Yongxin Ni
Venue: WWW
Year: 2025

Multi-modal recommender systems (MMRecs) leverage diverse modalities to deliver personalized recommendations, yet they often struggle with efficiency due to the large size of modality encoders and the complexity of fusing high-dimensional features. To address the efficiency issue, a promising solution is to compress a cumbersome MMRec into a lightweight ID-based Multi- Layer Perceptron-based Recommender system (MLPRec) through Knowledge Distillation (KD). Despite effectiveness, we argue that this approach overlooks the significant gap between the complex teacher MMRec and the lightweight, ID-based student MLPRec, which differ significantly in size, architecture, and input modalities, leading to ineffective knowledge transfer and suboptimal student performance. To bridge this gap, we propose TARec, a novel teacher-assisted Wasserstein Knowledge Distillation framework for compressing MMRecs into an efficient MLPRec. TARec introduces: (i) a two-staged KD process using an intermediate Teacher Assistant (TA) model to bridge the gap between teacher and student, facilitating smoother knowledge transfer; (ii) logit-level KD using the Wasserstein Distance as metric, replacing the conventional KL divergence to ensure stable gradient flow even with significant teacher-student gaps; and (iii) embedding-level contrastive KD to further distill high-quality embedding-level knowledge from teacher. Extensive experiments on real-world datasets verify the effectiveness of TARec, demonstrating that TARec significantly outperforms the state-of-the-art MMRecs while reducing computational costs. Our code is available at: https://github.com/Suehn/TARec.git.
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction
Authors: Jinfeng Zhuang, Yinrui Li, Runze Su, Ke Xu, Zhixuan Shao, Kungang Li, Ling Leng, Han Sun, Meng Qi, Yixiong Meng, Yang Tang, Zhifang Liu, Qifei Shen, Aayush Mudgal
Venue: WWW
Year: 2025

The predictions of click through rate (CTR) and conversion rate (CVR) play a crucial role in the success of ad-recommendation systems. A Deep Hierarchical Ensemble Network (DHEN) has been proposed to integrate multiple feature crossing modules and has achieved great success in CTR prediction. However, its performance for CVR prediction is unclear in the conversion ads setting, where an ad bids for the probability of a user's off-site actions on a third party website or app, including purchase, add to cart, sign up, etc. Because of the ensemble nature, the degree of freedom of DHEN is essentially high, for example, 1) What feature-crossing modules (MLP, DCN, Transformer, to name a few) should be included in DHEN? 2) How deep and wide should DHEN be to achieve the best trade-off between efficiency and efficacy? 3) What hyper-parameters to choose in each feature-crossing module? Orthogonal to the model architecture, the input personalization features also significantly impact model performance with a high degree of freedom. It is an important and interesting problem in the advertising industry on how to make DHEN work effectively with a diverse collection of personalization features for web-scale CVR prediction. In this paper, we attack this problem and present our contributions biased to the applied data science side, including: First, we propose a multitask learning framework with DHEN as the single backbone model architecture to predict all CVR tasks, with a detailed study on how to make DHEN work effectively in practice; Second, we build both on-site real-time user behavior sequences and off-site conversion event sequences for CVR prediction purposes, and conduct ablation study on its importance; Last but not least, we propose a self-supervised auxiliary loss to predict future actions in the input sequence, to help resolve the label sparseness issue in CVR prediction. Our method achieves state-of-the-art performance compared to previous single feature crossing modules with pre-trained user personalization features. It has been deployed in our Pinterest conversion ad recommendation system and has significantly boosted both user value and advertiser value by connecting online inspiration to real-world actions.
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Authors: Dingyi Zhuang, Hanyong Xu, Xiaotong Guo, Yunhan Zheng, Shenhao Wang, Jinhua Zhao
Venue: WWW
Year: 2025

Urban prediction tasks, such as forecasting traffic flow, temperature, and crime rates, are crucial for efficient urban planning and management. However, existing Spatiotemporal Graph Neural Networks (ST-GNNs) often rely solely on accuracy, overlooking spatial and demographic disparities in their predictions. This oversight can lead to imbalanced resource allocation and exacerbate existing inequities in urban areas. This study introduces a Residual-Aware Attention (RAA) Block and an equality-enhancing loss function to address these disparities. By adapting the adjacency matrix during training and incorporating spatial disparity metrics, our approach aims to reduce local segregation of residuals and errors. We applied our methodology to urban prediction tasks in Chicago, utilizing travel demand datasets as an example. Our model achieved a 48% significant improvement in fairness metrics with only a 9% increase in error metrics. Spatial analysis of residual distributions revealed that models with RAA Blocks produced more equitable prediction results, particularly by reducing errors clustered in central regions, supporting more balanced and equitable urban planning and policy-making.
Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs
Authors: Xingrui Zhuo, Jiapu Wang, Gongqing Wu, Shirui Pan, Xindong Wu
Venue: WWW
Year: 2025

Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. The whole process is commonly referred to as Query Pattern Learning (QPL). However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models' performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. Unlike the external information introduced by previous QPL methods, we first propose code-like instructions to express FOL queries in an alternative format. This format utilizes textual variables and nested tuples to convey the logical semantics within FOL queries, serving as raw materials for a PLM-based instruction encoder to obtain complete query patterns. Building on this, we design a query-guided instruction decoder to adapt query patterns to KGQE models. To further enhance QIPP's effectiveness across various KGQE models, we propose a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component, allowing KGQE models to utilize query patterns more efficiently. Extensive experiments demonstrate that our plug-and-play method improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods.
A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals
Authors: Julian Zimmert, Róbert Busa-Fekete, András György, Linhai Qiu, Hyomin Choi, Tzu-Wei Sung, Hao Shen, Sharmila Subramaniam, Li Xiao
Venue: WWW
Year: 2025

Web refresh crawling is the problem of keeping a cache of web pages fresh, that is, having the most recent copy available when a page is requested, given a limited bandwidth available to the crawler. Under the assumption that the change and request events, resp., to each web page follow independent Poisson processes, the optimal scheduling policy was derived by Azar et al. 2018. In this paper, we study an extension of this problem where side information indicating content changes, such as various types of web pings, for example, signals from sitemaps, content delivery networks, etc., is available. Incorporating such side information into the crawling policy is challenging, because (i) the signals can be noisy with false positive events and with missing change events; and (ii) the crawler should achieve a fair performance over web pages regardless of the quality of the side information, which might differ from web page to web page. We propose a scalable crawling algorithm which (i) uses the noisy side information in an optimal way under mild assumptions; (ii) can be deployed without heavy centralized computation; (iii) is able to crawl web pages at a constant total rate without spikes in the total bandwidth usage over any time interval, and automatically adapt to the new optimal solution when the total bandwidth changes without centralized computation. Experiments clearly demonstrate the versatility of our approach.
TAT: Improving Stance Detection on Social Media through Thought Alignment with LLMs
Authors: Minghui Zou, Ronghui Guo, Sai Zhang, Xiaowang Zhang, Zhiyong Feng
Venue: WWW
Year: 2025

Stance detection, which identifies a given statement's stance toward a specific target, plays a crucial role in various fields. With the development of large language models (LLMs), researchers have sought to integrate them into stance detection systems in two main approaches: finetuning-based approaches, which leverage additional data generated by LLMs or directly finetune LLMs with existing datasets, and prompt engineering-based approaches, which use task-specific prompts to guide LLMs without additional training. However, these methods face significant challenges, including limited accuracy and complexity of the synthesized data, reliance on resource-intensive models, and inefficiencies during inference. To address these limitations, this paper proposes a novel framework that integrates thought-chain data augmentation to systematically enrich training data by generating logically consistent reasoning chains, and thought-aligned finetuning to internalize reasoning capabilities into the model by harmonizing reasoning-intensive and direct prediction paradigms. Experimental results demonstrate that the proposed approach achieves state-of-the-art performance in both in-target and cross-target settings, validating its effectiveness.