OpenRaiser/Intern-Atlas huggingface dataset:
Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
Authors: Brian J. Chan, Chao-Ting Chen, Jui-Hung Cheng, Hen-Hsen Huang
Venue: WWW
Year: 2025

Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. However, RAG introduces challenges such as retrieval latency, potential errors in document selection, and increased system complexity. With the advent of large language models (LLMs) featuring significantly extended context windows, this paper proposes an alternative paradigm, cache-augmented generation (CAG) that bypasses real-time retrieval. Our method involves preloading all relevant resources, especially when the documents or knowledge for retrieval are of a limited and manageable size, into the LLM's extended context and caching its runtime parameters. During inference, the model utilizes these preloaded parameters to answer queries without additional retrieval steps. Comparative analyses reveal that CAG eliminates retrieval latency and minimizes retrieval errors while maintaining context relevance. Performance evaluations across multiple benchmarks highlight scenarios where long-context LLMs either outperform or complement traditional RAG pipelines. These findings suggest that, for certain applications, particularly those with a constrained knowledge base, CAG provide a streamlined and efficient alternative to RAG, achieving comparable or superior results with reduced complexity.
Singleton CTR Prediction: Towards Single-User Modeling Paradigm with Large Language Models
Authors: Xiyu Chang, Liang Zhang, Yong He, Binzong Geng, Ke Ding, Xing Zhu, Jian Cao, Linjian Mo
Venue: WWW
Year: 2025

Click-through rate (CTR) prediction is a critical task of recommender systems. Existing CTR prediction methods typically aggregate data from multiple users, achieving significant performance but facing challenges such as privacy breaches and users' reluctance to share data. Recent advancements in large language models (LLMs) for few-shot learning have demonstrated the potential to overcome these limitations by fine-tuning with minimal data and leveraging embedded knowledge for robust performance. Motivated by this, we propose a novel paradigm-Singleton CTR prediction (SCTR)-which leverages LLMs to model CTR using data from a single user. This study aims to explore the feasibility of SCTR and identify potential research topics. To this end, we present an exploratory framework for SCTR and conduct preliminary experiments on several datasets, demonstrating the significant potential of this paradigm in personalization and privacy protection. Finally, we present some potential research tasks of SCTR for further studies.
PerSRV: Personalized Sticker Retrieval with Vision-Language Model
Authors: Heng Er Metilda Chee, Jiayin Wang, Zhiqiang Guo, Weizhi Ma, Min Zhang
Venue: WWW
Year: 2025

Instant Messaging is a popular mean for daily communication, allowing users to send text and stickers. As the saying goes, ''a picture is worth a thousand words'', so developing an effective sticker retrieval technique is crucial for enhancing user experience. However, existing sticker retrieval methods rely on labeled data to interpret stickers, and general-purpose Vision-Language Models (VLMs) often struggle to capture the unique semantics of stickers. Additionally, relevant-based sticker retrieval methods lack personalization, creating a gap between diverse user expectations and retrieval results. To address these, we propose the Personalized Sticker Retrieval with Vision-Language Model framework, namely PerSRV, structured into offline calculations and online processing modules. The online retrieval part follows the paradigm of relevant recall and personalized ranking, supported by the offline pre-calculation parts, which are sticker semantic understanding, utility evaluation and personalization modules. Firstly, for sticker-level semantic understanding, we supervised fine-tuned LLaVA-1.5-7B to generate human-like sticker semantics, complemented by textual content extracted from figures and historical interaction queries. Secondly, we investigate three crowd-sourcing metrics for sticker utility evaluation. Thirdly, we cluster style centroids based on users' historical interactions to achieve personal preference modeling. Finally, we evaluate our proposed PerSRV method on a public sticker retrieval dataset from WeChat, containing 543,098 candidates and 12,568 interactions. Experimental results show that PerSRV significantly outperforms existing methods in multi-modal sticker retrieval. Additionally, our supervised fine-tuned VLM delivers notable improvements in sticker semantic understandings. The code and fine-tuned LLaVA model for sticker understanding are publicly available.
PRECTR: A Synergistic Framework for Integrating Personalized Search Relevance Matching and CTR Prediction
Authors: Rong Chen, Shuzhi Cao, Ailong He, Shuguang Han, Jufeng Chen
Venue: WWW
Year: 2025

The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for user queries whereas the latter forecasts which item may better match user interest. Prior research typically develops two models to predict the CTR and search relevance separately, then ranking candidate items based on the fusion of the two outputs. However, such a divide-and-conquer paradigm creates the inconsistency between different models. Meanwhile, the search relevance model mainly concentrates on the degree of objective text matching while neglecting personalized differences among different users, leading to restricted model performance. To tackle these issues, we propose a unified Personalized Search RElevance Matching and CTR Prediction Fusion Model (PRECTR). Specifically, based on the conditional probability fusion mechanism, PRECTR integrates the CTR prediction and search relevance matching into one framework to enhance the interaction and consistency of the two modules. However, directly optimizing CTR binary classification loss may bring challenges to the fusion model's convergence and indefinitely promote the exposure of items with high CTR, regardless of their search relevance. Hence, we further introduce two-stage training and semantic consistency regularization to accelerate the model's convergence and restrain the recommendation of irrelevant items. Finally, acknowledging that different users may have varied relevance preferences, we assessed current users' relevance preferences by analyzing past users' preferences for similar queries and tailored incentives for different candidate items accordingly. Extensive experimental results on our production dataset and online A/B testing demonstrate the effectiveness and superiority of our proposed PRECTR method.
LLM-powered Heterogeneous Information Network Analytics
Authors: Hao Chen, Lun Du, Xu Chen, Xiaojun Ma, Jiang Zhang
Venue: WWW
Year: 2025

Most existing Knowledge Base Question Answering methods focus primarily on retrieving factual information, leaving more complex, analysis-driven tasks relatively unexplored. However, real-world queries often involve graph-based computations such as degree calculation or community detection, which require more advanced reasoning. In this paper, we introduce LLM4GraphAna, a Large Language Model-based approach designed to handle these challenging, analysis-focused queries within the KBQA framework. By integrating Function Orchestration and Parameterization, LLM4GraphAna can invoke our well-defined functions to perform graph analytics. Experimental results demonstrate that our method significantly improves performance on analysis-intensive questions.
FLock: Robust and Privacy-Preserving Federated Learning based on Practical Blockchain State Channels
Authors: Ruonan Chen, Ye Dong, Yizhong Liu, Tingyu Fan, Dawei Li, Zhenyu Guan, Jianwei Liu, Jianying Zhou
Venue: WWW
Year: 2025

Federated Learning (FL) is a distributed machine learning paradigm that allows multiple clients to train models collaboratively without sharing local data. Numerous works have explored security and privacy protection in FL, as well as its integration with blockchain technology. However, existing FL works still face critical issues. i) It is difficult to achieving poisoning robustness and data privacy while ensuring high model accuracy. Malicious clients can launch poisoning attacks that degrade the global model. Besides, aggregators can infer private data from the gradients, causing privacy leakages. Existing privacy-preserving poisoning defense FL solutions suffer from decreased model accuracy and high computational overhead. ii) Blockchain-assisted FL records iterative gradient updates on-chain to prevent model tampering, yet existing schemes are not compatible with practical blockchains and incur high costs for maintaining the gradients on-chain. Besides, incentives are overlooked, where unfair reward distribution hinders the sustainable development of the FL community. In this work, we propose FLock, a robust and privacy-preserving FL scheme based on practical blockchain state channels. First, we propose a lightweight secure Multi-party Computation (MPC)-friendly robust aggregation method through quantization, median, and Hamming distance, which could resist poisoning attacks against up to <50% malicious clients. Besides, we propose communication-efficient Shamir's secret sharing-based MPC protocols to protect data privacy with high model accuracy. Second, we utilize blockchain off-chain state channels to achieve immutable model records and incentive distribution. FLock achieves cost-effective compatibility with practical cryptocurrency platforms, e.g. Ethereum, along with fair incentives, by merging the secure aggregation into a multi-party state channel. In addition, a pipelined Byzantine Fault-Tolerant (BFT) consensus is integrated where each aggregator can reconstruct the final aggregated results. Lastly, we implement FLock and the evaluation results demonstrate that FLock enhances robustness and privacy, while maintaining efficiency and high model accuracy. Even with 25 aggregators and 100 clients, FLock can complete one secure aggregation for ResNet in 2 minutes over a WAN. FLock successfully implements secure aggregation with such a large number of aggregators, thereby enhancing the fault tolerance of the aggregation.
RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
Authors: Luyu Chen, Quanyu Dai, Zeyu Zhang, Xueyang Feng, Mingyu Zhang, Pengcheng Tang, Xu Chen, Yue Zhu, Zhenhua Dong
Venue: WWW
Year: 2025

Conversational recommender systems (CRS) enhance user experience through multi-turn interactions, yet evaluating CRS remains challenging. User simulators can provide comprehensive evaluations through interactions with CRS, but building realistic and diverse simulators is difficult. While recent work leverages large language models (LLMs) to simulate user interactions, they still fall short in emulating individual real users across diverse scenarios and lack explicit rating mechanisms for quantitative evaluation. To address these gaps, we propose RecUserSim, an LLM agent-based user simulator with enhanced simulation realism and diversity while providing explicit scores. RecUserSim features several key modules: a profile module for defining realistic and diverse user personas, a memory module for tracking interaction history and discovering unknown preferences, and a core action module inspired by Bounded Rationality theory that enables nuanced decision-making while generating more fine-grained actions and personalized responses. To further enhance output control, a refinement module is designed to fine-tune final responses. Experiments demonstrate that RecUserSim generates diverse, controllable outputs and produces realistic, high-quality dialogues, even with smaller base LLMs. The ratings generated by RecUserSim show high consistency across different base LLMs, highlighting its effectiveness for CRS evaluation.
CTR-Driven Advertising Image Generation with Multimodal Large Language Models
Authors: Xingye Chen, Wei Feng, Zhenbang Du, Weizhen Wang, Yanyin Chen, Haohan Wang, Linkai Liu, Yaoyu Li, Jinyuan Zhao, Yu Li, Zheng Zhang, Jingjing Lv, Junjie Shen, Zhangang Lin, Jingping Shao, Yuanjie Shao, Xinge You, Changxin Gao, Nong Sang
Venue: WWW
Year: 2025

In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily focus on the aesthetic quality, which may fail to achieve satisfactory online performance. To address this limitation, we explore the use of Multimodal Large Language Models (MLLMs) for generating advertising images by optimizing for Click-Through Rate (CTR) as the primary objective. Firstly, we build targeted pre-training tasks, and leverage a large-scale e-commerce multimodal dataset to equip MLLMs with initial capabilities for advertising image generation tasks. To further improve the CTR of generated images, we propose a novel reward model to fine-tune pre-trained MLLMs through Reinforcement Learning (RL), which can jointly utilize multimodal features and accurately reflect user click preferences. Meanwhile, a product-centric preference optimization strategy is developed to ensure that the generated background content aligns with the product characteristics after fine-tuning, enhancing the overall relevance and effectiveness of the advertising images. Extensive experiments have demonstrated that our method achieves state-of-the-art performance in both online and offline metrics. Our code and pre-trained models are publicly available at: https://github.com/Chenguoz/CAIG.
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
Authors: Sirui Chen, Shen Han, Jiawei Chen, Binbin Hu, Sheng Zhou, Gang Wang, Yan Feng, Chun Chen, Can Wang
Venue: WWW
Year: 2025

Recommender Systems (RS) aim to generate personalized ranked lists for each user and are evaluated using ranking metrics. Although personalized ranking is a fundamental aspect of RS, this critical property is often overlooked in the design of model architectures. To address this issue, we propose Rankformer, a ranking-inspired recommendation model. The architecture of Rankformer is inspired by the gradient of the ranking objective, embodying a unique (graph) transformer architecture --- it leverages global information from all users and items to produce more informative representations and employs specific attention weights to guide the evolution of embeddings towards improved ranking performance. We further develop an acceleration algorithm for Rankformer, reducing its complexity to a linear level with respect to the number of positive instances. Extensive experimental results demonstrate that Rankformer outperforms state-of-the-art methods. The code is available at https://github.com/StupidThree/Rankformer.
Enhanced Zoo Visiting Experience with Participatory Computing in a Wireless Sensor Network
Authors: Xiaoming Chen, Johannes Karlsson, Tim Wark, Ming Li
Venue: WWW
Year: 2025

Zoos, as ''hotspots'' for tourists and visitors, are making positive economic contributions to many countries like Sweden and Australia. In this paper, we investigate how to enrich people's zoo visiting experience with a wireless sensor network and in particular with ''participatory computing'' of the zoo visitors. We proposed a conceptual framework with simulation, where the zoo visitors are allowed to contribute their discoveries and captured information to the wireless sensor network via their mobile devices. The visitors' supplied information is subsequently processed to help to find interesting animals or animal events in the zoo and also to enable a number of interesting services to the visitors. In particular, the visitors' contributions are divided into different levels according to the amount of efforts required by each participatory task. In return to visitors' contributions, they are subsequently offered various services in different levels too such as animal searching, augmented reality service on mobiles, etc.
Semantics-Aware Cookie Purpose Compliance
Authors: Baiqi Chen, Jiawei Lyu, Tingmin Wu, Mohan Baruwal Chhetri, Guangdong Bai
Venue: WWW
Year: 2025

Websites commonly display cookie banners to inform users about the use and purposes of cookies. However, they may still, whether intentionally or unintentionally (e.g., due to third-party libraries imported), mis-declare cookies that may be abused for tracking. In this work, we introduce COOVER (cookie value examiner) to assess the non-compliance between the website-declared purpose and the semantic-intended purpose of cookies (denoted as potential cookie purpose violation ). We advocate that the value of the cookie is a more reliable indicator of its semantic-intended purpose compared to other features such as expiration time. COOVER decomposes the cookie value into primitive segments representing minimal semantic units, and fine-tunes a GPT-3.5 model to automatically interpret their value-inferred semantics. Based on the interpretation, it classifies cookies into four GDPR-defined purposes. COOVER achieves an F1 score of 95%, significantly outperforming other methods. We employ COOVER to analyze Alexa Top 1k websites to understand the status quo of potential cookie purpose violation on the web. Remarkably, out of 15,339 cookies across these websites, only 3.1% quality as truly necessary cookies, while 44.1% of websites suffer from issues of potential purpose violation.
Inclusive and Accessible Bystander Interventions for Technology Facilitated Sexual Violence
Authors: Yimin Chen, Dana McKay, Kate Anderson, Ronny Andrade
Venue: WWW
Year: 2025

Addressing the severity and prevalence of technology facilitated sexual violence against women is a matter of great social importance. It is also imperative that these antiviolence initiatives be inclusive and accessible to avoid perpetuating further systemic injustices. This position paper argues that a bystander intervention approach which engages male allies in combatting TFSV is an underutilized but potentially effective strategy. Furthermore, we highlight the need for meaningful participation from people with disability and neurodiversity in the design and delivery of such initiatives.
Open Local Knowledge Graph Construction from Academic Papers Using Generative Large Language Models
Authors: Haoting Chen, Sergio José Rodríguez Méndez, Pouya Ghiasnezhad Omran
Venue: WWW
Year: 2025

This manuscript introduces paper2lkg, a novel Local Knowledge Graph Construction (KGC) pipeline designed to transform individual academic papers into their structured local Knowledge Graph (KG) representations. The pipeline harnesses Large Language Models (LLMs), particularly generative LLMs, to automate key Natural Language Processing (NLP) tasks in KGC. The constructed local KGs can potentially be used to enrich an existing academic KG that lacks detailed local representations of individual papers or further integrated into new academic KGs through Knowledge Graph Alignment (KGA).
A Cold-start Recommendation System at Kuaishou Designed from the Short-video Perspective
Authors: Gaode Chen, Ruina Sun, Yinjie Jiang, Tianxiang Li, Yinlong Dai, Qifan Shi, Xiaojie Qin, Jialong Fu, Piaoyi Chen, Ronggeng Huang, Na Li, Qi Zhang, Jiang Liang, Han Li, Kun Gai
Venue: WWW
Year: 2025

Existing recommendation systems take the consumer perspective, creating a strong feedback loop and Matthew Effect. This results in the well-known item cold-start problem, which can be summarized as self-unfairness and cross-unfairness. Self-unfairness refers to the recommendation models underestimating the prior estimation of cold-start videos, leading to many cold-start videos not being recommended. Cross-unfairness means that the posterior performance of cold-start videos is underestimated compared to popular videos, leaving many quality cold-start videos lacking competitiveness. In this paper, we shift from the consumer perspective to the short-video perspective in the cold-start recommendation, focusing on flow allocation across all videos, including mechanisms and models. First, we apply the coverage mechanism to ensure that each video receives a certain exposure opportunity to address self-unfairness. Here, we explore the exposure threshold for cold-start videos and propose an item-to-user recommendation model to efficiently find the most appropriate users for each video. Second, we utilize the competition mechanism to ensure that potential quality content in each growth phase receives more flow, addressing cross-unfairness. Specifically, we employ a hierarchical debiasing model to estimate the growth potential and posterior performance of cold-start videos. Besides, we design a ranking scheme that enables cold-start videos to remain competitive when ranked with popular videos. At last, we share our successful experience building a cold-start recommendation system at Kuaishou, validating the effectiveness of both the coverage and competition mechanisms, and demonstrating the practical improvements in terms of consumer experience, producer incentives, and content ecosystem.
BeyondLife: Third-Party Digital Will Application
Authors: Xinzhang Chen, Arash Shaghaghi, Jesse Laeuchli, Salil S. Kanhere
Venue: WWW
Year: 2025

In the digital age, nearly everyone leaves behind a vast array of digital footprints, including social media posts, emails, and files or photos stored in the cloud. Managing these digital assets after death has become an increasingly significant challenge. Major companies like Meta and Google provide limited and rigid options to users that require configuration separately. There are a handful of third-party closed-source services that are complex and do not have features deemed necessary. In this demo paper, we introduce BeyondLife, the first cross-platform solution for managing digital wills and securely handling digital assets after death. It introduces a cross-platform solution for digital wills, addressing limitations in existing services. The system combines a customized encryption scheme, multi-cloud storage, and decentralized architecture to enable secure and granular digital asset management. A prototype is available at https://github.com/Xinzhang-Chen/BeyondLife.
IPdb: A High-Precision IP Level Industry Categorization of Web Services
Authors: Hongxu Chen, Guanglei Song, Zhiliang Wang, Jiahai Yang, Songyun Wu, Jinlei Lin, Lin He, Chenglong Li
Venue: WWW
Year: 2025

IP addresses with web services are crucial in the Internet ecosystem. Classifying these addresses by industry and organization offers valuable insights into the entities utilizing them, enabling more efficient network management and enhanced security. Previous work in website classification and Internet management struggles to offer an IP-level perspective of the industries of web services due to their limited industry categories or potential industry inconsistencies between IP address owners and AS owners. To this end, we present IPdb, an IP-level industry categorization dataset. To construct the dataset, we developed LLMIC, a Large Language Model-based Industry Categorization framework with a precision of nearly 96%. IPdb serves as a labeled database for future endeavors in developing IP-level industry classifiers, encompassing over 200 million IP addresses. Furthermore, our study indicates that 30% ~ 50% of organizations within critical infrastructure industries deploy web servers across multiple ASes. Our study also validates the problem of mismatched granularity in industry categorization at the AS level with 87.83% ASes in IPv4 and 72.96% ASes in IPv6 containing IP addresses from different industries.