OpenRaiser/Intern-Atlas huggingface dataset:
SatGuard: Concealing Endless and Bursty Packet Losses in LEO Satellite Networks for Delay-Sensitive Web Applications
Authors: Jihao Li, Hewu Li, Zeqi Lai, Qian Wu, Yijie Liu, Qi Zhang, Yuanjie Li, Jun Liu
Venue: WWW
Year: 2024

Delay-sensitive Web services are crucial applications in emerging low-earth orbit (LEO) satellite networks (LSNs). However, our real-world measurement study based on SpaceX's Starlink, the most widely used commercial LSN today, reveals that the endless and bursty packet losses over unstable LEO satellite links impose significant challenges on guaranteeing the quality of experience (QoE) of Web applications. We propose SatGuard, a distributed in-orbit loss recovery mechanism that can reduce user-perceived delay by completely concealing packet losses in the unstable and lossy LSN environment from endpoints. Specifically, SatGuard adopts a series of techniques to: (i) correctly migrate on-board packet buffer to support link-local retransmission under LEO dynamics; (ii) efficiently detect packet losses on satellite links; and (iii) ensure packet ordering for endpoints. We implement a SatGuard prototype, and conduct extensive trace-driven evaluations guided by public constellation information and real-world measurements. Our experiments demonstrate that, in comparison with other state-of-the-art approaches, SatGuard can significantly improve Web-based QoE, by reducing: (i) up to 48.3% of page load time for Web browsing; and (ii) up to 57.4% end-to-end communication delay for WebRTC.
Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework
Authors: Xiping Li, Jianghong Ma, Kangzhe Liu, Shanshan Feng, Haijun Zhang, Yutong Wang
Venue: WWW
Year: 2024

In recent years, the video game industry has experienced substantial growth, presenting players with a vast array of game choices. This surge in options has spurred the need for a specialized recommender system tailored for video games. However, current video game recommendation approaches tend to prioritize accuracy over diversity, potentially leading to unvaried game suggestions. In addition, the existing game recommendation methods commonly lack the ability to establish strict connections between games to enhance accuracy. Furthermore, many existing diversity-focused methods fail to leverage crucial item information, such as item category and popularity during neighbor modeling and message propagation. To address these challenges, we introduce a novel framework, called CPGRec, comprising three modules, namely accuracy-driven, diversity-driven, and comprehensive modules. The first module extends the state-of-the-art accuracy-focused game recommendation method by connecting games in a more stringent manner to enhance recommendation accuracy. The second module connects neighbors with diverse categories within the proposed game graph and harnesses the advantages of popular game nodes to amplify the influence of long-tail games within the player-game bipartite graph, thereby enriching recommendation diversity. The third module combines the above two modules and employs a new negative-sample rating score reweighting method to balance accuracy and diversity. Experimental results on the Steam dataset demonstrate the effectiveness of our proposed method in improving game recommendations. The dataset and source codes are anonymously released at: https://github.com/CPGRec2024/CPGRec.git.
Stable-Sketch: A Versatile Sketch for Accurate, Fast, Web-Scale Data Stream Processing
Authors: Weihe Li, Paul Patras
Venue: WWW
Year: 2024

Data stream processing plays a pivotal role in various web-related applications, including click fraud detection, anomaly identification, and recommendation systems. Accurate and fast detection of items relevant to such tasks within data streams, e.g., heavy hitters, heavy changers, and persistent items, is however non-trivial. This is due to growing streaming speeds, limited fast memory (L1 cache) available in current systems, and highly skewed item distributions encountered in practice. In effect, items of interest that are tracked only based on their features (e.g., item frequency or persistence value) are susceptible to replacement by non-relevant ones, leading to modest detection accuracy, as we reveal. In this work, we introduce the notion of bucket stability, which quantifies the degree of recorded item variation, and show that this is a powerful metric for identifying distinct item types. We propose Stable-Sketch, an elegant and versatile sketch that exploits multidimensional information, including item statistics and bucket stability, and adopts a stochastic approach to drive replacement decisions. We present a theoretical analysis of the error bounds of Stable-Sketch, and conduct extensive experiments to demonstrate that our solution achieves substantially higher accuracy and faster processing speeds than state-of-the-art sketches in a range of item detection tasks, even with tight memories. We further enhance Stable-Sketch's update throughput with Single Instruction Multiple Data (SIMD) instructions and implement our solution with P4, demonstrating real world deployment viability.
Full-stage Diversified Recommendation: Large-scale Online Experiments in Short-video Platform
Authors: Nian Li, Yunzhu Pan, Chen Gao, Depeng Jin, Qingmin Liao
Venue: WWW
Year: 2024

The recommender systems on online platforms assist users in finding personalized information, yet this also leads to the issue of limited diversity, potentially giving rise to societal issues such as filter bubbles. Despite significant progress in diversified recommendation algorithms, they have not been extensively experimented with and evaluated for effectiveness in large-scale, full-stage industrial recommender systems. Specifically, industrial recommenders usually consist of three stages of matching, ranking, and re-ranking, in which specific characteristics lead to critical challenges for promoting both recommendation diversity and user engagement. First, user interests are partially observed due to only relevance maximization. Second, item-side feature-aware bias causes imbalanced recommendations. Last, the impact of diversity perception on user engagement stresses the necessity of explicit diversity modeling. To address these challenges in industrial systems, in this work, we deploy several existing diversified algorithms in a real-world short-video platform, including exploration-exploitation, feature-aware debiasing, and diversity optimization. We conduct large-scale online A/B testing for evaluation via online metrics of user engagement and recommendation diversity. Performance improvement across full stages demonstrates the effectiveness of these simple solutions. From comparing performance across different stages and algorithms, we identify that the ranking stage is the most suitable for real-world deployment, and the combination of debiasing and diversity optimization is a promising direction in terms of diversified recommendations. This work provides experiential guidance for the large-scale deployment of diversified algorithms and the construction of a more inclusive platform on the Web.
SPRING: Improving the Throughput of Sharding Blockchain via Deep Reinforcement Learning Based State Placement
Authors: Pengze Li, Mingxuan Song, Mingzhe Xing, Zhen Xiao, Qiuyu Ding, Shengjie Guan, Jieyi Long
Venue: WWW
Year: 2024

Sharding provides an opportunity to overcome the inherent scalability challenges of the blockchain, which is the infrastructure for the next generation of the Web. In a sharding blockchain, the state is partitioned into smaller groups known as "shards." Since the states are placed on different shards, cross-shard transactions are inevitable, which is detrimental to the performance of the sharding blockchain. Existing solutions place states based on heuristic algorithms or redistribute states via graph-partitioning-based methods, which are either less effective or costly. In this paper, we present SPRING, the first deep-reinforcement-learning(DRL)-based sharding framework for state placement. SPRING formulates the state placement as a Markov Decision Process, which considers the cross-shard transaction ratio and workload balancing and employs DRL to learn the effective state placement policy. Experimental results based on real Ethereum transaction data demonstrate the superiority of SPRING compared to other state placement solutions. In particular, it decreases the cross-shard transaction ratio by up to 26.63% and boosts throughput by up to 36.03%, all without unduly sacrificing the workload balance among shards. Moreover, updating the training model and making decisions takes only 0.1s and 0.002s, respectively, which shows the overhead is acceptable.
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Authors: Yushen Li, Jinpeng Wang, Tao Dai, Jieming Zhu, Jun Yuan, Rui Zhang, Shu-Tao Xia
Venue: WWW
Year: 2024

Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference context to enhance the prediction. To make up for such deficiency, this paper develops a Retrieval-Augmented Transformer (RAT), aiming to acquire fine-grained feature interactions within and across samples. By retrieving similar samples, we construct augmented input for each target sample. We then build Transformer layers with cascaded attention to capture both intra- and cross-sample feature interactions, facilitating comprehensive reasoning for improved CTR prediction while retaining efficiency. Extensive experiments on real-world datasets substantiate the effectiveness of RAT and suggest its advantage in long-tail scenarios. The code will be open-sourced.
Scenario-Driven Cyber-Physical-Social System: Intelligent Workflow Generation Based on Capability
Authors: Yi Li, Xinkui Zhao, Chen Chen, Shengye Pang, Zhengyang Zhou, Jianwei Yin
Venue: WWW
Year: 2024

Cyber-Physical-Social System (CPSS) is a pioneering solution in Crowd Computing, which integrates heterogeneous resources from cyber, physical, and social spaces, possessing collaborative capabilities in perception, computation, and control. However, existing CPSSs usually confine their functionality to rigid scenarios or tasks, and often oversimplify human resource modeling that fails to dynamically recognize human capabilities. In this work, we propose a Scenario-Driven CPSS to enable an adaptive resource choreography across scenarios. More concretely, we leverage temporal environments to identify events and disassemble these events into workflows, triggering the execution of corresponding capability units, where the capability units abstract the shared functionality of heterogeneous resource groups. Meanwhile, we improve the pre-assuming human capabilities to construct the relationship between human and their capabilities during the execution of workflows, sufficiently promoting human intelligence. Our real-world demo on fire rescue demonstrates the effectiveness of the solution.
Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
Authors: Wenyu Li, Yinuo Zhu, Xin Lin, Ming Li, Ziyue Jiang, Ziqian Zeng
Venue: WWW
Year: 2024

Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales.
Understanding GDPR Non-Compliance in Privacy Policies of Alexa Skills in European Marketplaces
Authors: Song Liao, Mohammed Aldeen, Jingwen Yan, Long Cheng, Xiapu Luo, Haipeng Cai, Hongxin Hu
Venue: WWW
Year: 2024

Amazon Alexa is one of the largest Voice Personal Assistant (VPA) platforms and it allows third-party developers to publish their voice apps, named skills, to the Alexa skill store. To satisfy the needs of European users, Amazon Alexa has established multiple skill marketplaces in Europe and allows developers to publish skills in their native languages. Skills in European marketplaces are required to comply with GDPR (General Data Protection Regulation), which imposes strict obligations on data collection and processing. Skills that involve data collection should provide a privacy policy to disclose the data practice to users and meet GDPR requirements. In this work, we analyze the privacy policies of skills in European marketplaces, focusing on whether skills' privacy policies and data collection behaviors comply with GDPR. We collect a large-scale dataset that includes skills in all European marketplaces with privacy policies. To classify whether a sentence in a privacy policy provides GDPR information, we gather a labeled dataset including skills' privacy policy sentences and use it to train a BERT model. Then, we analyze the GDPR compliance of European skills. Using a dynamic testing tool based on ChatGPT, we check whether skills' privacy policies comply with GDPR and are consistent with the actual data collection behaviors. Surprisingly, we find that 67% of the privacy policies fail to comply with GDPR and don't provide necessary GDPR-related information. For 1,187 skills with data collection behaviors, we observe that 603 skills (50.8%) don't provide a complete privacy policy and 1,128 skills (95%) have GDPR non-compliance issues in their privacy policies. Meanwhile, we find that the GDPR has a positive influence on European privacy policies.
Phishing Vs. Legit: Comparative Analysis of Client-Side Resources of Phishing and Target Brand Websites
Authors: Kyungchan Lim, Jaehwan Park, Doowon Kim
Venue: WWW
Year: 2024

Phishing attacks have persistently remained a prevalent and widespread cybersecurity threat for several years. This leads to numerous endeavors aimed at comprehensively understanding the phishing attack ecosystem, with a specific focus on presenting new attack tactics and defense mechanisms against phishing attacks. Unfortunately, little is known about how client-side resources (e.g., JavaScript libraries) are used in phishing websites, compared to those in their corresponding legitimate target brand websites. This understanding can help us gain insights into the construction and techniques of phishing websites and phishing attackers' behaviors when building phishing websites. In this paper, we gain a deeper understanding of how client-side resources (especially, JavaScript libraries) are used in phishing websites by comparing them with the resources used in the legitimate target websites. For our study, we collect both client-side resources from phishing websites and their corresponding legitimate target brand websites for 25 months: 3.4M phishing websites (1.1M distinct phishing domains). Our study reveals that phishing websites tend to employ more diverse JavaScript libraries than their legitimate websites do. However, these libraries in phishing websites are older (nearly 21.2 months) and distinct in comparison. For example, Socket.IO is uniquely used in phishing websites to send victims' information to an external server in real time. Furthermore, we find that a considerable portion of them still maintain a basic and simplistic structure (e.g., simply displaying a login form or image), while phishing websites have significantly evolved to bypass anti-phishing measures. Finally, through HTML structure and style similarities, we can identify specific target webpages of legitimate brands that phishing attackers reference and use to mimic for their phishing attacks.
Tel2Veh: Fusion of Telecom Data and Vehicle Flow to Predict Camera-Free Traffic via a Spatio-Temporal Framework
Authors: ChungYi Lin, Shen-Lung Tung, Hung-Ting Su, Winston H. Hsu
Venue: WWW
Year: 2024

Predicting vehicle flow is crucial for traffic management but is often limited by the scope of sensors. In contrast, extensive mobile network coverage enables us to utilize counts of mobile users' network activities (cellular traffic) on roadways as a proxy for vehicle flow. However, cellular traffic counts, which encompass various user types, may not directly align with vehicle flow. To address this issue, we present a new task: utilizing cellular traffic to predict vehicle flow in camera-free areas. This is supported by our Tel2Veh dataset, which comprises extensive cellular traffic and sparse vehicle flows. To tackle this task, we propose a two-stage framework. It first independently extracts features from multimodal data, and then integrates them using a graph neural network (GNN)-based fusion to generate predictions of vehicle flow in camera-free areas. We pioneer the fusion of telecom and vision-based data, paving the way for future expansions in web-based applications and systems.
Information Retrieval Meets Large Language Models
Authors: Zheng Liu, Yujia Zhou, Yutao Zhu, Jianxun Lian, Chaozhuo Li, Zhicheng Dou, Defu Lian, Jian-Yun Nie
Venue: WWW
Year: 2024

The advent of large language models (LLMs) presents both opportunities and challenges for the information retrieval (IR) community. On one hand, LLMs will revolutionize how people access information, meanwhile the retrieval techniques can play a crucial role in addressing many inherent limitations of LLMs. On the other hand, there are open problems regarding the collaboration of retrieval and generation, the potential risks of misinformation, and the concerns about cost-effectiveness. To seize the critical moment for development, it calls for the joint effort from academia and industry on many key issues, including identification of new research problems, proposal of new techniques, and creation of new evaluation protocols. It has been one year since the launch of ChatGPT in November last year, and the entire community is currently undergoing a profound transformation in techniques. Therefore, this workshop will be a timely venue to exchange ideas and forge collaborations. The organizers, committee members, and invited speakers are composed of a diverse group of researchers coming from leading institutions in the world. This event will be made up of multiple sessions, including invited talks, paper presentations, hands-on tutorials, and panel discussions. All the materials collected for this workshop will be archived and shared publicly, which will present a long-term value to the community.
News-Driven Price Movement Forecasting with Label-Prior Graph Attention
Authors: Yi-Ting Liu, Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2024

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model's efficacy is further validated through experiments on two multi-label datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis.
AI for Materials Innovation: Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principles Simulation
Authors: Bin Liu
Venue: WWW
Year: 2024

Artificial intelligence (AI) based self-learning or self-improving material discovery systems will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principles calculation and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet--triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first principle-based materials design, and the discovered structures could boost the development of photosensitization-related applications, which is one of the typical examples of how AI can be used to accelerate materials innovation and facilitate science development in general.
Bridging the Space Gap: Unifying Geometry Knowledge Graph Embedding with Optimal Transport
Authors: Yuhan Liu, Zelin Cao, Xing Gao, Ji Zhang, Rui Yan
Venue: WWW
Year: 2024

Knowledge Graph Embedding (KGE) is a critical field aiming to transform the elements of knowledge graphs (KGs) into continuous spaces, offering great potential for structured data representation. In contemporary KGE research, the utilization of either hyperbolic or Euclidean space for knowledge graph Embedding is a common practice. However, knowledge graphs encompass diverse geometric data structures, including chains and hierarchies, whose hybrid nature exceeds the capacity of a single embedding space to capture effectively. This paper introduces a novel and highly effective approach called Unified Geometry Knowledge Graph Embedding (UniGE) to address the challenge of representing diverse geometric data in KGs. UniGE stands out as a novel KGE method that seamlessly integrates KGE in both Euclidean and hyperbolic geometric spaces. We introduce an embedding alignment method and fusion strategy, which harnesses optimal transport techniques and the Wasserstein barycenter method. Furthermore, we offer a comprehensive theoretical analysis to substantiate the superiority of our approach, as evident from a more robust error bound. To substantiate the strength of UniGE, we conducted comprehensive experiments on three benchmark datasets. The results consistently demonstrate that UniGE outperforms state-of-the-art methods, aligning with the conclusions drawn from our theoretical analysis.