ACM Transactions on the Web最新文献_第3页

Adoption of Recurrent Innovations: A Large-Scale Case Study on Mobile App Updates 采用循环创新:手机应用更新的大规模案例研究

4区计算机科学

ACM Transactions on the Web Pub Date : 2023-10-10 DOI: 10.1145/3626189

Fuqi Lin, Xuan Lu, Wei Ai, Huoran Li, Yun Ma, Yulian Yang, Hongfei Deng, Qingxiang Wang, Qiaozhu Mei, Xuanzhe Liu

引用次数: 0

CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation 基于会话推荐的类别感知无损异构超图神经网络

4区计算机科学

ACM Transactions on the Web Pub Date : 2023-10-04 DOI: 10.1145/3626569

Yutao Ma, Zesheng Wang, Liwei Huang, Jian Wang

{"title":"CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation","authors":"Yutao Ma, Zesheng Wang, Liwei Huang, Jian Wang","doi":"10.1145/3626569","DOIUrl":"https://doi.org/10.1145/3626569","url":null,"abstract":"In recent years, session-based recommendation (SBR), which seeks to predict the target user’s next click based on anonymous interaction sequences, has drawn increasing interest for its practicality. The key to completing the SBR task is modeling user intent accurately. Due to the popularity of graph neural networks (GNNs), most state-of-the-art (SOTA) SBR approaches attempt to model user intent from the transitions among items in a session with GNNs. Despite their accomplishments, there are still two limitations. Firstly, most existing SBR approaches utilize limited information from short user-item interaction sequences and suffer from the data sparsity problem of session data. Secondly, most GNN-based SBR approaches describe pairwise relations between items while neglecting complex and high-order data relations. Although some recent studies based on hypergraph neural networks (HGNNs) have been proposed to model complex and high-order relations, they usually output unsatisfactory results due to insufficient relation modeling and information loss. To this end, we propose a category-aware lossless heterogeneous hypergraph neural network (CLHHN) in this article to recommend possible items to the target users by leveraging the category of items. More specifically, we convert each category-aware session sequence with repeated user clicks into a lossless heterogeneous hypergraph consisting of item and category nodes as well as three types of hyperedges, each of which can capture specific relations to reflect various user intents. Then, we design an attention-based lossless hypergraph convolutional network to generate session-wise and multi-granularity intent-aware item representations. Experiments on three real-world datasets indicate that CLHHN can outperform the SOTA models in making a better trade-off between prediction performance and training efficiency. An ablation study also demonstrates the necessity of CLHHN’s key components.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135645181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing 基于进化博弈的边缘云计算会话信息搜索边缘缓存放置策略

4区计算机科学

ACM Transactions on the Web Pub Date : 2023-09-20 DOI: 10.1145/3624985

Hongjian Shi, Meng Zhang, RuHui Ma, Liwei Lin, Rui Zhang, Haibing Guan

{"title":"Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing","authors":"Hongjian Shi, Meng Zhang, RuHui Ma, Liwei Lin, Rui Zhang, Haibing Guan","doi":"10.1145/3624985","DOIUrl":"https://doi.org/10.1145/3624985","url":null,"abstract":"In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this paper, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience OPERA:协调面向任务的对话和信息搜索体验

4区计算机科学

ACM Transactions on the Web Pub Date : 2023-09-11 DOI: 10.1145/3623381

Miaoran Li, Baolin Peng, Jianfeng Gao, Zhu Zhang

引用次数: 3

An Empirical Analysis of Web Storage and its Applications to Web Tracking Web存储的实证分析及其在Web跟踪中的应用

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-09-09 DOI: 10.1145/3623382

Zubair Ahmad, Samuele Casarin, Stefano Calzavara

引用次数: 0

Multi-stage reasoning on introspecting and revising bias for visual question answering 多阶段推理的内省与修正偏见视觉问答

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-08-28 DOI: 10.1145/3616399

Anjin Liu, Zimu Lu, Ning Xu, Min Liu, Chenggang Yan, Bolun Zheng, Bo Lv, Yulong Duan, Zhuang Shao, Xuanya Li

{"title":"Multi-stage reasoning on introspecting and revising bias for visual question answering","authors":"Anjin Liu, Zimu Lu, Ning Xu, Min Liu, Chenggang Yan, Bolun Zheng, Bo Lv, Yulong Duan, Zhuang Shao, Xuanya Li","doi":"10.1145/3616399","DOIUrl":"https://doi.org/10.1145/3616399","url":null,"abstract":"Visual Question Answering (VQA) is a task that involves predicting an answer to a question depending on the content of an image. However, recent VQA methods have relied more on language priors between the question and answer rather than the image content. To address this issue, many debiasing methods have been proposed to reduce language bias in model reasoning. However, the bias can be divided into two categories: good bias and bad bias. Good bias can benefit to the answer predication, while the bad bias may associate the models with the unrelated information. Therefore, instead of excluding good and bad bias indiscriminately in existing debiasing methods, we proposed a bias discrimination module to distinguish them. Additionally, bad bias may reduce the model’s reliance on image content during answer reasoning, and thus attend little on image features updating. To tackle this, we leverage Markov theory to construct a Markov field with image regions and question words as nodes. This helps with feature updating for both image regions and question words, thereby facilitating more accurate and comprehensive reasoning about both the image content and question. To verify the effectiveness of our network, we evaluate our network on VQA v2 and VQA cp v2 datasets and conduct extensive quantity and quality studies to verify the effectiveness of our proposed network. Experimental results show that our network achieves significant performance against the previous state-of-the-art methods.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41312288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Human team behavior and predictability in the massively multiplayer online game WOT Blitz 大型多人在线游戏《坦克世界闪电战》中的人类团队行为和可预测性

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-08-26 DOI: 10.1145/3617509

F. Emmert-Streib, S. Tripathi, M. Dehmer

{"title":"Human team behavior and predictability in the massively multiplayer online game WOT Blitz","authors":"F. Emmert-Streib, S. Tripathi, M. Dehmer","doi":"10.1145/3617509","DOIUrl":"https://doi.org/10.1145/3617509","url":null,"abstract":"Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and non-deterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs their behavior is understudied. In this paper, we investigate the MMOG World of Tanks (WOT) Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insight into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47255856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-Behavior Prediction 面向多行为预测的社会增强异构图卷积网络

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-08-26 DOI: 10.1145/3617510

Lei Zhang, Wuji Zhang, Likang Wu, Ming He, Hongke Zhao

{"title":"SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-Behavior Prediction","authors":"Lei Zhang, Wuji Zhang, Likang Wu, Ming He, Hongke Zhao","doi":"10.1145/3617510","DOIUrl":"https://doi.org/10.1145/3617510","url":null,"abstract":"In recent years, multi-behavior information has been utilized to address data sparsity and cold-start issues. The general multi-behavior models capture multiple behaviors of users to make the representation of relevant features more fine-grained and informative. However, most current multi-behavior recommendation methods neglect the exploration of social relations between users. Actually, users’ potential social connections are critical to assist them in filtering multifarious messages, which may be one key for models to tap deeper into users’ interests. Additionally, existing models usually focus on the positive behaviors (e.g. click, follow and purchase) of users and tend to ignore the value of negative behaviors (e.g. unfollow and badpost). In this work, we present a Multi-Behavior Graph (MBG) construction method based on user behaviors and social relationships, and then introduce a novel socially enhanced and behavior-aware graph neural network for behavior prediction. Specifically, we propose a Socially Enhanced Heterogeneous Graph Convolutional Network (SHGCN) model, which utilizes behavior heterogeneous graph convolution module and social graph convolution module to effectively incorporate behavior features and social information to achieve precise multi-behavior prediction. In addition, the aggregation pooling mechanism is suggested to integrate the outputs of different graph convolution layers, and a dynamic adaptive loss (DAL) method is presented to explore the weight of each behavior. The experimental results on the datasets of the e-commerce platforms (i.e., Epinions and Ciao) indicate the promising performance of SHGCN. Compared with the most powerful baseline, SHGCN achieves 3.3% and 1.4% uplift in terms of AUC on the Epinions and Ciao datasets. Further experiments, including model efficiency analysis, DAL mechanism and ablation experiments, confirm the validity of the multi-behavior information and social enhancement.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43629543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Credibility Estimation of News, User, and Publisher via Role-Relational Graph Convolutional Networks 基于角色关系图卷积网络的新闻、用户和发布者联合可信度估计

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-08-26 DOI: 10.1145/3617418

Anu Shrestha, Jason Duran, Francesca Spezzano, Edoardo Serra

{"title":"Joint Credibility Estimation of News, User, and Publisher via Role-Relational Graph Convolutional Networks","authors":"Anu Shrestha, Jason Duran, Francesca Spezzano, Edoardo Serra","doi":"10.1145/3617418","DOIUrl":"https://doi.org/10.1145/3617418","url":null,"abstract":"The presence of fake news on online social media is overwhelming and is responsible for having impacted several aspects of people’s lives, from health to politics, the economy, and response to natural disasters. Although significant effort has been made to mitigate fake news spread, current research focuses on single aspects of the problem, such as detecting fake news spreaders and classifying stories as either factual or fake. In this paper, we propose a new method to exploit inter-relationships between stories, sources, and final users and integrate prior knowledge of these three entities to jointly estimate the credibility degree of each entity involved in the news ecosystem. Specifically, we develop a new graph convolutional network, namely Role-Relational Graph Convolutional Networks (Role-RGCN), to learn, for each node type (or role), a unique node representation space and jointly connect the different representation spaces with edge relations. To test our proposed approach, we conducted an experimental evaluation on the state-of-the-art FakeNewsNet-Politifact dataset and a new dataset with ground truth on news credibility degrees we collected. Experimental results show a superior performance of our Role-RGCN proposed method at predicting the credibility degree of stories, sources, and users compared to state-of-the-art approaches and other baselines.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45331298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Scraping Relevant Images from Web Pages Without Download 在未下载的情况下从网页中删除相关图像

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-08-19 DOI: 10.1145/3616849

Erdinç Uzun

{"title":"Scraping Relevant Images from Web Pages Without Download","authors":"Erdinç Uzun","doi":"10.1145/3616849","DOIUrl":"https://doi.org/10.1145/3616849","url":null,"abstract":"Automatically scraping relevant images from web pages is an error-prone and time-consuming task, leading experts to prefer manually preparing extraction patterns for a website. Existing web scraping tools are built on these patterns. However, this manual approach is laborious and requires specialized knowledge. Automatic extraction approaches, while a potential solution, require large training datasets and numerous features, including width, height, pixels, and file size, that can be difficult and time-consuming to obtain. To address these challenges, we propose a semi-automatic approach that does not require an expert, utilizes small training datasets, and has a low error rate while saving time and storage. Our approach involves clustering web pages from a website and suggesting several pages for a non-expert to annotate relevant images. The approach then uses these annotations to construct a learning model based on textual data from the HTML elements. In the experiments, we used a dataset of 635,015 images from 200 news websites, each containing 100 pages, with 22,632 relevant images. When comparing several machine learning methods for both automatic approaches and our proposed approach, the AdaBoost method yields the best performance results. When using automatic extraction approaches, the best f-Measure that can be achieved is 0.805 with a learning model constructed from a large training dataset consisting of 120 websites (12,000 web pages). In contrast, our approach achieved an average f-Measure of 0.958 for 200 websites with only six web pages annotated per website. This means that a non-expert only needs to examine 1,200 web pages to determine the relevant images for 200 websites. Our approach also saves time and storage space by not requiring the download of images and can be easily integrated into currently available web scraping tools because it is based on textual data.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43978242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1