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“HOT” ChatGPT: The Promise of ChatGPT in Detecting and Discriminating Hateful, Offensive, and Toxic Comments on Social Media ACM Trans. Web (IF 3.5) Pub Date : 2024-03-12 Lingyao Li, Lizhou Fan, Shubham Atreja, Libby Hemphill
Harmful textual content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to this issue is developing detection models that rely on human annotations. However, the tasks required to build such models expose annotators to harmful and offensive content and may require significant time and cost to complete. Generative AI models have the
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Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. Manipulation ACM Trans. Web (IF 3.5) Pub Date : 2024-03-12 Alisa Rieger, Tim Draws, Mariët Theune, Nava Tintarev
When people use web search engines to find information on debated topics, the search results they encounter can influence opinion formation and practical decision-making with potentially far-reaching consequences for the individual and society. However, current web search engines lack support for information-seeking strategies that enable responsible opinion formation, e.g., by mitigating confirmation
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BNoteHelper: A Note-based Outline Generation Tool for Structured Learning on Video-sharing Platforms ACM Trans. Web (IF 3.5) Pub Date : 2024-03-12 Fangyu Yu, Peng Zhang, Xianghua Ding, Tun Lu, Ning Gu
Usually generated by ordinary users and often not particularly designed for learning, the videos on video-sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing studies attempt to structure the video using video summarization techniques. However, these methods focus on extracting information from within the video
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DeLink: An Adversarial Framework for Defending against Cross-site User Identity Linkage ACM Trans. Web (IF 3.5) Pub Date : 2024-03-12 Peng Zhang, Qi Zhou, Tun Lu, Hansu Gu, Ning Gu
Cross-site user identity linkage (UIL) aims to link the identities of the same person across different social media platforms. Social media practitioners and service providers can construct composite user portraits based on cross-site UIL, which helps understand user behavior holistically and conduct accurate recommendations and personalization. However, many social media users expect each profile
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Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2024-03-08 Guangping Zhang, Dongsheng Li, Hansu Gu, Tun Lu, Ning Gu
The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation
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Fuzzy Influence Maximization in Social Networks ACM Trans. Web (IF 3.5) Pub Date : 2024-03-01 Ahmad Zareie, Rizos Sakellariou
Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: a user is either influenced
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Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web? ACM Trans. Web (IF 3.5) Pub Date : 2024-02-26 Chirag Shah, Emily M. Bender
We observe a recent trend towards applying large language models (LLMs) in search and positioning them as effective information access systems. While the interfaces may look appealing and the apparent breadth of applicability is exciting, we are concerned that the field is rushing ahead with a technology without sufficient study of the uses it is meant to serve, how it would be used, and what its use
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Exif2Vec: A Framework to Ascertain Untrustworthy Crowdsourced Images Using Metadata ACM Trans. Web (IF 3.5) Pub Date : 2024-02-13 Muhammad Umair, Athman Bouguettaya, Abdallah Lakhdari, Mourad Ouzzani, Yuyun Liu
In the context of social media, the integrity of images is often dubious. To tackle this challenge, we introduce Exif2Vec, a novel framework specifically designed to discover modifications in social media images. The proposed framework leverages an image’s metadata to discover changes in an image. We use a service-oriented approach that considers discovery of changes in images as a service. A novel
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Semantic Interaction Matching Network for Few-Shot Knowledge Graph Completion ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Pengfei Luo, Xi Zhu, Tong Xu, Yi Zheng, Enhong Chen
The prosperity of knowledge graphs, as well as related downstream applications, has raised the urgent need for knowledge graph completion techniques that fully support knowledge graph reasoning tasks, especially under the circumstance of training data scarcity. Although large efforts have been made on solving this challenge via few-shot learning tools, they mainly focus on simply aggregating entity
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BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Mingyi Liu, Zhiying Tu, Tonghua Su, Xianzhi Wang, Xiaofei Xu, Zhongjie Wang
Dynamic link prediction has become a trending research subject because of its wide applications in the web, sociology, transportation, and bioinformatics. Currently, the prevailing approach for dynamic link prediction is based on graph neural networks, in which graph representation learning is the key to perform dynamic link prediction tasks. However, there are still great challenges because the structure
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Introduction to the Special Issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications: Part 2 ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Hao Peng, Jian Yang, Jia Wu, Philip S. Yu
No abstract available.
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Contrastive Graph Similarity Networks ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Luzhi Wang, Yizhen Zheng, Di Jin, Fuyi Li, Yongliang Qiao, Shirui Pan
Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a variety of fields, including object tracking, recommender systems, similarity search, and so on. Recent methods for graph similarity learning that utilize deep learning typically share two deficiencies: (1) they leverage graph neural networks as backbones for learning graph
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A Dual-channel Semi-supervised Learning Framework on Graphs via Knowledge Transfer and Meta-learning ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Ziyue Qiao, Pengyang Wang, Pengfei Wang, Zhiyuan Ning, Yanjie Fu, Yi Du, Yuanchun Zhou, Jianqiang Huang, Xian-Sheng Hua, Hui Xiong
This article studies the problem of semi-supervised learning on graphs, which aims to incorporate ubiquitous unlabeled knowledge (e.g., graph topology, node attributes) with few-available labeled knowledge (e.g., node class) to alleviate the scarcity issue of supervised information on node classification. While promising results are achieved, existing works for this problem usually suffer from the
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Heterogeneous Information Crossing on Graphs for Session-Based Recommender Systems ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Xiaolin Zheng, Rui Wu, Zhongxuan Han, Chaochao Chen, Linxun Chen, Bing Han
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users’ personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users’ behaviors in the current session. However
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Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Mei Yu, Kun Zhu, Mankun Zhao, Jian Yu, Tianyi Xu, Di Jin, Xuewei Li, Ruiguo Yu
The task of sequential recommendation aims to predict a user’s preference by analyzing the user’s historical behaviours. Existing methods model item transitions through leveraging sequential patterns. However, they mainly consider the target user’s behaviours and dynamic characteristics, while often ignoring high-order collaborative connections when modelling user preferences. Some recent works try
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Deep Adaptive Graph Clustering via von Mises-Fisher Distributions ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Pengfei Wang, Daqing Wu, Chong Chen, Kunpeng Liu, Yanjie Fu, Jianqiang Huang, Yuanchun Zhou, Jianfeng Zhan, Xiansheng Hua
Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding
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Incorporating a Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Haorui Zhu, Fei Xiong, Hongshu Chen, Xi Xiong, Liang Wang
Graph neural networks have been clearly proven to be powerful in recommendation tasks since they can capture high-order user-item interactions and integrate them with rich attributes. However, they are still limited by the cold-start problem and data sparsity. Using social relationships to assist recommendation is an effective practice, but it can only moderately alleviate these problems. In addition
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Community-enhanced Link Prediction in Dynamic Networks ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Mukesh Kumar, Shivansh Mishra, Shashank Sheshar Singh, Bhaskar Biswas
The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which
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PIDKG: Propagating Interaction Influence on the Dynamic Knowledge Graph for Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2024-01-08 Chunjing Xiao, Wanlin Ji, Yuxiang Zhang, Shenkai Lv
Modeling the dynamic interactions between users and items on knowledge graphs is crucial for improving the accuracy of recommendation. Although existing methods have made great progress in modeling the dynamic knowledge graphs for recommendation, they usually only consider the mutual influence between users and items involved in the interactions, and ignore the influence propagation from the interacting
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Bridging Performance of X (formerly known as Twitter) Users: A Predictor of Subjective Well-Being During the Pandemic ACM Trans. Web (IF 3.5) Pub Date : 2024-01-05 Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang
The outbreak of the COVID-19 pandemic triggered the perils of misinformation over social media. By amplifying the spreading speed and popularity of trustworthy information, influential social media users have been helping overcome the negative impacts of such flooding misinformation. In this article, we use the COVID-19 pandemic as a representative global health crisisand examine the impact of the
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Random Testing and Evolutionary Testing for Fuzzing GraphQL APIs ACM Trans. Web (IF 3.5) Pub Date : 2024-01-05 Asma Belhadi, Man Zhang, Andrea Arcuri
The Graph Query Language (GraphQL) is a powerful language for application programming interface (API) manipulation in web services. It has been recently introduced as an alternative solution for addressing the limitations of RESTful APIs. This article introduces an automated solution for GraphQL API testing. We present a full framework for automated API testing, from the schema extraction to test case
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CLHHN: Category-aware Lossless Heterogeneous Hypergraph Neural Network for Session-based Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2023-11-14 Yutao Ma, Zesheng Wang, Liwei Huang, Jian Wang
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
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Adoption of Recurrent Innovations: A Large-Scale Case Study on Mobile App Updates ACM Trans. Web (IF 3.5) Pub Date : 2023-11-14 Fuqi Lin, Xuan Lu, Wei Ai, Huoran Li, Yun Ma, Yulian Yang, Hongfei Deng, Qingxiang Wang, Qiaozhu Mei, Xuanzhe Liu
Modern technology innovations feature a successive and even recurrent procedure. Intervals between old and new generations of technology are shrinking, and the Internet and Web services have facilitated the fast adoption of an innovation even before the convergence of its predecessor. While the adoption and diffusion of innovations have been studied for decades, most theories and analyses focus on
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Multiresolution Local Spectral Attributed Community Search ACM Trans. Web (IF 3.5) Pub Date : 2023-11-03 Qingqing Li, Huifang Ma, Zhixin Li, Liang Chang
Community search has become especially important in graph analysis task, which aims to identify latent members of a particular community from a few given nodes. Most of the existing efforts in community search focus on exploring the community structure with a single scale in which the given nodes are located. Despite promising results, the following two insights are often neglected. First, node attributes
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A Graph-Based Context-Aware Model to Understand Online Conversations ACM Trans. Web (IF 3.5) Pub Date : 2023-11-03 Vibhor Agarwal, Anthony P. Young, Sagar Joglekar, Nishanth Sastry
Online forums that allow for participatory engagement between users have been transformative for the public discussion of many important issues. However, such conversations can sometimes escalate into full-blown exchanges of hate and misinformation. Existing approaches in natural language processing (NLP), such as deep learning models for classification tasks, use as inputs only a single comment or
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Triangle-oriented Community Detection Considering Node Features and Network Topology ACM Trans. Web (IF 3.5) Pub Date : 2023-11-03 Guangliang Gao, Weichao Liang, Ming Yuan, Hanwei Qian, Qun Wang, Jie Cao
The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views,
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Causality and Correlation Graph Modeling for Effective and Explainable Session-Based Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Huizi Wu, Cong Geng, Hui Fang
Session-based recommendation, which has witnessed a booming interest recently, focuses on predicting a user’s next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to distinguish a causality and correlation
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Scraping Relevant Images from Web Pages without Download ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Erdinç Uzun
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
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Summarizing Web Archive Corpora via Social Media Storytelling by Automatically Selecting and Visualizing Exemplars ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Shawn M. Jones, Martin Klein, Michele C. Weigle, Michael L. Nelson
People often create themed collections to make sense of an ever-increasing number of archived web pages. Some of these collections contain hundreds of thousands of documents. Thousands of collections exist, many covering the same topic. Few collections include standardized metadata. This scale makes understanding a collection an expensive proposition. Our Dark and Stormy Archives (DSA) five-process
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SHGCN: Socially Enhanced Heterogeneous Graph Convolutional Network for Multi-behavior Prediction ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Lei Zhang, Wuji Zhang, Likang Wu, Ming He, Hongke Zhao
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
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Human Team Behavior and Predictability in the Massively Multiplayer Online Game WOT Blitz ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Frank Emmert-Streib, Shailesh Tripathi, Matthias Dehmer
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 nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this
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Joint Credibility Estimation of News, User, and Publisher via Role-relational Graph Convolutional Networks ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Anu Shrestha, Jason Duran, Francesca Spezzano, Edoardo Serra
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
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An Empirical Analysis of Web Storage and Its Applications to Web Tracking ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Zubair Ahmad, Samuele Casarin, Stefano Calzavara
In this article, we present a large-scale empirical analysis of the use of web storage in the wild.By using dynamic taint tracking at the level of JavaScript and by performing an automated classification of the detected information flows, we shed light on the key characteristics of web storage uses in the Tranco Top 10k. Our analysis shows that web storage is routinely accessed by third parties, including
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Understanding Rug Pulls: An In-depth Behavioral Analysis of Fraudulent NFT Creators ACM Trans. Web (IF 3.5) Pub Date : 2023-10-11 Trishie Sharma, Rachit Agarwal, Sandeep Kumar Shukla
The explosive growth of non-fungible tokens (NFTs) on Web3 has created a new frontier for digital art and collectibles and an emerging space for fraudulent activities. This study provides an in-depth analysis of NFT rug pulls, the fraudulent schemes that steal investors’ funds. From a curated dataset of 760 rug pulls across 10 NFT marketplaces, we examine these schemes’ structural and behavioral properties
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Layout Cross-Browser Failure Classification for Mobile Responsive Design Web Applications: Combining Classification Models Using Feature Selection ACM Trans. Web (IF 3.5) Pub Date : 2023-10-10 Willian Massami Watanabe, Danilo Alves dos Santos, Claiton de Oliveira
Cross-browser incompatibilities (XBIs) are defined as inconsistencies that can be observed in Web applications when they are rendered in a specific browser compared to others. These inconsistencies are associated with differences in the way each browser implements its capabilities and renders Web applications. The inconsistencies range from minor layout differences to lack of core functionalities of
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Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product Search ACM Trans. Web (IF 3.5) Pub Date : 2023-10-10 Bin Wu, Zaiqiao Meng, Shangsong Liang
In this article, we study the problem of dynamic personalized product search. Due to the data-sparsity problem in the real world, existing methods suffer from the challenge of data inefficiency. We address the challenge by proposing a Dynamic Bayesian Contrastive Predictive Coding model (DBCPC), which aims to capture the rich structured information behind search records to improve data efficiency.
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Deep Gated Multi-modal Fusion for Image Privacy Prediction ACM Trans. Web (IF 3.5) Pub Date : 2023-10-10 Chenye Zhao, Cornelia Caragea
With the rapid development of technologies in mobile devices, people can post their daily lives on social networking sites such as Facebook, Flickr, and Instagram. This leads to new privacy concerns due to people’s lack of understanding that private information can be leaked and used to their detriment. Image privacy prediction models are developed to predict whether images contain sensitive information
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Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social Networks ACM Trans. Web (IF 3.5) Pub Date : 2023-10-10 Royal Pathak, Francesca Spezzano, Maria Soledad Pera
Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially of interest to their users, given the known biases and filter
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Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing ACM Trans. Web (IF 3.5) Pub Date : 2023-09-20 Hongjian Shi, Meng Zhang, RuHui Ma, Liwei Lin, Rui Zhang, Haibing Guan
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
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OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience ACM Trans. Web (IF 3.5) Pub Date : 2023-09-11 Miaoran Li, Baolin Peng, Jianfeng Gao, Zhu Zhang
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. Towards the goal of constructing a conversational agent that can complete user tasks and support information seeking, it is important to develop a system that can handle both TOD and QA with access to various external knowledge sources. In this work, we propose a new task, Open-Book
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Multi-stage reasoning on introspecting and revising bias for visual question answering ACM Trans. Web (IF 3.5) Pub Date : 2023-08-28 An-An Liu, Zimu Lu, Ning Xu, Min Liu, Chenggang Yan, Bolun Zheng, Bo Lv, Yulong Duan, Zhuang Shao, Xuanya Li
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
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Deep Gated Multi-modal Fusion for Image Privacy Prediction ACM Trans. Web (IF 3.5) Pub Date : 2023-07-22 Chenye Zhao, Cornelia Caragea
With the rapid development of technologies in mobile devices, people can post their daily lives on social networking sites such as Facebook, Flickr, and Instagram. This leads to new privacy concerns due to people’s lack of understanding that private information can be leaked and used to their detriment. Image privacy prediction models are developed to predict whether images contain sensitive information
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Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product Search ACM Trans. Web (IF 3.5) Pub Date : 2023-07-13 Bin Wu, Zaiqiao Meng, Shangsong Liang
In this paper, we study the problem of dynamic personalized product search. Due to the data-sparsity problem in the real world, existing methods suffer from the challenge of data inefficiency. We address the challenge by proposing a Dynamic Bayesian Contrastive Predictive Coding model (DBCPC), which aims to capture the rich structured information behind search records to improve data efficiency. Our
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Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and Anxiety ACM Trans. Web (IF 3.5) Pub Date : 2023-07-11 Ashlee Milton, Maria Soledad Pera
Researchers worldwide have explored the behavioral nuances that emerge from interactions of individuals afflicted by mental health disorders (MHD) with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to a persuasive technology that is part of their everyday lives: web search engines (SE). Each day, users with MHD embark on information seeking journeys using
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Reverse Maximum Inner Product Search: Formulation, Algorithms, and Analysis ACM Trans. Web (IF 3.5) Pub Date : 2023-07-11 Daichi Amagata, Takahiro Hara
The maximum inner product search (MIPS), which finds the item with the highest inner product with a given query user, is an essential problem in the recommendation field. Usually e-commerce companies face situations where they want to promote and sell new or discounted items. In these situations, we have to consider the following questions: Who is interested in the items, and how do we find them? This
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A Novel Review Helpfulness Measure Based on the User-Review-Item Paradigm ACM Trans. Web (IF 3.5) Pub Date : 2023-07-11 Luca Pajola, Dongkai Chen, Mauro Conti, V.S. Subrahmanian
Review platforms are viral online services where users share and read opinions about products (e.g., a smartphone) or experiences (e.g., a meal at a restaurant). Other users may be influenced by such opinions when deciding what to buy. The usability of review platforms is currently limited by the massive number of opinions on many products. Therefore, showing only the most helpful reviews for each
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To Re-experience the Web: A Framework for the Transformation and Replay of Archived Web Pages ACM Trans. Web (IF 3.5) Pub Date : 2023-07-11 John Berlin, Mat Kelly, Michael L. Nelson, Michele C. Weigle
When replaying an archived web page, or memento, the fundamental expectation is that the page should be viewable and function exactly as it did at the archival time. However, this expectation requires web archives upon replay to modify the page and its embedded resources so that all resources and links reference the archive rather than the original server. Although these modifications necessarily change
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Closeness Centrality on Uncertain Graphs ACM Trans. Web (IF 3.5) Pub Date : 2023-07-11 Zhenfang Liu, Jianxiong Ye, Zhaonian Zou
Centrality is a family of metrics for characterizing the importance of a vertex in a graph. Although a large number of centrality metrics have been proposed, a majority of them ignores uncertainty in graph data. In this article, we formulate closeness centrality on uncertain graphs and define the batch closeness centrality evaluation problem that computes the closeness centrality of a subset of vertices
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Summarizing Web Archive Corpora Via Social Media Storytelling By Automatically Selecting and Visualizing Exemplars ACM Trans. Web (IF 3.5) Pub Date : 2023-07-03 Shawn M. Jones, Martin Klein, Michele C. Weigle, Michael L. Nelson
People often create themed collections to make sense of an ever-increasing number of archived web pages. Some of these collections contain hundreds of thousands of documents. Thousands of collections exist, many covering the same topic. Few collections include standardized metadata. This scale makes understanding a collection an expensive proposition. Our Dark and Stormy Archives (DSA) five-process
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Pre-Training Across Different Cities for Next POI Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2023-06-20 Ke Sun, Tieyun Qian, Chenliang Li, Xuan Ma, Qing Li, Ming Zhong, Yuanyuan Zhu, Mengchi Liu
The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across cities to alleviate those data sparsity and imbalance problems for next POI recommendation. Recently, pre-training over a large-scale dataset has achieved great success in many relevant fields, like computer vision
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Layout Cross-Browser Failure Classification for Mobile Responsive Design Web Applications: Combining Classification Models Using Feature Selection ACM Trans. Web (IF 3.5) Pub Date : 2023-06-17 Willian Massami Watanabe, Danilo Alves dos Santos, Claiton de Oliveira
Cross-Browser Incompatibilities - XBIs are defined as inconsistencies that can be observed in Web applications when they are rendered in a specific browser compared to others. These inconsistencies are associated with differences in the way each browser implements their capabilities and render Web applications. The inconsistencies range from minor layout differences to lack of core functionalities
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Privacy Scoring Over OSNs: Shared Data Granularity as a Latent Dimension ACM Trans. Web (IF 3.5) Pub Date : 2023-06-17 Yasir Kilic, Ali Inan
Privacy scoring aims at measuring the privacy violation risk of a user over an online social network (OSN) based on attribute values shared in the user’s OSN profile page and the user’s position in the network. Existing studies on privacy scoring rely on possibly biased or emotional survey data. In this study, we work with real-world data collected from the professional LinkedIn OSN and show that probabilistic
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Causality and Correlation Graph Modeling for Effective and Explainable Session-based Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2023-06-05 Huizi Wu, Cong Geng, Hui Fang
Session-based recommendation which has been witnessed a booming interest recently, focuses on predicting a user’s next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address co-occurrence between items, but fail to well distinguish causality
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A Multi-Task Graph Neural Network with Variational Graph Auto-Encoders for Session-Based Travel Packages Recommendation ACM Trans. Web (IF 3.5) Pub Date : 2023-05-24 Guixiang Zhu, Jie Cao, Lei Chen, Youquan Wang, Zhan Bu, Shuxin Yang, Jianqing Wu, Zhiping Wang
Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure
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Introduction to the Special Issue on Advanced Graph Mining on the Web: Theory, Algorithms, and Applications: Part 1 ACM Trans. Web (IF 3.5) Pub Date : 2023-05-22 Hao Peng, Jian Yang, Jia Wu, Philip S. Yu
No abstract available.
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RoSGAS: Adaptive Social Bot Detection with Reinforced Self-supervised GNN Architecture Search ACM Trans. Web (IF 3.5) Pub Date : 2023-05-22 Yingguang Yang, Renyu Yang, Yangyang Li, Kai Cui, Zhiqin Yang, Yue Wang, Jie Xu, Haiyong Xie
Social bots are referred to as the automated accounts on social networks that make attempts to behave like humans. While Graph Neural Networks (GNNs) have been massively applied to the field of social bot detection, a huge amount of domain expertise and prior knowledge is heavily engaged in the state-of-the-art approaches to design a dedicated neural network architecture for a specific classification
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Niffler: Real-time Device-level Anomalies Detection in Smart Home ACM Trans. Web (IF 3.5) Pub Date : 2023-05-22 Haohua Du, Yue Wang, Xiaoya Xu, Mingsheng Liu
Device-level security has become a major concern in smart home systems. Detecting problems in smart home sytems strives to increase accuracy in near real time without hampering the regular tasks of the smart home. The current state of the art in detecting anomalies in smart home devices is mainly focused on the app level, which provides a basic level of security by assuming that the devices are functioning
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GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group Alignment ACM Trans. Web (IF 3.5) Pub Date : 2023-05-22 Li Sun, Yang Du, Shuai Gao, Junda Ye, Feiyang Wang, Fuxin Ren, Mingchen Liang, Yue Wang, Shuhai Wang
Social network alignment, which aims to uncover the correspondence across different social networks, shows fundamental importance in a wide spectrum of applications such as cross-domain recommendation and information propagation. In the literature, the vast majority of the existing studies focus on the social network alignment at user level. In practice, the user-level alignment usually relies on abundant
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Graph Attention Network for Text Classification and Detection of Mental Disorder ACM Trans. Web (IF 3.5) Pub Date : 2023-05-22 Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava
A serious issue in today’s society is Depression, which can have a devastating impact on a person’s ability to cope in daily life. Numerous studies have examined the use of data generated directly from users using social media to diagnose and detect Depression as a mental illness. Therefore, this paper investigates the language used in individuals’ personal expressions to identify depressive symptoms
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Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems ACM Trans. Web (IF 3.5) Pub Date : 2023-05-22 Qian Li, Jianxin Li, Lihong Wang, Cheng Ji, Yiming Hei, Jiawei Sheng, Qingyun Sun, Shan Xue, Pengtao Xie
Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power supply strategies. However, the limited length of short electrical record texts causes severe information sparsity, and numerous domain-specific terminologies of power systems makes it difficult to transfer knowledge from