ACM Transactions on the Web最新文献_第10页

GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group Alignment GroupAligner：一种基于领域自适应的深度强化学习方法

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-24 DOI: 10.1145/3580509

Li Sun, Yang Du, Shuai Gao, Junda Ye, Feiyang Wang, Fuxin Ren, Mingchen Liang, Yue Wang, Shuhai Wang

{"title":"GroupAligner: A Deep Reinforcement Learning with Domain Adaptation for Social Group Alignment","authors":"Li Sun, Yang Du, Shuai Gao, Junda Ye, Feiyang Wang, Fuxin Ren, Mingchen Liang, Yue Wang, Shuhai Wang","doi":"10.1145/3580509","DOIUrl":"https://doi.org/10.1145/3580509","url":null,"abstract":"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 personal information and high-quality supervision, which is expensive and even impossible in the real-world scenario. Alternatively, we propose to study the problem of social group alignment across different social networks, focusing on the interests of social groups rather than personal information. However, social group alignment is non-trivial and faces significant challenges in both (i) feature inconsistency across different social networks and (ii) group discovery within a social network. To bridge this gap, we present a novel GroupAligner, a deep reinforcement learning with domain adaptation for social group alignment. In GroupAligner, to address the first issue, we propose the cycle domain adaptation approach with the Wasserstein distance to transfer the knowledge from the source social network, aligning the feature space of social networks in the distribution level. To address the second issue, we model the group discovery as a sequential decision process with reinforcement learning in which the policy is parameterized by a proposed proximity-enhanced Graph Neural Network (pGNN) and a GNN-based discriminator to score the reward. Finally, we utilize pre-training and teacher forcing to stabilize the learning process of GroupAligner. Extensive experiments on several real-world datasets are conducted to evaluate GroupAligner, and experimental results show that GroupAligner outperforms the alternative methods for social group alignment.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42206032","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

Community Enhanced Link Prediction in Dynamic Networks 动态网络中社区增强的链路预测

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-24 DOI: 10.1145/3580513

Mukesh Kumar, S. Mishra, S. Singh, Bhaskar Biswas

{"title":"Community Enhanced Link Prediction in Dynamic Networks","authors":"Mukesh Kumar, S. Mishra, S. Singh, Bhaskar Biswas","doi":"10.1145/3580513","DOIUrl":"https://doi.org/10.1145/3580513","url":null,"abstract":"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 uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community enhanced framework is presented in this paper to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using parameterized influence regions of nodes and their contribution in community partitions. Then, a unique feature set is generated using local, global, and quasi-local similarity-based as well as community information-based features. This feature set is further optimized using scoring-based feature selection methods to select only the most relevant features. Finally, four machine learning-based classification models are used for link prediction. The experiments are performed on six well-known dynamic networks and three performance metrics, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41646083","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

Incorporating A Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation 将具有多重隐式反馈的三图神经网络用于社会推荐

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-21 DOI: 10.1145/3580517

Haorui Zhu, Fei Xiong, Hongshu Chen, Xi Xiong, Liang Wang

{"title":"Incorporating A Triple Graph Neural Network with Multiple Implicit Feedback for Social Recommendation","authors":"Haorui Zhu, Fei Xiong, Hongshu Chen, Xi Xiong, Liang Wang","doi":"10.1145/3580517","DOIUrl":"https://doi.org/10.1145/3580517","url":null,"abstract":"Graph neural networks (GNNs) 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 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, rich attributes are often unavailable, which prevents GNNs from being fully effective. Hence, we propose to enrich the model by mining multiple implicit feedback and constructing a triple GCN component. We have noticed that users may be influenced not only by their trusted friends but also by the ratings that already exist. The implicit influence spreads among the item’s previous and potential raters, and do make a difference on future ratings. The implicit influence is analysed on the mechanism of information propagation, and fused with user’s binary implicit attitude, since negative influence propagates as well as the positive one. Furthermore, we leverage explicit feedback, social relationships and multiple implicit feedback in the triple GCN component. Abundant experiments on real-world datasets reveal that our model has improved significantly in the rating prediction task compared with other state-of-the-art methods.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42769823","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: 3

Validation of an improved vision-based web page parsing pipeline 改进的基于视觉的网页解析管道的验证

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-21 DOI: 10.1145/3580519

M. Cormier, R. Cohen, R. Mann, Karyn Moffatt, Daniel Vogel, Mengfei Liu, Shangshang Zheng

{"title":"Validation of an improved vision-based web page parsing pipeline","authors":"M. Cormier, R. Cohen, R. Mann, Karyn Moffatt, Daniel Vogel, Mengfei Liu, Shangshang Zheng","doi":"10.1145/3580519","DOIUrl":"https://doi.org/10.1145/3580519","url":null,"abstract":"In this paper, we present a novel approach to quantitative evaluation of a model for parsing web pages as visual images, intended to provide improvements for users with assistive needs (cognitive or visual deficits, enabling decluttering or zooming and supporting more effective screen reader output). This segmentation-classification pipeline is tested in stages: We first discuss the validation of the segmentation algorithm, showing that our approach produces automated segmentations that are very similar to those produced by real users when making use of a drawing interface to designate edges and regions. We also examine the properties of these ground truth segmentations produced under different conditions. We then describe our Hidden-Markov tree approach for classification and present results which serve provide important validation for this model. The analysis is set against effective choices for dataset and pruning options, measured with respect to manual ground truth labelling of regions. In all, we offer a detailed quantitative validation (focused on complex news pages) of a fully pipelined approach for interpreting web pages as visual images, an approach which enables important advances for users with assistive needs.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49028561","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

Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social Networks 基于图神经网络的在线社交网络信息传播意见领袖研究

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-20 DOI: 10.1145/3580516

Lokesh Jain, R. Katarya, Shelly Sachdeva

{"title":"Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social Networks","authors":"Lokesh Jain, R. Katarya, Shelly Sachdeva","doi":"10.1145/3580516","DOIUrl":"https://doi.org/10.1145/3580516","url":null,"abstract":"Various opportunities are available to depict different domains due to the diverse nature of social networks and researchers' insatiable. An opinion leader is a human entity or cluster of people who can redirect human assessment strategy by intellectual skills in a social network. A more comprehensive range of approaches is developed to detect opinion leaders based on network-specific and heuristic parameters. For many years, deep learning–based models have solved various real-world multifaceted, graph-based problems with high accuracy and efficiency. The Graph Neural Network (GNN) is a deep learning–based model that modernized neural networks’ efficiency by analyzing and extracting latent dependencies and confined embedding via messaging and neighborhood aggregation of data in the network. In this article, we have proposed an exclusive GNN for Opinion Leader Identification (GOLI) model utilizing the power of GNNs to categorize the opinion leaders and their impact on online social networks. In this model, we first measure the n-node neighbor's reputation of the node based on materialized trust. Next, we perform centrality conciliation instead of the input data's conventional node-embedding mechanism. We experiment with the proposed model on six different online social networks consisting of billions of users’ data to validate the model's authenticity. Finally, after training, we found the top-N opinion leaders for each dataset and analyzed how the opinion leaders are influential in information diffusion. The training-testing accuracy and error rate are also measured and compared with the other state-of-art standard Social Network Analysis (SNA) measures. We determined that the GNN-based model produced high performance concerning accuracy and precision.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64063195","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: 8

Improving Conformance of Web Services: A Constraint-based Model-driven Approach 提高Web服务的一致性：一种基于约束的模型驱动方法

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-19 DOI: 10.1145/3580515

Chang-ai Sun, An Fu, Jingting Jia, Meng Li, Jun Han

{"title":"Improving Conformance of Web Services: A Constraint-based Model-driven Approach","authors":"Chang-ai Sun, An Fu, Jingting Jia, Meng Li, Jun Han","doi":"10.1145/3580515","DOIUrl":"https://doi.org/10.1145/3580515","url":null,"abstract":"Web services have been widely used to develop complex distributed software systems in the context of Service Oriented Architecture (SOA). As a standard for describing Web services, the Web Service Description Language (WSDL) provides a universal mechanism to describe the service’s functionalities for the service consumers. However, the current WSDL only provides the description of the interfaces to a Web Service without any restrictions or assumptions on how to properly invoke the service, resulting in divergent understanding of the Web service’s behavior between the service developer and service consumer. A particular challenge is how to make explicit the various behavior assumptions and restrictions of a service (for the user), and make sure that the service implementation conforms to them (for the developer). In this article, we propose a constraint-based model-driven approach to improving the behavior conformance of Web services. In our approach, constraints are introduced in an extended WSDL, called CxWSDL, to formally and explicitly express the implicit restrictions and assumptions on the behavior of a Web service, and then the predefined constraints are used to derive test cases in a model-driven manner to test the service implementation’s conformance to its behavior constraints from the user’s perspective. An empirical study involving four real-life Web services was conducted to evaluate the effectiveness of our approach, and four actual inconsistencies were discovered.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43011202","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

Enhancing Conversational Recommendation Systems with Representation Fusion 用表示融合增强会话推荐系统

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-19 DOI: 10.1145/3577034

Yingxu Wang, Xiaoru Chen, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang

{"title":"Enhancing Conversational Recommendation Systems with Representation Fusion","authors":"Yingxu Wang, Xiaoru Chen, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang","doi":"10.1145/3577034","DOIUrl":"https://doi.org/10.1145/3577034","url":null,"abstract":"Conversational Recommendation Systems (CRSs) aim to improve recommendation performance by utilizing information from a conversation session. A CRS first constructs questions and then asks users for their feedback in each conversation session to refine better recommendation lists to users. The key design of CRS is to construct proper questions and obtain users’ feedback in response to these questions so as to effectively capture user preferences. Many CRS works have been proposed; however, they suffer from defects when constructing questions for users to answer: (1) employing a dialogue policy agent for constructing questions is one of the most common choices in CRS, but it needs to be trained with a huge corpus, and (2) it is not appropriate that constructing questions from a single policy (e.g., a CRS only selects attributes that the user has interacted with) for all users with different preferences. To address these defects, we propose a novel CRS model, namely a Representation Fusion–based Conversational Recommendation model, where the whole conversation session is divided into two subsessions (i.e., Local Question Search subsession and Global Question Search subsession) and two different question search methods are proposed to construct questions in the corresponding subsessions without employing policy agents. In particular, in the Local Question Search subsession we adopt a novel graph mining method to find questions, where the paths in the graph between users and attributes can eliminate irrelevant attributes; in the Global Question Search subsession we propose to initialize user preference on items with the user and all item historical rating records and construct questions based on user’s preference. Then, we update the embeddings independently over the two subsessions according to user’s feedback and fuse the final embeddings from the two subsessions for the recommendation. Experiments on three real-world recommendation datasets demonstrate that our proposed method outperforms five state-of-the-art baselines.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43875123","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

BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction 行为网:用于动态链接预测的细粒度行为感知网络

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-19 DOI: 10.1145/3580514

Mingyi Liu, Zhiying Tu, Tonghua Su, Xianzhi Wang, Xiaofei Xu, Zhongjie Wang

{"title":"BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link Prediction","authors":"Mingyi Liu, Zhiying Tu, Tonghua Su, Xianzhi Wang, Xiaofei Xu, Zhongjie Wang","doi":"10.1145/3580514","DOIUrl":"https://doi.org/10.1145/3580514","url":null,"abstract":"Dynamic link prediction has become a trending research subject because of its wide applications in 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 of graphs evolves over time. A common approach is to represent a dynamic graph as a collection of discrete snapshots, in which information over a period is aggregated through summation or averaging. This way results in some fine-grained time-related information loss, which further leads to a certain degree of performance degradation. We conjecture that such fine-grained information is vital because it implies specific behavior patterns of nodes and edges in a snapshot. To verify this conjecture, we propose a novel fine-grained behavior-aware network (BehaviorNet) for dynamic network link prediction. Specifically, BehaviorNet adapts a transformer-based graph convolution network to capture the latent structural representations of nodes by adding edge behaviors as an additional attribute of edges. GRU is applied to learn the temporal features of given snapshots of a dynamic network by utilizing node behaviors as auxiliary information. Extensive experiments are conducted on several real-world dynamic graph datasets, and the results show significant performance gains for BehaviorNet over several state-of-the-art (SOTA) discrete dynamic link prediction baselines. Ablation study validates the effectiveness of modeling fine-grained edge and node behaviors.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44359751","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: 2

A Dual-Channel Semi-Supervised Learning Framework on Graphs via Knowledge Transfer and Meta-Learning 基于知识转移和元学习的图的双通道半监督学习框架

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-18 DOI: 10.1145/3577033

Ziyue Qiao, Pengyang Wang, P. Wang, Zhiyuan Ning, Yanjie Fu, Yi Du, Yuanchun Zhou, Jianqiang Huang, Xiansheng Hua, H. Xiong

{"title":"A Dual-Channel Semi-Supervised Learning Framework on Graphs via Knowledge Transfer and Meta-Learning","authors":"Ziyue Qiao, Pengyang Wang, P. Wang, Zhiyuan Ning, Yanjie Fu, Yi Du, Yuanchun Zhou, Jianqiang Huang, Xiansheng Hua, H. Xiong","doi":"10.1145/3577033","DOIUrl":"https://doi.org/10.1145/3577033","url":null,"abstract":"This paper 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 poor balance of generalization and fitting ability due to the heavy reliance on labels or task-agnostic unsupervised information. To address the challenge, we propose a dual-channel framework for semi-supervised learning on Graphs via Knowledge Transfer between independent supervised and unsupervised embedding spaces, namely GKT. Specifically, we devise a dual-channel framework including a supervised model for learning the label probability of nodes and an unsupervised model for extracting information from massive unlabeled graph data. A knowledge transfer head is proposed to bridge the gap between the generalization and fitting capability of the two models. We use the unsupervised information to reconstruct batch-graphs to smooth the label probability distribution on the graphs to improve the generalization of prediction. We also adaptively adjust the reconstructed graphs by encouraging the label-related connections to solidify the fitting ability. Since the optimization of the supervised channel with knowledge transfer contains that of the unsupervised channel as a constraint and vice versa, we then propose a meta-learning-based method to solve the bi-level optimization problem, which avoids the negative transfer and further improves the model’s performance. Finally, extensive experiments validate the effectiveness of our proposed framework by comparing state-of-the-art algorithms.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41564761","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

Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and Anxiety 进入未知:探索搜索引擎对抑郁和焦虑用户的反应

IF 3.5 4区计算机科学

ACM Transactions on the Web Pub Date : 2023-01-18 DOI: 10.1145/3580283

Ashlee Milton, M. S. Pera

{"title":"Into the Unknown: Exploration of Search Engines’ Responses to Users with Depression and Anxiety","authors":"Ashlee Milton, M. S. Pera","doi":"10.1145/3580283","DOIUrl":"https://doi.org/10.1145/3580283","url":null,"abstract":"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 popular SE, like Google or Bing. Every step of the search process for better or worse has the potential to influence a searcher’s mindset. In this work, we empirically investigate what subliminal stimulus SE present to these vulnerable individuals during their searches. For this, we use synthetic queries to produce associated query suggestions and search engine results pages. Then, we infer the subliminal stimulus present in text from SE, i.e., query suggestions, snippets, and web resources. Findings from our empirical analysis reveal that the subliminal stimulus displayed by SE at different stages of the information seeking process differ between MHD searchers and our control group comprised of ”average” SE users. Outcomes from this work showcase open problems related to query suggestions, search engine result pages, and ranking, that the information retrieval community needs to address so that SE can better support individuals with MHD.","PeriodicalId":50940,"journal":{"name":"ACM Transactions on the Web","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49211440","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