2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第6页

“I Recall this Picture”: Understanding Picture Password Selections based on Users’ Sociocultural Experiences “我记得这张照片”:基于用户的社会文化经验理解图片密码选择

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352557

Argyris Constantinides, C. Fidas, Marios Belk, A. Pitsillides

引用次数: 6

Eye Tracking based Cognitive-Centered User Models 基于认知中心用户模型的眼动追踪

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352563

C. Fidas

{"title":"Eye Tracking based Cognitive-Centered User Models","authors":"C. Fidas","doi":"10.1145/3350546.3352563","DOIUrl":"https://doi.org/10.1145/3350546.3352563","url":null,"abstract":"State-of-the-art predictive user models, like Goals, Operators, Methods, and Selection Rules (GOMS) or Keystroke-Level Model (KLM), do not consider human differences in information processing, and do not model user interaction by also considering visual behavior patterns of users during task execution. This can be accredited mainly to insufficient methods and approaches on how to model such interdependencies and thus correlate users’ cognitive characteristics with their interaction and visual behavior during task execution, and ultimately considering such cognitive-centered user models practically within current state-of-the-art information systems’ personalization and adaptation approaches. In this paper, we elaborate on such an endeavor and propose a seven-step, gaze-based human cognitive-centered user model as a basis of synthesizing user cognitive styles along with their interaction and visual behavior patterns. Aiming to prove the validity of the suggested user model, we applied it in the context of an eye tracking study that investigated influences of users’ human cognitive differences on their visual behavior in user authentication within traditional desktop and mixed reality contexts. Initial results of the study are also reported. CCS CONCEPTS • Human-centered computing ~ HCI theory, concepts and models ~ Empirical studies in HCI.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126884585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CluStream-GT: Online Clustering for Personalization in the Health Domain 在运行状况域中用于个性化的在线集群

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352529

E. M. Grua, M. Hoogendoorn, I. Malavolta, P. Lago, A. Eiben

引用次数: 4

An Iterative Distance-Based Model for Unsupervised Weighted Rank Aggregation 基于迭代距离的无监督加权秩聚集模型

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352547

Leonidas Akritidis, Athanasios Fevgas, Panayiotis Bozanis

{"title":"An Iterative Distance-Based Model for Unsupervised Weighted Rank Aggregation","authors":"Leonidas Akritidis, Athanasios Fevgas, Panayiotis Bozanis","doi":"10.1145/3350546.3352547","DOIUrl":"https://doi.org/10.1145/3350546.3352547","url":null,"abstract":"Rank aggregation is a popular problem that combines different ranked lists from various sources (frequently called voters or judges), and generates a single aggregated list with improved ranking of its items. In this context, a portion of the existing methods attempt to address the problem by treating all voters equally. Nevertheless, several related works proved that the careful and effective assignment of different weights to each voter leads to enhanced performance. In this article, we introduce an unsupervised algorithm for learning the weights of the voters for a specific topic or query. The proposed method is based on the fact that if a voter has submitted numerous elements which have been placed in high positions in the aggregated list, then this voter should be treated as an expert, compared to the voters whose suggestions appear in lower places or do not appear at all. The algorithm iteratively computes the distance of each input list with the aggregated list and modifies the weights of the voters until all weights converge. The effectiveness of the proposed method is experimentally demonstrated by aggregating input lists from six TREC conferences.CCS CONCEPTS• Information systems → Rank aggregation; •Theory of computation → Unsupervised learning and clustering.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129557520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Translation-based Embedding Model for Rating Conversion in Recommender Systems 基于翻译的推荐系统评级转换嵌入模型

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352521

Phannakan Tengkiattrakul, Saranya Maneeroj, A. Takasu

{"title":"Translation-based Embedding Model for Rating Conversion in Recommender Systems","authors":"Phannakan Tengkiattrakul, Saranya Maneeroj, A. Takasu","doi":"10.1145/3350546.3352521","DOIUrl":"https://doi.org/10.1145/3350546.3352521","url":null,"abstract":"Ratings, which are explicit feedback, are the most popular form that is often used in Recommender System (RSs). However, using the actual ratings from neighbors to predict ratings of target user toward target item often leads to low accuracy prediction due to the improper rating range problem. Rating conversion methods are proposed to solve this problem over the past few years. To propose rating conversion method, each user’s preference or rating pattern is needed. Some studies adopt the idea from translation-based embedding model and represent user’s preference in graph form. Although some studies represent users, items, and relations in embedding vector form, their representation may be improper and inaccurate if the rating pattern of each user is not in the same range. These vectors still suffer from the improper rating range as well. In this work, we propose a translation-based embedding model with rating conversion in RSs. We aim to solve the improper rating range problem in translation-based embedding model. Our challenges are 1) representing the relation (rating) between a pair of user and item in vector form, instead of scalar form and 2) dealing with rating conversion of user’s rating in vector form. The FilmTrust and MovieLens dataset are used in experiments comparing the proposed method with the existing methods. The evaluation showed that the proposed rating conversion method provides better accuracy results in term of both rating prediction and ranking recommendation.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"329 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117183063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Structural Graph Representations based on Multiscale Local Network Topologies 基于多尺度局部网络拓扑的结构图表示

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352505

Felix Borutta, Julian Busch, Evgeniy Faerman, Adina Klink, Matthias Schubert

{"title":"Structural Graph Representations based on Multiscale Local Network Topologies","authors":"Felix Borutta, Julian Busch, Evgeniy Faerman, Adina Klink, Matthias Schubert","doi":"10.1145/3350546.3352505","DOIUrl":"https://doi.org/10.1145/3350546.3352505","url":null,"abstract":"In many applications, it is required to analyze a graph merely based on its topology. In these cases, nodes can only be distinguished based on their structural neighborhoods and it is common that nodes having the same functionality or role yield similar neighbor-hood structures. In this work, we investigate two problems: (1) how to create structural node embeddings which describe a node’s role and (2) how important the nodes’ roles are for characterizing entire graphs. To describe the role of a node, we explore the structure within the local neighborhood (or multiple local neighborhoods of various extents) of the node in the vertex domain, compute the visiting probability distribution of nodes in the local neighborhoods and summarize each distribution to a single number by computing its entropy. Furthermore, we argue that the roles of nodes are important to characterize the entire graph. Therefore, we propose to aggregate the role representations to describe whole graphs for graph classification tasks. Our experiments show that our new role descriptors outperform state-of-the-art structural node representations that are usually more expensive to compute. Additionally, we achieve promising results compared to advanced state-of-the-art approaches for graph classification on various benchmark datasets often outperforming these approaches.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117344913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Opening the black box of perceived quality: Predicting endorsement on a blog site 打开感知质量的黑盒子:预测博客网站的认可

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352553

Catherine Sotirakou, Damian Trilling, Panagiotis Germanakos, C. Mourlas

引用次数: 1

WoTDL: Web of Things Description Language for Automatic Composition 用于自动合成的物联网描述语言

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352558

Mahda Noura, M. Gaedke

引用次数: 20

Extracting Ego-Centric Social Networks from Linked Open Data 从关联的开放数据中提取以自我为中心的社交网络

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3352570

Raji Ghawi, Mirco Schönfeld, J. Pfeffer

引用次数: 2

Semi-supervised Auto-encoder Based Event Detection in Constructing Knowledge Graph for Social Good 基于半监督自编码器的社会公益知识图谱事件检测

2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2019-10-01 DOI: 10.1145/3350546.3360736

Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng

{"title":"Semi-supervised Auto-encoder Based Event Detection in Constructing Knowledge Graph for Social Good","authors":"Yue Zhao, Xiaolong Jin, Yuanzhuo Wang, Xueqi Cheng","doi":"10.1145/3350546.3360736","DOIUrl":"https://doi.org/10.1145/3350546.3360736","url":null,"abstract":"Knowledge graphs have recently been extensively applied in many different areas (e.g., disaster management and relief, disease diagnosis). For example, event-centric knowledge graphs have been developed to improve decision making in disaster management and relief. This paper focuses on the task of event detection, which is the precondition of event extraction for constructing event-centric knowledge graphs. Event detection identifies trigger words of events in the sentences of a document and further classifies the types of events. It is straightforward that context information is useful for event detection. Therefore, the feature-based methods adopt crosssentence information. However, they suffer from the complication of human-designed features. On the other hand, the representationbased methods learn document-level embeddings, which, however, contain much noise caused by unsupervised learning. To overcome these problems, in this paper we propose a new model based on Semi-supervised Auto-Encoder, which learns Context information to Enhance Event Detection, thus called SAE-CEED. This model first applies large-scale unlabeled texts to pre-train an auto-encoder, so that the embeddings of segments learned by the encoder contain the semantic and order information of the original text. It then uses the decoder to extract the context embeddings and fine-tunes them to enhance a bidirectional neural network model to identify event triggers and their types in sentences. Through experiments on the benchmark ACE-2005 dataset, we demonstrate the effectiveness of the proposed SAE-CEED model. In addition, we systematically conduct a series of experiments to verify the impact of different lengths of text segments in the pre-training of the auto-encoder on event detection.","PeriodicalId":171168,"journal":{"name":"2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115330432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1