2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第6页

Ridiculously Expensive Watches and Surprisingly Many Reviewers: A Study of Irony 贵得离谱的手表和多得惊人的评论者:讽刺的研究

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0131

Pavel Savov, R. Nielek

引用次数: 1

Development and Evaluation of an Operational Service Robot Using Wikipedia-Based and Domain Ontologies 基于维基百科和领域本体的操作服务机器人的开发与评估

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0086

Hiroshi Asano, Takeshi Morita, Takahira Yamaguchi

引用次数: 2

Multi-organizational Access Control Model Based on Mobile Agents for Cloud Computing 基于移动代理的云计算多组织访问控制模型

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0116

Zeineb Ben Yahya, F. Ktata, K. Ghédira

引用次数: 3

Characterization of Football Supporters from Twitter Conversations 从推特对话中分析足球支持者的特征

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0033

D. Pacheco, Diego Pinheiro, Fernando Buarque de Lima-Neto, Eraldo Ribeiro, R. Menezes

{"title":"Characterization of Football Supporters from Twitter Conversations","authors":"D. Pacheco, Diego Pinheiro, Fernando Buarque de Lima-Neto, Eraldo Ribeiro, R. Menezes","doi":"10.1109/WI.2016.0033","DOIUrl":"https://doi.org/10.1109/WI.2016.0033","url":null,"abstract":"Football (aka Soccer) is the most popular sport in the world. The popularity of the sport leads to several stories (some perhaps anecdotal) about supporters behaviors and to the emergence of rivalries such as the famous Barcelona-Real Madrid (in Spain). Little however has been done to characterize/profile online users' behaviors as football supporters and use them as an aggregate measure to club characterization. Today, the availability of data enable us to understand at a much greater scale if rivalries exist and if there are signatures that can be used to characterize supporting behavior. In this paper we use techniques from Data Science to characterize football supporters according to their activity on Twitter and to characterize clubs according to the behavior of their supporters. We show that it is possible to: (i) rank football clubs by their popularity and fans' dislike, (ii) identify the rivalries that exist between clubs and their supporters, and (iii) find specific signatures that repeat themselves across different clubs and in different countries. The results are evaluated on a large dataset of tweets relevant to major football leagues in Brazil and in the United Kingdom.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"52 1","pages":"169-176"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89601827","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: 12

Bayesian Nominal Matrix Factorization for Mining Daily Activity Patterns 基于贝叶斯标称矩阵分解的日常活动模式挖掘

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0054

Chen Li, W. K. Cheung, Jiming Liu, J. Ng

{"title":"Bayesian Nominal Matrix Factorization for Mining Daily Activity Patterns","authors":"Chen Li, W. K. Cheung, Jiming Liu, J. Ng","doi":"10.1109/WI.2016.0054","DOIUrl":"https://doi.org/10.1109/WI.2016.0054","url":null,"abstract":"With the advent of the Internet of things (IoT) and smart sensor technologies, the data-driven paradigm has been found promising to support human behavioral analysis in a smart home for better healthcare and well-being of senior adults. This work focuses on discovering daily activity routines from sensor data collected in a smart home. By representing the sensor data as a matrix, daily activity routines can be identified using matrix factorization methods. The key challenge rests on the fact that the matrix contains discrete labels as its elements, and decomposing the nominal data matrix into basis vectors of the labels is nontrivial. We propose a novel principled methodology to tackle the nominal matrix factorization problem. Assuming that the similarity matrix of the labels is known, the discrete labels are first projected onto a continuous space with the interlabel distance preserving the given similarity matrix of the labels as far as possible. Then, we extend a hierarchical probabilistic model for ordinal matrix factorization with Bayesian Lasso that the factorization can be more robust to noise and more sparse to ease human interpretation. Our experimental results based on a synthetic data set shows that the factorization results obtained using the proposed methodology outperform those obtained using a number of the state-of-the-art factorization methods in terms of the basis vector reconstruction accuracy. We also applied our model to a publicly available smart home data set to illustrate how the proposed methodology can be used to support daily activity routine analysis.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"1 1","pages":"335-342"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88655029","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: 2

Frame Dispatcher: A Multi-frame Classification System for Social Movement by Using Microblogging Data 框架调度:基于微博数据的社会运动多帧分类系统

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0101

Hung-Min Hsu, Wei-Sheng Zeng, Chen-Shuo Hung, Dung-Sheng Chen, R. Chang, Shian-Hua Lin, Jan-Ming Ho

引用次数: 1

ExATO - High Quality Term Extraction for Portuguese and English 高质量的术语提取葡萄牙语和英语

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0092

Lucelene Lopes, Paulo Fernandes, R. Vieira

引用次数: 1

Predicting Depression from Internet Behaviors by Time-Frequency Features 网络行为的时频特征预测抑郁

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0060

Changye Zhu, Baobin Li, Ang Li, T. Zhu

引用次数: 14

A Time Aware Method for Predicting Dull Nodes and Links in Evolving Networks for Data Cleaning 演化网络中钝节点和钝链路预测的时间感知方法

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0050

Niladri Sett, Subhrendu Chattopadhyay, Sanasam Ranbir Singh, Sukumar Nandi

引用次数: 2

A Composite Recommendation System for Planning Tourist Visits 旅游行程规划的综合推荐系统

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0110

Idir Benouaret, D. Lenne

{"title":"A Composite Recommendation System for Planning Tourist Visits","authors":"Idir Benouaret, D. Lenne","doi":"10.1109/WI.2016.0110","DOIUrl":"https://doi.org/10.1109/WI.2016.0110","url":null,"abstract":"Classical recommender systems provide users with ranked lists of recommendations that are relevant to their preferences. Each recommendation consists of a single item, e.g., a movie or a book. However, these ranked lists are not suitable for applications such as travel planning, which deal with heterogeneous items. In fact, in such applications, there is a need to recommend packages the user can choose from, each package being a set of Points of Interest (POIs), e.g., museums, parks, monuments, etc. In this paper, we focus on the problem of recommending a set of packages to the user, where each package is constituted with a set of POIs that may constitute a tour. Given a collection of POIs, where each POI has a cost and a time associated with it, and the user specifying a maximum total value for both the cost and the time (budgets), our goal is to recommend the most interesting packages for the user, where each package satisfies the budget constraints. We formally define the problem and we present a novel composite recommendation system, inspired from composite retrieval. We introduce a scoring function and propose a ranking algorithm that takes into account the preferences of the user, the diversity of POIs included in the package, as well as the popularity of POIs in the package. Extensive experimental evaluation of our proposed system, using a real dataset demonstrates its quality and its ability to improve both diversity and relevance of recommendations.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"30 1","pages":"626-631"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72977567","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: 13