2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology最新文献_第4页

CAViAR: Context Aware Visual Indoor Augmented Reality for a University Campus CAViAR:大学校园环境感知视觉室内增强现实

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.99

Buti Al Delail, L. Weruaga, M. Zemerly

引用次数: 46

Rating Prediction by Correcting User Rating Bias 通过修正用户评分偏差进行评分预测

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.186

Masanao Ochi, Y. Matsuo, Makoto Okabe, R. Onai

{"title":"Rating Prediction by Correcting User Rating Bias","authors":"Masanao Ochi, Y. Matsuo, Makoto Okabe, R. Onai","doi":"10.1109/WI-IAT.2012.186","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.186","url":null,"abstract":"We propose a novel method to improve the prediction accuracy on the rating prediction task by correcting the bias of user ratings. We demonstrate that the manner of user rating and review is biased and that it is necessary to correct this difference for more accurate prediction. Our proposed method comprises approaches based on the detection of each user value to ratings: The bias of the rating is detected using entropy of user rating and by updating word weights only when the words appear in the review, the problem of bias is reduced. We implement this idea by extending the Prank algorithm. We apply a review -- item matrix as a feature matrix instead of a user -- item matrix because of its volume of information. Our quantitative evaluation shows that our method improves the prediction accuracy (the Rank Loss measurement) significantly by 8.70 % compared with the normal Prank algorithm. Our proposed method helps users find out what they care about when buying something, and is applicable to newer variants of the Prank algorithm. Moreover, it is useful to most review sites because we use only rating and review data.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130222678","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

Agent-Based Modeling and Simulation of Multi-organizational Interactions in International Trade 基于agent的国际贸易多组织互动建模与仿真

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.40

Jie Jiang, Virginia Dignum, Shengyu Wu, Yao-Hua Tan

引用次数: 1

NoobLab: An Intelligent Learning Environment for Teaching Programming NoobLab:一个用于编程教学的智能学习环境

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.218

P. Neve, G. Hunter, D. Livingstone, J. Orwell

引用次数: 14

Challenges and Opportunities for Trust Management in Crowdsourcing 众包环境下信任管理的挑战与机遇

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.104

Han Yu, Zhiqi Shen, C. Miao, Bo An

{"title":"Challenges and Opportunities for Trust Management in Crowdsourcing","authors":"Han Yu, Zhiqi Shen, C. Miao, Bo An","doi":"10.1109/WI-IAT.2012.104","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.104","url":null,"abstract":"Crowd sourcing (CS) systems offer a new way for businesses and individuals to leverage on the power of mass collaboration to accomplish complex tasks in a divide-and-conquer manner. In existing CS systems, no facility has been provided for analyzing the trustworthiness of workers and providing decision support for allocating tasks to workers, which leads to high dependency of the quality of work on the behavior of workers in CS systems as shown in this paper. To address this problem, trust management mechanisms are urgently needed. Traditional trust management techniques are focused on identifying the most trustworthy service providers (SPs) as accurately as possible. Little thoughts were given to the question of how to utilize these SPs due to two common assumptions: 1) an SP can serve an unlimited number of requests in one time unit, and 2) a service consumer (SC) only needs to select one SP for interaction to complete a task. However, in CS systems, these two assumptions are no longer valid. Thus, existing models cannot be directly used for trust management in CS systems. This paper takes the first step towards a systematic investigation of trust management in CS systems by extending existing trust management models for CS trust management and conducting extensive experiments to study and analyze the performance of various trust management models in crowd sourcing. In this paper, the following key contributions are made. We 1) propose extensions to existing trust management approaches to enable them to operate in CS systems, 2) design a simulation test-bed based on the system characteristics of Amazon's Mechanical Turk (AMT) to make evaluation close to practical CS systems, 3) discuss the effect of incorporating trust management into CS system on the overall social welfare, and 4) identify the challenges and opportunities for future trust management research in CS systems.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122320974","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: 72

E-rank: A Structural-Based Similarity Measure in Social Networks E-rank:社会网络中基于结构的相似性度量

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.111

Mingxi Zhang, Zhenying He, Hao Hu, Wei Wang

引用次数: 15

Selective Behavior in Online Social Networks 在线社交网络中的选择性行为

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.42

Chunjing Xiao, Ling Su, J. Bi, Yuxia Xue, A. Kuzmanovic

{"title":"Selective Behavior in Online Social Networks","authors":"Chunjing Xiao, Ling Su, J. Bi, Yuxia Xue, A. Kuzmanovic","doi":"10.1109/WI-IAT.2012.42","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.42","url":null,"abstract":"According to the classical communication theories, known as Gate keeping and Selective Exposure, individuals tend to have selective behavior when they disseminate and receive information based on their psychological preferences. Selective behavior related to these two theories have been broadly studied separately. While, thanks to the advent of Online Social Networks (OSNs), larger-scale feedback and user information can be collected. In this paper, based on these data, We analyze the correlation among users' properties (such as age, gender, and cultural background) and analyze their selective behavior by tagging users as disseminators and/or audiences in YouTube, Flickr, and Twitter. We find that despite enormous amount of content available in OSNs, users have a comparatively small selective range and do exhibit selective behavior properties. In particular, they pay the most attention to the content published by disseminators that share similar properties, i.e., gender, age, and country. Nonetheless, we also find significant differences and commonalities among the three OSNs with respect to selective behavior. In particular, (i) the proportion and properties of disseminators, audiences, and dual-role users are quite different for the three networks, (ii) the global level of information spread in Flickr is almost two times than that in Twitter and YouTube is approximately the median one, (iii) For a given country, the global level of information spread is different for different OSNs. For a given OSN, it is different for different countries, (iv) despite ubiquitous presence of dual-role users in OSNs, most of such users are very active as either disseminators or audiences, but not both. Our findings are not only useful for understanding these two theories, but also have applications ranging from advertising and recommendation systems to developing predicting models.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121208779","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: 11

A Preliminary Experimental Analysis on Combinatorial Auction-Based Electric Power Allocation for Manufacturing Industries 基于组合拍卖的制造业电力分配初步实验分析

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.221

Naoki Fukuta, Takayuki Ito

引用次数: 8

Approximating Linear Order Inference in OWL 2 DL by Horn Compilation 基于Horn编译的owl2线性顺序推理逼近

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.11

Jianfeng Du, G. Qi, Jeff Z. Pan, Yi-Dong Shen

引用次数: 1

TaxoLearn: A Semantic Approach to Domain Taxonomy Learning taxollearn:领域分类学习的语义方法

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology Pub Date : 2012-12-04 DOI: 10.1109/WI-IAT.2012.129

Emmanuelle-Anna Dietz, Damir Vandic, F. Frasincar

{"title":"TaxoLearn: A Semantic Approach to Domain Taxonomy Learning","authors":"Emmanuelle-Anna Dietz, Damir Vandic, F. Frasincar","doi":"10.1109/WI-IAT.2012.129","DOIUrl":"https://doi.org/10.1109/WI-IAT.2012.129","url":null,"abstract":"Building domain taxonomies is a crucial task in the domain of ontology construction. Domain taxonomy learning keeps getting more important as a form of automatically obtaining a knowledge representation of a certain domain. The alternative of manually developing domain taxonomies is not trivial. The main issues encountered when manually developing a taxonomy are the non-availability of a domain knowledge expert and the considerable amount of effort needed for this task. This paper proposes Taxo Learn, an approach to automatic construction of domain taxonomies. Taxo Learn is a new methodology that combines aspects from existing approaches, but also contains new steps in order to improve the quality of the resulted domain taxonomy. The contribution of this paper is threefold. First, we employ a word sense disambiguation step when detecting concepts in the text. Second, we show the use of semantics-based hierarchical clustering for the purpose of taxonomy learning. Third, we propose a novel dynamic labeling procedure for the concept clusters. We evaluate our approach by comparing the machine generated taxonomy with a manually constructed golden taxonomy. Based on a corpus of documents in the field of financial economics, Taxo Learn shows a high precision for the learned taxonomic concept relationships.","PeriodicalId":220218,"journal":{"name":"2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology","volume":"2017 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116404","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: 24