2018 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第8页

Finding Tenuous Groups in Social Networks 在社交网络中寻找脆弱的群体

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00048

Wentan Li

引用次数: 4

When and Where?: Behavior Dominant Location Forecasting with Micro-Blog Streams 何时何地?:基于微博流的行为主导位置预测

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00169

Bhaskar Gautam, Annappa Basava, Abhishek Singh, Amit Agrawal

{"title":"When and Where?: Behavior Dominant Location Forecasting with Micro-Blog Streams","authors":"Bhaskar Gautam, Annappa Basava, Abhishek Singh, Amit Agrawal","doi":"10.1109/ICDMW.2018.00169","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00169","url":null,"abstract":"The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133978403","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

TCENR: A Hybrid Neural Recommender for Location Based Social Networks 基于位置的社交网络的混合神经推荐

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00170

Omer Tal, Yang Liu

引用次数: 2

Leveraging Reconstructive Profiles of Users and Items for Tag-Aware Recommendation 利用用户和项目的重构配置文件进行标签感知推荐

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00184

Zhaoqiang Li, Jiajin Huang, N. Zhong

{"title":"Leveraging Reconstructive Profiles of Users and Items for Tag-Aware Recommendation","authors":"Zhaoqiang Li, Jiajin Huang, N. Zhong","doi":"10.1109/ICDMW.2018.00184","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00184","url":null,"abstract":"It is an effective recommendation method by revealing user preferences and extracting latent semantic information of items through social tag information. Recent research shows impressive recommendation performance by using neural network-based methods to transform tag-based user or item profiles to abstract feature representations. However, in the process of training a neural network, these methods need an more effective measurement to balance the tag-based profiles and the abstract representations to further improve item recommendation. This paper proposes a method based on Generative Adversarial Networks to tackle this issue. In this method, abstract features of users and items are extracted from their tag-based profiles by a disentangling network. These abstract features are then used to calculate the probability of a user preferring an item, and are also used to reconstruct new user and item profiles by a generative network. Furthermore, the discriminative network is introduced to identify generated profiles for enforcing smoothness in the representation of users and items. Experiments on two real-world data-sets demonstrate the state-of-the-art performance of the proposed method.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122135744","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

Location, Occupation, and Semantics Based Socioeconomic Status Inference on Twitter Twitter上基于位置、职业和语义的社会经济地位推断

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00171

Jacob Levy Abitbol, M. Karsai, E. Fleury

引用次数: 18

A Smartphone-Based Probe Data Platform for Road Management and Safety in Developing Countries 基于智能手机的发展中国家道路管理和安全探测数据平台

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00095

Kotaro Kataoka, Saurabh Gangwar, Karthik Yadav Mudda, Souraj Mandal

引用次数: 6

Linky: Visualizing User Identity Linkage Results for Multiple Online Social Networks 链接:可视化多个在线社交网络的用户身份链接结果

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00207

R. Lee, Ming Shan Hee, Philips Kokoh Prasetyo, Ee-Peng Lim

{"title":"Linky: Visualizing User Identity Linkage Results for Multiple Online Social Networks","authors":"R. Lee, Ming Shan Hee, Philips Kokoh Prasetyo, Ee-Peng Lim","doi":"10.1109/ICDMW.2018.00207","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00207","url":null,"abstract":"User identity linkage across online social networks is an emerging research topic that has attracted attention in recent years. Many user identity linkage methods have been proposed so far and most of them utilize user profile, content and network information to determine if two social media accounts belong to the same person. In most cases, user identity linkage methods are evaluated by performing some prediction tasks with the results presented using some overall accuracy measures. However, the methods are rarely compared at the individual user level where a predicted matched (or linked) pair of user identities from different online social networks can be visually compared in terms of user profile (e.g. username), content and network information. Such a comparison is critical to determine the relative strengths and weaknesses of each method. In this work, we present Linky, a visual analytical tool which extracts the results from different user identity linkage methods performed on multiple online social networks and visualizes the user profiles, content and ego networks of the linked user identities. Linky is designed to help researchers to (a) inspect the linked user identities at the individual user level, (b) compare results returned by different user linkage methods, and (c) provide a preliminary empirical understanding on which aspects of the user identities, e.g. profile, content or network, contributed to the user identity linkage results.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127751515","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

An Empirical Study on Sentiments in Twitter Communities 推特社区情绪的实证研究

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00167

Noha Alduaiji, A. Datta

{"title":"An Empirical Study on Sentiments in Twitter Communities","authors":"Noha Alduaiji, A. Datta","doi":"10.1109/ICDMW.2018.00167","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00167","url":null,"abstract":"Sentiment analysis and community detection are two popular research subjects in data mining. Lots of research have been published in recent years that aim to enhance the mining of text using sentiment analysis tools and to mine network structure to find cohesive and important communities in social networks. However, there is a lack of knowledge of the importance of understanding the sentiment and its changes on the community lifetime. In this paper, we aim to study the sentiments and its impact on user behaviour and the evolution of social network communities. To do that, we collect three Twitter datasets, two of which are based on the communications between people who share following links and the third dataset is based on people who talked about world cup subject. Next, we analyse the sentiments of communications to positive, negative or neutral. After that, we detect communities using k-core. Later, we track changes of sentiments in communities for an extended period of time. Our results showed that the positive sentiment is contagious because members of the communities increasingly share positive tweets more than the negative ones over time. Also, we found a strong correlation between positive sentiments and the size of the community in all three datasets. These results lay shed on the existence of like-minded users within the communities which attract social network companies for their viral marketing and recommendation systems.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128999105","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: 5

Supervised Image Classification with Self-Paced Regularization 基于自节奏正则化的监督图像分类

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00067

Zhang Tao, Chen Gong, W. Jia, Xiaoning Song, Jun Sun, Xiaojun Wu

引用次数: 2

Ask George Washington on Negotiated Texts with Quill Q&A 问乔治华盛顿谈判文本与鹅毛笔问答

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00209

Omer Gunes, Nicholas Cole, Sebastian Bates, Jonas Tupp-Mugglestone

{"title":"Ask George Washington on Negotiated Texts with Quill Q&A","authors":"Omer Gunes, Nicholas Cole, Sebastian Bates, Jonas Tupp-Mugglestone","doi":"10.1109/ICDMW.2018.00209","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00209","url":null,"abstract":"Open-domain question answering is an emerging field of natural language processing. Application areas of question answering (QA) systems are increasing with the availability of new datasets, more powerful machines and easy access to voice-enabled devices that interact with humans in their daily lives. Users can get into interactions with QA systems without the need to be limited by any domain. However, there is still need for QA systems that can answer questions in a specific field such as international criminal law, violence of human rights or bill of rights. The recent approaches on open-domain question answering made it possible to extend open-domain QA systems towards specific fields. This paper presents Quill-QA, the new feature of the Quill Platform. Quill Platform is a web platform providing set of tools that digitize and visualize negotiated texts, any text in the form of formal negotiations. Quill-QA is based on Facebook's DrQA system and as a result it adopts a flexible Ranker-Reader architecture. Quill-QA answers questions addressed on the samples of negotiated texts such as U.S. Constitutional Convention of 1787, Utah State Constitutional Convention of 1895 and European Union Act 2017. Without limiting the scope of the input questions to a finite set of templates, Quill-QA is the first QA system that focuses on negotiated texts.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"500 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197784","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: 0