Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining最新文献_第10页

Hadoop: a view from the trenches Hadoop:从战壕的角度来看

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2491128

M. Bhandarkar

引用次数: 4

Inferring social roles and statuses in social networks 推断社会网络中的社会角色和地位

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2487597

Yuchen Zhao, Guan Wang, Philip S. Yu, Shaobo Liu, Simon Zhang

{"title":"Inferring social roles and statuses in social networks","authors":"Yuchen Zhao, Guan Wang, Philip S. Yu, Shaobo Liu, Simon Zhang","doi":"10.1145/2487575.2487597","DOIUrl":"https://doi.org/10.1145/2487575.2487597","url":null,"abstract":"Users in online social networks play a variety of social roles and statuses. For example, users in Twitter can be represented as advertiser, content contributor, information receiver, etc; users in Linkedin can be in different professional roles, such as engineer, salesperson and recruiter. Previous research work mainly focuses on using categorical and textual information to predict the attributes of users. However, it cannot be applied to a large number of users in real social networks, since much of such information is missing, outdated and non-standard. In this paper, we investigate the social roles and statuses that people act in online social networks in the perspective of network structures, since the uniqueness of social networks is connecting people. We quantitatively analyze a number of key social principles and theories that correlate with social roles and statuses. We systematically study how the network characteristics reflect the social situations of users in an online society. We discover patterns of homophily, the tendency of users to connect with users with similar social roles and statuses. In addition, we observe that different factors in social theories influence the social role/status of an individual user to various extent, since these social principles represent different aspects of the network. We then introduce an optimization framework based on Factor Conditioning Symmetry, and we propose a probabilistic model to integrate the optimization framework on local structural information as well as network influence to infer the unknown social roles and statuses of online users. We will present experiment results to show the effectiveness of the inference.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74211678","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: 73

Heat pump detection from coarse grained smart meter data with positive and unlabeled learning 热泵检测从粗粒度智能电表数据与积极的和未标记的学习

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2488203

Hongliang Fei, Younghun Kim, S. Sahu, M. Naphade, Sanjay K. Mamidipalli, John Hutchinson

{"title":"Heat pump detection from coarse grained smart meter data with positive and unlabeled learning","authors":"Hongliang Fei, Younghun Kim, S. Sahu, M. Naphade, Sanjay K. Mamidipalli, John Hutchinson","doi":"10.1145/2487575.2488203","DOIUrl":"https://doi.org/10.1145/2487575.2488203","url":null,"abstract":"Recent advances in smart metering technology enable utility companies to have access to tremendous amount of smart meter data, from which the utility companies are eager to gain more insight about their customers. In this paper, we aim to detect electric heat pumps from coarse grained smart meter data for a heat pump marketing campaign. However, appliance detection is a challenging task, especially given a very low granularity and partial labeled even unlabeled data. Traditional methods install either a high granularity smart meter or sensors at every appliance, which is either too expensive or requires technical expertise. We propose a novel approach to detect heat pumps that utilizes low granularity smart meter data, prior sales data and weather data. In particular, motivated by the characteristics of heat pump consumption pattern, we extract novel features that are highly relevant to heat pump usage from smart meter data and weather data. Under the constraint that only a subset of heat pump users are available, we formalize the problem into a positive and unlabeled data classification and apply biased Support Vector Machine (BSVM) to our extracted features. Our empirical study on a real-world data set demonstrates the effectiveness of our method. Furthermore, our method has been deployed in a real-life setting where the partner electric company runs a targeted campaign for 292,496 customers. Based on the initial feedback, our detection algorithm can successfully detect substantial number of non-heat pump users who were identified heat pump users with the prior algorithm the company had used.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81426326","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: 34

Diversity maximization under matroid constraints 矩阵约束下的多样性最大化

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2487636

Z. Abbassi, V. Mirrokni, Mayur Thakur

{"title":"Diversity maximization under matroid constraints","authors":"Z. Abbassi, V. Mirrokni, Mayur Thakur","doi":"10.1145/2487575.2487636","DOIUrl":"https://doi.org/10.1145/2487575.2487636","url":null,"abstract":"Aggregator websites typically present documents in the form of representative clusters. In order for users to get a broader perspective, it is important to deliver a diversified set of representative documents in those clusters. One approach to diversification is to maximize the average dissimilarity among documents. Another way to capture diversity is to avoid showing several documents from the same category (e.g. from the same news channel). We combine the above two diversification concepts by modeling the latter approach as a (partition) matroid constraint, and study diversity maximization problems under matroid constraints. We present the first constant-factor approximation algorithm for this problem, using a new technique. Our local search 0.5-approximation algorithm is also the first constant-factor approximation for the max-dispersion problem under matroid constraints. Our combinatorial proof technique for maximizing diversity under matroid constraints uses the existence of a family of Latin squares which may also be of independent interest. In order to apply these diversity maximization algorithms in the context of aggregator websites and as a preprocessing step for our diversity maximization tool, we develop greedy clustering algorithms that maximize weighted coverage of a predefined set of topics. Our algorithms are based on computing a set of cluster centers, where clusters are formed around them. We show the better performance of our algorithms for diversity and coverage maximization by running experiments on real (Twitter) and synthetic data in the context of real-time search over micro-posts. Finally we perform a user study validating our algorithms and diversity metrics.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81608109","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: 97

Cross-task crowdsourcing Cross-task众包

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2487593

Kaixiang Mo, Erheng Zhong, Qiang Yang

{"title":"Cross-task crowdsourcing","authors":"Kaixiang Mo, Erheng Zhong, Qiang Yang","doi":"10.1145/2487575.2487593","DOIUrl":"https://doi.org/10.1145/2487575.2487593","url":null,"abstract":"Crowdsourcing is an effective method for collecting labeled data for various data mining tasks. It is critical to ensure the veracity of the produced data because responses collected from different users may be noisy and unreliable. Previous works solve this veracity problem by estimating both the user ability and question difficulty based on the knowledge in each task individually. In this case, each single task needs large amounts of data to provide accurate estimations. However, in practice, budgets provided by customers for a given target task may be limited, and hence each question can be presented to only a few users where each user can answer only a few questions. This data sparsity problem can cause previous approaches to perform poorly due to the overfitting problem on rare data and eventually damage the data veracity. Fortunately, in real-world applications, users can answer questions from multiple historical tasks. For example, one can annotate images as well as label the sentiment of a given title. In this paper, we employ transfer learning, which borrows knowledge from auxiliary historical tasks to improve the data veracity in a given target task. The motivation is that users have stable characteristics across different crowdsourcing tasks and thus data from different tasks can be exploited collectively to estimate users' abilities in the target task. We propose a hierarchical Bayesian model, TLC (Transfer Learning for Crowdsourcing), to implement this idea by considering the overlapping users as a bridge. In addition, to avoid possible negative impact, TLC introduces task-specific factors to model task differences. The experimental results show that TLC significantly improves the accuracy over several state-of-the-art non-transfer-learning approaches under very limited budget in various labeling tasks.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85270911","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: 50

A unified search federation system based on online user feedback 基于在线用户反馈的统一搜索联合系统

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2488198

Luo Jie, Sudarshan Lamkhede, Rochit Sapra, Evans Hsu, Helen Song, Yi Chang

{"title":"A unified search federation system based on online user feedback","authors":"Luo Jie, Sudarshan Lamkhede, Rochit Sapra, Evans Hsu, Helen Song, Yi Chang","doi":"10.1145/2487575.2488198","DOIUrl":"https://doi.org/10.1145/2487575.2488198","url":null,"abstract":"Today's popular web search engines expand the search process beyond crawled web pages to specialized corpora (\"verticals\") like images, videos, news, local, sports, finance, shopping etc., each with its own specialized search engine. Search federation deals with problems of the selection of search engines to query and merging of their results into a single result set. Despite a few recent advances, the problem is still very challenging. First, due to the heterogeneous nature of different verticals, how the system merges the vertical results with the web documents to serve the user's information need is still an open problem. Moreover, the scale of the search engine and the increasing number of vertical properties requires a solution which is efficient and scaleable. In this paper, we propose a unified framework for the search federation problem. We model the search federation as a contextual bandit problem. The system uses reward as a proxy for user satisfaction. Given a query, our system predicts the expected reward for each vertical, then organizes the search result page (SERP) in a way which maximizes the total reward. Instead of relying on human judges, our system leverages implicit user feedback to learn the model. The method is efficient to implement and can be applied to verticals of different nature. We have successfully deployed the system to three different markets, and it handles multiple verticals in each market. The system is now serving hundreds of millions of queries live each day, and has improved user metrics considerably.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81129879","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: 20

Multi-space probabilistic sequence modeling 多空间概率序列建模

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2487632

Shuo Chen, Jiexun Xu, T. Joachims

引用次数: 29

FISM: factored item similarity models for top-N recommender systems top-N推荐系统的因子项目相似度模型

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2487589

Santosh Kabbur, Xia Ning, G. Karypis

引用次数: 622

Synthetic review spamming and defense 综合评论垃圾邮件和防御

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2487688

Huan Sun, Alex Morales, Xifeng Yan

{"title":"Synthetic review spamming and defense","authors":"Huan Sun, Alex Morales, Xifeng Yan","doi":"10.1145/2487575.2487688","DOIUrl":"https://doi.org/10.1145/2487575.2487688","url":null,"abstract":"Online reviews have been popularly adopted in many applications. Since they can either promote or harm the reputation of a product or a service, buying and selling fake reviews becomes a profitable business and a big threat. In this paper, we introduce a very simple, but powerful review spamming technique that could fail the existing feature-based detection algorithms easily. It uses one truthful review as a template, and replaces its sentences with those from other reviews in a repository. Fake reviews generated by this mechanism are extremely hard to detect: Both the state-of-the-art computational approaches and human readers acquire an error rate of 35%-48%, just slightly better than a random guess. While it is challenging to detect such fake reviews, we have made solid progress in suppressing them. A novel defense method that leverages the difference of semantic flows between synthetic and truthful reviews is developed, which is able to reduce the detection error rate to approximately 22%, a significant improvement over the performance of existing approaches. Nevertheless, it is still a challenging research task to further decrease the error rate. Synthetic Review Spamming Demo: www.cs.ucsb.edu/~alex_morales/reviewspam/","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77695058","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: 19

An integrated framework for suicide risk prediction 自杀风险预测的综合框架

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2013-08-11 DOI: 10.1145/2487575.2488196

T. Tran, Dinh Q. Phung, Wei Luo, R. Harvey, M. Berk, S. Venkatesh

{"title":"An integrated framework for suicide risk prediction","authors":"T. Tran, Dinh Q. Phung, Wei Luo, R. Harvey, M. Berk, S. Venkatesh","doi":"10.1145/2487575.2488196","DOIUrl":"https://doi.org/10.1145/2487575.2488196","url":null,"abstract":"Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient's clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in l1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.","PeriodicalId":20472,"journal":{"name":"Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88956659","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: 44