Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining最新文献_第6页

New algorithms for parking demand management and a city-scale deployment 停车需求管理和城市规模部署的新算法

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623359

O. Zoeter, C. Dance, S. Clinchant, J. Andreoli

引用次数: 20

Data science through the lens of social science 从社会科学的角度看数据科学

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2630824

D. Conway

引用次数: 3

Personalized search result diversification via structured learning 通过结构化学习实现个性化搜索结果多样化

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623650

Shangsong Liang, Z. Ren, M. de Rijke

{"title":"Personalized search result diversification via structured learning","authors":"Shangsong Liang, Z. Ren, M. de Rijke","doi":"10.1145/2623330.2623650","DOIUrl":"https://doi.org/10.1145/2623330.2623650","url":null,"abstract":"This paper is concerned with the problem of personalized diversification of search results, with the goal of enhancing the performance of both plain diversification and plain personalization algorithms. In previous work, the problem has mainly been tackled by means of unsupervised learning. To further enhance the performance, we propose a supervised learning strategy. Specifically, we set up a structured learning framework for conducting supervised personalized diversification, in which we add features extracted directly from the tokens of documents and those utilized by unsupervised personalized diversification algorithms, and, importantly, those generated from our proposed user-interest latent Dirichlet topic model. Based on our proposed topic model whether a document can cater to a user's interest can be estimated in our learning strategy. We also define two constraints in our structured learning framework to ensure that search results are both diversified and consistent with a user's interest. We conduct experiments on an open personalized diversification dataset and find that our supervised learning strategy outperforms unsupervised personalized diversification methods as well as other plain personalization and plain diversification methods.","PeriodicalId":20536,"journal":{"name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87670534","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: 43

Gradient boosted feature selection 梯度增强特征选择

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623635

Z. Xu, Gao Huang, Kilian Q. Weinberger, A. Zheng

引用次数: 150

Efficient mini-batch training for stochastic optimization 随机优化的高效小批量训练

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623612

Mu Li, T. Zhang, Yuqiang Chen, Alex Smola

引用次数: 700

Scaling out big data missing value imputations: pythia vs. godzilla 扩展大数据缺失价值估算:皮提亚vs哥斯拉

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623615

C. Anagnostopoulos, P. Triantafillou

{"title":"Scaling out big data missing value imputations: pythia vs. godzilla","authors":"C. Anagnostopoulos, P. Triantafillou","doi":"10.1145/2623330.2623615","DOIUrl":"https://doi.org/10.1145/2623330.2623615","url":null,"abstract":"Solving the missing-value (MV) problem with small estimation errors in big data environments is a notoriously resource-demanding task. As datasets and their user community continuously grow, the problem can only be exacerbated. Assume that it is possible to have a single machine (`Godzilla'), which can store the massive dataset and support an ever-growing community submitting MV imputation requests. Is it possible to replace Godzilla by employing a large number of cohort machines so that imputations can be performed much faster, engaging cohorts in parallel, each of which accesses much smaller partitions of the original dataset? If so, it would be preferable for obvious performance reasons to access only a subset of all cohorts per imputation. In this case, can we decide swiftly which is the desired subset of cohorts to engage per imputation? But efficiency and scalability is just one key concern! Is it possible to do the above while ensuring comparable or even better than Godzilla's imputation estimation errors? In this paper we derive answers to these fundamentals questions and develop principled methods and a framework which offer large performance speed-ups and better, or comparable, errors to that of Godzilla, independently of which missing-value imputation algorithm is used. Our contributions involve Pythia, a framework and algorithms for providing the answers to the above questions and for engaging the appropriate subset of cohorts per MV imputation request. Pythia functionality rests on two pillars: (i) dataset (partition) signatures, one per cohort, and (ii) similarity notions and algorithms, which can identify the appropriate subset of cohorts to engage. Comprehensive experimentation with real and synthetic datasets showcase our efficiency, scalability, and accuracy claims.","PeriodicalId":20536,"journal":{"name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85916114","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: 21

Identifying tourists from public transport commuters 从公共交通通勤者中识别游客

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623352

Mingqiang Xue, Huayu Wu, Wei Chen, W. Ng, Gin Howe Goh

{"title":"Identifying tourists from public transport commuters","authors":"Mingqiang Xue, Huayu Wu, Wei Chen, W. Ng, Gin Howe Goh","doi":"10.1145/2623330.2623352","DOIUrl":"https://doi.org/10.1145/2623330.2623352","url":null,"abstract":"Tourism industry has become a key economic driver for Singapore. Understanding the behaviors of tourists is very important for the government and private sectors, e.g., restaurants, hotels and advertising companies, to improve their existing services or create new business opportunities. In this joint work with Singapore's Land Transport Authority (LTA), we innovatively apply machine learning techniques to identity the tourists among public commuters using the public transportation data provided by LTA. On successful identification, the travelling patterns of tourists are then revealed and thus allow further analyses to be carried out such as on their favorite destinations, region of stay, etc. Technically, we model the tourists identification as a classification problem, and design an iterative learning algorithm to perform inference with limited prior knowledge and labeled data. We show the superiority of our algorithm with performance evaluation and comparison with other state-of-the-art learning algorithms. Further, we build an interactive web-based system for answering queries regarding the moving patterns of the tourists, which can be used by stakeholders to gain insight into tourists' travelling behaviors in Singapore.","PeriodicalId":20536,"journal":{"name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86021061","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

Frontiers in E-commerce personalization 电子商务个性化的前沿

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2630820

S. Subramaniam

引用次数: 6

Relevant overlapping subspace clusters on categorical data 分类数据的相关重叠子空间聚类

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623652

Xiao He, Jing Feng, B. Konte, S. T. Mai, C. Plant

{"title":"Relevant overlapping subspace clusters on categorical data","authors":"Xiao He, Jing Feng, B. Konte, S. T. Mai, C. Plant","doi":"10.1145/2623330.2623652","DOIUrl":"https://doi.org/10.1145/2623330.2623652","url":null,"abstract":"Clustering categorical data poses some unique challenges: Due to missing order and spacing among the categories, selecting a suitable similarity measure is a difficult task. Many existing techniques require the user to specify input parameters which are difficult to estimate. Moreover, many techniques are limited to detect clusters in the full-dimensional data space. Only few methods exist for subspace clustering and they produce highly redundant results. Therefore, we propose ROCAT (Relevant Overlapping Subspace Clusters on Categorical Data), a novel technique based on the idea of data compression. Following the Minimum Description Length principle, ROCAT automatically detects the most relevant subspace clusters without any input parameter. The relevance of each cluster is validated by its contribution to compress the data. Optimizing the trade-off between goodness-of-fit and model complexity, ROCAT automatically determines a meaningful number of clusters to represent the data. ROCAT is especially designed to detect subspace clusters on categorical data which may overlap in objects and/or attributes; i.e. objects can be assigned to different clusters in different subspaces and attributes may contribute to different subspaces containing clusters. ROCAT naturally avoids undesired redundancy in clusters and subspaces by allowing overlap only if it improves the compression rate. Extensive experiments demonstrate the effectiveness and efficiency of our approach.","PeriodicalId":20536,"journal":{"name":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85718260","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: 9

A dirichlet multinomial mixture model-based approach for short text clustering 基于dirichlet多项混合模型的短文本聚类方法

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2014-08-24 DOI: 10.1145/2623330.2623715

Jianhua Yin, Jianyong Wang

引用次数: 443