Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献_第10页

Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs 在非常大的图中快速完全动态的基于地标的最短路径距离估计

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063834

Konstantin Tretyakov, Abel Armas-Cervantes, L. García-Bañuelos, J. Vilo, M. Dumas

{"title":"Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs","authors":"Konstantin Tretyakov, Abel Armas-Cervantes, L. García-Bañuelos, J. Vilo, M. Dumas","doi":"10.1145/2063576.2063834","DOIUrl":"https://doi.org/10.1145/2063576.2063834","url":null,"abstract":"Computing the shortest path between a pair of vertices in a graph is a fundamental primitive in graph algorithmics. Classical exact methods for this problem do not scale up to contemporary, rapidly evolving social networks with hundreds of millions of users and billions of connections. A number of approximate methods have been proposed, including several landmark-based methods that have been shown to scale up to very large graphs with acceptable accuracy. This paper presents two improvements to existing landmark-based shortest path estimation methods. The first improvement relates to the use of shortest-path trees (SPTs). Together with appropriate short-cutting heuristics, the use of SPTs allows to achieve higher accuracy with acceptable time and memory overhead. Furthermore, SPTs can be maintained incrementally under edge insertions and deletions, which allows for a fully-dynamic algorithm. The second improvement is a new landmark selection strategy that seeks to maximize the coverage of all shortest paths by the selected landmarks. The improved method is evaluated on the DBLP, Orkut, Twitter and Skype social networks.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"15 1","pages":"1785-1794"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82523921","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: 85

Retrieving and ranking unannotated images through collaboratively mining online search results 通过协同挖掘在线搜索结果检索和排序未注释的图像

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063650

Songhua Xu, Hao Jiang, F. Lau

{"title":"Retrieving and ranking unannotated images through collaboratively mining online search results","authors":"Songhua Xu, Hao Jiang, F. Lau","doi":"10.1145/2063576.2063650","DOIUrl":"https://doi.org/10.1145/2063576.2063650","url":null,"abstract":"We present a new image search and ranking algorithm for retrieving unannotated images by collaboratively mining online search results which consist of online image and text search results. The online image search results are leveraged as reference examples to perform content-based image search over unannotated images. The online text search results are utilized to estimate the reference images' relevance to the search query. The key feature of our method is its capability to deal with unreliable online image search results through jointly mining visual and textual aspects of online search results. Through such collaborative mining, our algorithm infers the relevance of an online search result image to a text query. Once we obtain the estimate of query relevance score for each online image search result, we can selectively use query specific online search result images as reference examples for retrieving and ranking unannotated images. We tested our algorithm both on the standard public image datasets and several modestly sized personal photo collections. We also compared our method with two well-known peer methods. The results indicate that our algorithm is superior to existing content-based image search algorithms for retrieving and ranking unannotated images.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"25 1","pages":"485-494"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80909168","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: 3

Recency ranking by diversification of result set 基于结果集多样化的近期排序

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063862

Andrey Styskin, Fedor Romanenko, F. Vorobyev, P. Serdyukov

引用次数: 20

Managing interoperability and complexity inhealth systems: MIXHS'11 workshop summary 管理卫生系统的互操作性和复杂性:MIXHS'11研讨会总结

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2064050

M. Bouamrane, C. Tao

引用次数: 3

Duplicate detection through structure optimization 通过结构优化进行重复检测

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063644

Luís Leitão, P. Calado

{"title":"Duplicate detection through structure optimization","authors":"Luís Leitão, P. Calado","doi":"10.1145/2063576.2063644","DOIUrl":"https://doi.org/10.1145/2063576.2063644","url":null,"abstract":"Detecting and eliminating duplicates in databases is a task of critical importance in many applications. Although solutions for traditional models, such as relational data, have been widely studied, recently there has been some focus on solutions for more complex hierarchical structures as, for instance, XML data. Such data presents many different challenges, among which is the issue of how to exploit the schema structure to determine if two objects are duplicates. In this paper, we argue that structure can indeed have a significant impact on the process of duplicate detection. We propose a novel method that automatically restructures database objects in order to take full advantage of the relations between its attributes. This new structure reflects the relative importance of the attributes in the database and avoids the need to perform a manual selection. To test our approach we applied it to an existing duplicate detection system. Experiments performed on several datasets show that, using the new learned structure, we consistently outperform both the results obtained with the original database structure and those obtained by letting a knowledgeable user manually choose the attributes to compare.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"12 1","pages":"443-452"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89826857","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

A pairwise ranking based approach to learning with positive and unlabeled examples 一种基于两两排序的学习方法，使用正样例和未标记样例

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063675

Sundararajan Sellamanickam, Priyanka Garg, S. Keerthi

{"title":"A pairwise ranking based approach to learning with positive and unlabeled examples","authors":"Sundararajan Sellamanickam, Priyanka Garg, S. Keerthi","doi":"10.1145/2063576.2063675","DOIUrl":"https://doi.org/10.1145/2063576.2063675","url":null,"abstract":"A large fraction of binary classification problems arising in web applications are of the type where the positive class is well defined and compact while the negative class comprises everything else in the distribution for which the classifier is developed; it is hard to represent and sample from such a broad negative class. Classifiers based only on positive and unlabeled examples reduce human annotation effort significantly by removing the burden of choosing a representative set of negative examples. Various methods have been proposed in the literature for building such classifiers. Of these, the state of the art methods are Biased SVM and Elkan & Noto's methods. While these methods often work well in practice, they are computationally expensive since hyperparameter tuning is very important, particularly when the size of labeled positive examples set is small and class imbalance is high. In this paper we propose a pairwise ranking based approach to learn from positive and unlabeled examples (LPU) and we give a theoretical justification for it. We present a pairwise RankSVM (RSVM) based method for our approach. The method is simple, efficient, and its hyperparameters are easy to tune. A detailed experimental study using several benchmark datasets shows that the proposed method gives competitive classification performance compared to the mentioned state of the art methods, while training 3-10 times faster. We also propose an efficient AUC based feature selection technique in the LPU setting and demonstrate its usefulness on the datasets. To get an idea of the goodness of the LPU methods we compare them against supervised learning (SL) methods that also make use of negative examples in training. SL methods give a slightly better performance than LPU methods when there is a rich set of negative examples; however, they are inferior when the number of negative training examples is not large enough.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"7 1","pages":"663-672"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89862696","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: 28

Ranking-based processing of SQL queries 基于排名的SQL查询处理

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063614

H. Azzam, T. Roelleke, Sirvan Yahyaei

引用次数: 0

Content-driven detection of campaigns in social media 内容驱动的社交媒体活动检测

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063658

Kyumin Lee, James Caverlee, Zhiyuan Cheng, D. Sui

引用次数: 39

WikiLabel: an encyclopedic approach to labeling documents en masse 维基标签:一个百科全书式的标记文档的方法

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063961

Tadashi Nomoto

引用次数: 15

Latent feature encoding using dyadic and relational data 使用二进和关系数据的潜在特征编码

Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management Pub Date : 2011-10-24 DOI: 10.1145/2063576.2063926

S. Ando

{"title":"Latent feature encoding using dyadic and relational data","authors":"S. Ando","doi":"10.1145/2063576.2063926","DOIUrl":"https://doi.org/10.1145/2063576.2063926","url":null,"abstract":"Learning from dyadic and relational data is a fundamental problem for IR and KDD applications in web and social media domain. Basic behaviors and characteristics of users and documents are typically described by a collection of dyads, i.e., pairs of entities. Discriminative features extracted from such data are essential in exploratory and discriminatory analyses. Relational properties of the entities reflect pair-wise similarities and their collective community structure which are also valuable for discriminative learning. A challenging aspect of learning from the relational data in many domains, is that the generative process of relational links appears noisy and is not well described by a stochastic model.\u0000 In this paper, we present a principled approach for learning discriminative features from heterogeneous sources of dyadic and relational data. We propose an information-theoretic framework called Latent Feature Encoding (LFE) which projects the entities and the links to a latent feature space in the analogy of -encoding. Projection is formalized as a maximization of the mutual information preserved in the latent features, regularized by the compression rate of encoding. The regularization is emphasized over more probable links to account for the noisiness of the observation. An empirical evaluation of the proposed method using text and social media datasets is presented. Performances in supervised and unsupervised learning tasks are compared with those of conventional latent feature extraction methods.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"44 1","pages":"2201-2204"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88134749","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