Proceedings of the 19th ACM international conference on Information and knowledge management最新文献_第7页

Mr.KNN: soft relevance for multi-label classification Mr.KNN:多标签分类的软相关性

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871485

Xiaotong Lin, Xue-wen Chen

{"title":"Mr.KNN: soft relevance for multi-label classification","authors":"Xiaotong Lin, Xue-wen Chen","doi":"10.1145/1871437.1871485","DOIUrl":"https://doi.org/10.1145/1871437.1871485","url":null,"abstract":"Multi-label classification refers to learning tasks with each instance belonging to one or more classes simultaneously. It arose from real-world applications such as information retrieval, text categorization and functional genomics. Currently, most of the multi-label learning methods use the strategy called binary relevance, which constructs a classifier for each unique label by grouping data into positives (examples with this label) and negatives (examples without this label). With binary relevance, an example with multiple labels is considered as a positive data for each label it belongs to. For some classes, this data point may behave like an outlier confusing classifiers, especially in the cases of well-separated classes. In this paper, we first introduce a new strategy called soft relevance, where each multi-label example is assigned a relevance score to the labels it belongs to. This soft relevance is then employed in a voting function used in a k nearest neighbor classifier. Furthermore, a voting-margin ratio is introduced to the k nearest neighbor classifier for better performance. We compare the proposed method to other multi-label learning methods over three multi-label datasets and demonstrate that the proposed method provides an effective way to multi-label learning.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133511787","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: 31

Identifying hotspots on the real-time web 识别实时网络上的热点

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871742

K. Kamath, James Caverlee

引用次数: 3

XML schema computations: schema compatibility testing and subschema extraction XML模式计算:模式兼容性测试和子模式提取

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871545

Thomas Y. T. Lee, D. Cheung

引用次数: 5

Network growth and the spectral evolution model 网络增长和频谱演化模型

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871533

Jérôme Kunegis, D. Fay, C. Bauckhage

引用次数: 49

Skyline query processing for uncertain data 不确定数据的Skyline查询处理

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871604

Mohamed E. Khalefa, M. Mokbel, Justin J. Levandoski

引用次数: 39

Manifold ranking with sink points for update summarization 歧管排名与汇点更新摘要

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871722

Pan Du, J. Guo, Jin Zhang, Xueqi Cheng

{"title":"Manifold ranking with sink points for update summarization","authors":"Pan Du, J. Guo, Jin Zhang, Xueqi Cheng","doi":"10.1145/1871437.1871722","DOIUrl":"https://doi.org/10.1145/1871437.1871722","url":null,"abstract":"Update summarization aims to create a summary over a topic-related multi-document dataset based on the assumption that the user has already read a set of earlier documents of the same topic. Beyond the problems (i.e., topic relevance, salience, and diversity in extracted information) tackled by topic-focused multi-document summarization, the update summarization must address the novelty problem as well. In this paper, we propose a novel extractive approach based on manifold ranking with sink points for update summarization. Specifically, our approach leverages a manifold ranking process over the sentence manifold to find topic relevant and salient sentences. More important, by introducing the sink points into sentence manifold, the ranking process can further capture the novelty and diversity based on the intrinsic sentence manifold. Therefore, we are able to address the four challenging problems above for update summarization in a unified way. Experiments on benchmarks of TAC are performed and the evaluation results show that our approach can achieve comparative performance to the existing best performing systems in TAC tasks.","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125656499","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

User behavior driven ranking without editorial judgments 用户行为驱动排名，无需编辑判断

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871650

Taesup Moon, G. Dupret, Shihao Ji, Ciya Liao, Zhaohui Zheng

{"title":"User behavior driven ranking without editorial judgments","authors":"Taesup Moon, G. Dupret, Shihao Ji, Ciya Liao, Zhaohui Zheng","doi":"10.1145/1871437.1871650","DOIUrl":"https://doi.org/10.1145/1871437.1871650","url":null,"abstract":"We explore the potential of using users click-through logs where no editorial judgment is available to improve the ranking function of a vertical search engine. We base our analysis on the Cumulate Relevance Model, a user behavior model recently proposed as a way to extract relevance signal from click-through logs. We propose a novel way of directly learning the ranking function, effectively by-passing the need to have explicit editorial relevance label for each query-document pair. This approach potentially adjusts more closely the ranking function to a variety of user behaviors both at the individual and at the aggregate levels. We investigate two ways of using behavioral model; First, we consider the parametric approach where we learn the estimates of document relevance and use them as targets for the machine learned ranking schemes. In the second, functional approach, we learn a function that maximizes the behavioral model likelihood, effectively by-passing the need to estimate a substitute for document labels. Experiments using user session data collected from a commercial vertical search engine demonstrate the potential of our approach. While in terms of DCG, the editorial model out-perform the behavioral one, online experiments show that the behavioral model is on par --if not superior-- to the editorial model. To our knowledge, this is the first report in the Literature of a competitive behavioral model in a commercial setting","PeriodicalId":310611,"journal":{"name":"Proceedings of the 19th ACM international conference on Information and knowledge management","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129129551","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: 6

Multi-document topic segmentation 多文档主题分割

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871579

Minwoo Jeong, Ivan Titov

引用次数: 17

Exploiting user interests for collaborative filtering: interests expansion via personalized ranking 利用用户兴趣进行协同过滤:通过个性化排名进行兴趣扩展

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871707

Qi Liu, Enhong Chen, Hui Xiong, C. Ding

引用次数: 14

Two-tier similarity model for story link detection 故事链接检测的两层相似度模型

Proceedings of the 19th ACM international conference on Information and knowledge management Pub Date : 2010-10-26 DOI: 10.1145/1871437.1871539

Tadashi Nomoto

引用次数: 17