Proceedings of the 21st ACM international conference on Information and knowledge management最新文献_第3页

PicAlert!: a system for privacy-aware image classification and retrieval PicAlert !:一个具有隐私意识的图像分类和检索系统

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398735

Sergej Zerr, Stefan Siersdorfer, Jonathon S. Hare

引用次数: 35

Incorporating variability in user behavior into systems based evaluation 将用户行为的可变性纳入基于系统的评估中

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396782

Ben Carterette, E. Kanoulas, Emine Yilmaz

{"title":"Incorporating variability in user behavior into systems based evaluation","authors":"Ben Carterette, E. Kanoulas, Emine Yilmaz","doi":"10.1145/2396761.2396782","DOIUrl":"https://doi.org/10.1145/2396761.2396782","url":null,"abstract":"Click logs present a wealth of evidence about how users interact with a search system. This evidence has been used for many things: learning rankings, personalizing, evaluating effectiveness, and more. But it is almost always distilled into point estimates of feature or parameter values, ignoring what may be the most salient feature of users---their variability. No two users interact with a system in exactly the same way, and even a single user may interact with results for the same query differently depending on information need, mood, time of day, and a host of other factors. We present a Bayesian approach to using logs to compute posterior distributions for probabilistic models of user interactions. Since they are distributions rather than point estimates, they naturally capture variability in the population. We show how to cluster posterior distributions to discover patterns of user interactions in logs, and discuss how to use the clusters to evaluate search engines according to a user model. Because the approach is Bayesian, our methods can be applied to very large logs (such as those possessed by Web search engines) as well as very small (such as those found in almost any other setting).","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128463578","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: 27

Discovering logical knowledge for deep question answering 发现逻辑知识，进行深度问答

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398544

Zhao Liu, Xipeng Qiu, L. Cao, Xuanjing Huang

引用次数: 4

Multiview hierarchical bayesian regression model andapplication to online advertising 多视图层次贝叶斯回归模型及其在网络广告中的应用

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396825

Tianbing Xu, Ruofei Zhang, Zhen Guo

{"title":"Multiview hierarchical bayesian regression model andapplication to online advertising","authors":"Tianbing Xu, Ruofei Zhang, Zhen Guo","doi":"10.1145/2396761.2396825","DOIUrl":"https://doi.org/10.1145/2396761.2396825","url":null,"abstract":"With the development of Web applications, large scale data are popular; and they are not only getting richer, but also ubiquitously interconnected with users and other objects in various ways, which brings about multi-view data with implicit structure. In this paper, we propose a novel hierarchical Bayesian mixture regression model, which discovers and then exploits the relationships among multiple views of the data to perform various machine learning tasks. A stochastic EM inference and learning algorithm is derived; and a parallel implementation in Hadoop MapReduce [9] paradigm is developed to scale up the learning. We apply the developed model and algorithm on click-through-rate (CTR) prediction and campaign targeting recommendation in online advertising to measure its effectiveness. The experiments on both synthetic data and large scale ads serving data from a real world online advertising exchange demonstrate the superior CTR prediction accuracy of our method compared to existing state-of-the-art methods. The results also show that our model can recommend high performance targeting features for online advertising campaigns.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129322555","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: 2

MEET: a generalized framework for reciprocal recommender systems 相互推荐系统的广义框架

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2396770

Lei Li, Tao Li

引用次数: 51

Importance weighted passive learning 重要性加权被动学习

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398611

Shuaiqiang Wang, Xiaoming Xi, Yilong Yin

引用次数: 0

SHB 2012: international workshop on smart health and wellbeing SHB 2012:智能健康和福祉国际讲习班

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398756

Christopher C. Yang, Hsinchun Chen, H. Wactlar, Combi Carlo, Xuning Tang

引用次数: 0

Full-text citation analysis: enhancing bibliometric and scientific publication ranking 全文引文分析:提高文献计量学和科学出版物排名

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398555

Xiaozhong Liu, Jinsong Zhang, Chun Guo

{"title":"Full-text citation analysis: enhancing bibliometric and scientific publication ranking","authors":"Xiaozhong Liu, Jinsong Zhang, Chun Guo","doi":"10.1145/2396761.2398555","DOIUrl":"https://doi.org/10.1145/2396761.2398555","url":null,"abstract":"The goal of this paper is to use innovative text and graph mining algorithms along with full-text citation analysis and topic modeling to enhance classical bibliometric analysis and publication ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. Based on 104 topics (labeled with keywords) and their review papers, the cited publications of each review paper are assumed as \"important publications\" for ranking evaluation. The result shows that full text citation and publication content prior topic distribution along with the PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance for academic IR system.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129037555","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

Predicting CTR of new ads via click prediction 通过点击预测来预测新广告的点击率

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398688

Alexander Kolesnikov, Yury Logachev, V. A. Topinskiy

引用次数: 8

Weighted linear kernel with tree transformed features for malware detection 基于树变换特征的加权线性核

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI: 10.1145/2396761.2398622

Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, A. Nucci, P. Tan

{"title":"Weighted linear kernel with tree transformed features for malware detection","authors":"Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, A. Nucci, P. Tan","doi":"10.1145/2396761.2398622","DOIUrl":"https://doi.org/10.1145/2396761.2398622","url":null,"abstract":"Malware detection from network traffic flows is a challenging problem due to data irregularity issues such as imbalanced class distribution, noise, missing values, and heterogeneous types of features. To address these challenges, this paper presents a two-stage classification approach for malware detection. The framework initially employs random forest as a macro-level classifier to separate the malicious from non-malicious network flows, followed by a collection of one-class support vector machine classifiers to identify the specific type of malware. A novel tree-based feature construction approach is proposed to deal with data imperfection issues. As the performance of the support vector machine classifier often depends on the kernel function used to compute the similarity between every pair of data points, designing an appropriate kernel is essential for accurate identification of malware classes. We present a simple algorithm to construct a weighted linear kernel on the tree transformed features and demonstrate its effectiveness in detecting malware from real network traffic data.","PeriodicalId":313414,"journal":{"name":"Proceedings of the 21st ACM international conference on Information and knowledge management","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130637947","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: 11