Proceedings of the 2017 ACM on Conference on Information and Knowledge Management最新文献

Tensor Rank Estimation and Completion via CP-based Nuclear Norm 基于cp核范数的张量秩估计与补全

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132945

Qiquan Shi, Haiping Lu, Yiu-ming Cheung

{"title":"Tensor Rank Estimation and Completion via CP-based Nuclear Norm","authors":"Qiquan Shi, Haiping Lu, Yiu-ming Cheung","doi":"10.1145/3132847.3132945","DOIUrl":"https://doi.org/10.1145/3132847.3132945","url":null,"abstract":"Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition. However, this approach often requires the determination of a tensor rank a priori. This rank estimation problem is difficult in practice. Several Bayesian solutions have been proposed but they often under/over-estimate the tensor rank while being quite slow. To address this problem of rank estimation with missing entries, we view the weight vector of the orthogonal CP decomposition of a tensor to be analogous to the vector of singular values of a matrix. Subsequently, we define a new CP-based tensor nuclear norm as the $L_1$-norm of this weight vector. We then propose Tensor Rank Estimation based on $L_1$-regularized orthogonal CP decomposition (TREL1) for both CP-rank and Tucker-rank. Specifically, we incorporate a regularization with CP-based tensor nuclear norm when minimizing the reconstruction error in TC to automatically determine the rank of an incomplete tensor. Experimental results on both synthetic and real data show that: 1) Given sufficient observed entries, TREL1 can estimate the true rank (both CP-rank and Tucker-rank) of incomplete tensors well; 2) The rank estimated by TREL1 can consistently improve recovery accuracy of decomposition-based TC methods; 3) TREL1 is not sensitive to its parameters in general and more efficient than existing rank estimation methods.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75262250","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

Coupled Sparse Matrix Factorization for Response Time Prediction in Logistics Services 耦合稀疏矩阵分解在物流服务响应时间预测中的应用

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132948

Yuqi Wang, Jiannong Cao, Lifang He, Wengen Li, Lichao Sun, Philip S. Yu

引用次数: 6

Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information 基于深度行为和内容信息联合建模的社交机器人检测

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133050

C. Cai, Linjing Li, D. Zeng

{"title":"Detecting Social Bots by Jointly Modeling Deep Behavior and Content Information","authors":"C. Cai, Linjing Li, D. Zeng","doi":"10.1145/3132847.3133050","DOIUrl":"https://doi.org/10.1145/3132847.3133050","url":null,"abstract":"Bots are regarded as the most common kind of malwares in the era of Web 2.0. In recent years, Internet has been populated by hundreds of millions of bots, especially on social media. Thus, the demand on effective and efficient bot detection algorithms is more urgent than ever. Existing works have partly satisfied this requirement by way of laborious feature engineering. In this paper, we propose a deep bot detection model aiming to learn an effective representation of social user and then detect social bots by jointly modeling social behavior and content information. The proposed model learns the representation of social behavior by encoding both endogenous and exogenous factors which affect user behavior. As to the representation of content, we regard the user content as temporal text data instead of just plain text as be treated in other existing works to extract semantic information and latent temporal patterns. To the best of our knowledge, this is the first trial that applies deep learning in modeling social users and accomplishing social bot detection. Experiments on real world dataset collected from Twitter demonstrate the effectiveness of the proposed model.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73428419","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: 45

Capturing Feature-Level Irregularity in Disease Progression Modeling 捕获疾病进展建模中的特征级不规则性

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132944

Kaiping Zheng, Wei Wang, Jinyang Gao, K. Ngiam, B. Ooi, J. Yip

引用次数: 31

Automatic Navbox Generation by Interpretable Clustering over Linked Entities 链接实体上可解释聚类的自动导航框生成

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132899

Chenhao Xie, Lihan Chen, Jiaqing Liang, Kezun Zhang, Yanghua Xiao, Hanghang Tong, Haixun Wang, Wei Wang

引用次数: 1

Deception Detection: When Computers Become Better than Humans 欺骗检测:当计算机变得比人类更好

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3137174

Rada Mihalcea

引用次数: 0

FM-Hawkes: A Hawkes Process Based Approach for Modeling Online Activity Correlations 基于Hawkes过程的在线活动相关性建模方法

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132883

Sha Li, Xiaofeng Gao, Weiming Bao, Guihai Chen

{"title":"FM-Hawkes: A Hawkes Process Based Approach for Modeling Online Activity Correlations","authors":"Sha Li, Xiaofeng Gao, Weiming Bao, Guihai Chen","doi":"10.1145/3132847.3132883","DOIUrl":"https://doi.org/10.1145/3132847.3132883","url":null,"abstract":"Understanding and predicting user behavior on online platforms has proved to be of significant value, with applications spanning from targeted advertising, political campaigning, anomaly detection to user self-monitoring. With the growing functionality and flexibility of online platforms, users can now accomplish a variety of tasks online. This advancement has rendered many previous works that focus on modeling a single type of activity obsolete. In this work, we target this new problem by modeling the interplay between the time series of different types of activities and apply our model to predict future user behavior. Our model, FM-Hawkes, stands for Fourier-based kernel multi-dimensional Hawkes process. Specifically, we model the multiple activity time series as a multi-dimensional Hawkes process. The correlations between different types of activities are then captured by the influence factor. As for the temporal triggering kernel, we observe that the intensity function consists of numerous kernel functions with time shift. Thus, we employ a Fourier transformation based non-parametric estimation. Our model is not bound to any particular platform and explicitly interprets the causal relationship between actions. By applying our model to real-life datasets, we confirm that the mutual excitation effect between different activities prevails among users. Prediction results show our superiority over models that do not consider action types and flexible kernels","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76157235","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: 18

Query and Animate Multi-attribute Trajectory Data 查询和动画多属性轨迹数据

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133178

Jianqiu Xu, R. H. Güting

引用次数: 2

Source Retrieval for Web-Scale Text Reuse Detection web规模文本重用检测的源检索

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133097

Matthias Hagen, Martin Potthast, Payam Adineh, Ehsan Fatehifar, Benno Stein

引用次数: 17

Additional Workshops Co-located with CIKM 2017 与CIKM 2017同期举办的额外研讨会

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3152359

M. Winslett

引用次数: 0