Proceedings of the 2017 ACM on Conference on Information and Knowledge Management最新文献_第8页

Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining 利用对比模式挖掘总结网络流量的重大变化

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133111

E. Chavary, S. Erfani, C. Leckie

{"title":"Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining","authors":"E. Chavary, S. Erfani, C. Leckie","doi":"10.1145/3132847.3133111","DOIUrl":"https://doi.org/10.1145/3132847.3133111","url":null,"abstract":"Extracting knowledge from the massive volumes of network traffic is an important challenge in network and security management. In particular, network managers require concise reports about significant changes in their network traffic. While most existing techniques focus on summarizing a single traffic dataset, the problem of finding significant differences between multiple datasets is an open challenge. In this paper, we focus on finding important differences between network traffic datasets, and preparing a summarized and interpretable report for security managers. We propose the use of contrast pattern mining, which finds patterns whose support differs significantly from one dataset to another. We show that contrast patterns are highly effective at extracting meaningful changes in traffic data. We also propose several evaluation metrics that reflect the interpretability of patterns for security managers. Our experimental results show that with the proposed unsupervised approach, the vast majority of extracted patterns are pure, i.e., most changes are either attack traffic or normal traffic, but not a mixture of both.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85625120","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: 7

A Robust Named-Entity Recognition System Using Syllable Bigram Embedding with Eojeol Prefix Information 一种基于音节重图嵌入词形前缀信息的鲁棒命名实体识别系统

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133105

Sunjae Kwon, Youngjoong Ko, Jungyun Seo

引用次数: 4

PQBF: I/O-Efficient Approximate Nearest Neighbor Search by Product Quantization PQBF:基于积量化的I/ o高效近似近邻搜索

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132901

Yingfan Liu, Hong Cheng, Jiangtao Cui

{"title":"PQBF: I/O-Efficient Approximate Nearest Neighbor Search by Product Quantization","authors":"Yingfan Liu, Hong Cheng, Jiangtao Cui","doi":"10.1145/3132847.3132901","DOIUrl":"https://doi.org/10.1145/3132847.3132901","url":null,"abstract":"Approximate nearest neighbor (ANN) search in high-dimensional space plays an essential role in many multimedia applications. Recently, product quantization (PQ) based methods for ANN search have attracted enormous attention in the community of computer vision, due to its good balance between accuracy and space requirement. PQ based methods embed a high-dimensional vector into a short binary code (called PQ code), and the squared Euclidean distance is estimated by asymmetric quantizer distance (AQD) with pretty high precision. Thus, ANN search in the original space can be converted to similarity search on AQD using the PQ approach. All existing PQ methods are in-memory solutions, which may not handle massive data if they cannot fit entirely in memory. In this paper, we propose an I/O-efficient PQ based solution for ANN search. We design an index called PQB+-forest to support efficient similarity search on AQD. PQB+-forest first creates a number of partitions of the PQ codes by a coarse quantizer and then builds a B+-tree, called PQB+-tree, for each partition. The search process is greatly expedited by focusing on a few selected partitions that are closest to the query, as well as by the pruning power of PQB+-trees. According to the experiments conducted on two large-scale data sets containing up to 1 billion vectors, our method outperforms its competitors, including the state-of-the-art PQ method and the state-of-the-art LSH methods for ANN search.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"70 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87402271","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: 12

Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation 基于特征表示的噪声对比迁移学习的内容推荐

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132855

Yiyang Li, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang

{"title":"Content Recommendation by Noise Contrastive Transfer Learning of Feature Representation","authors":"Yiyang Li, Guanyu Tao, Weinan Zhang, Yong Yu, Jun Wang","doi":"10.1145/3132847.3132855","DOIUrl":"https://doi.org/10.1145/3132847.3132855","url":null,"abstract":"Personalized recommendation has been proved effective as a content discovery tool for many online news publishers. As fresh news articles are frequently coming to the system while the old ones are fading away quickly, building a consistent and coherent feature representation over the ever-changing articles pool is fundamental to the performance of the recommendation. However, learning a good feature representation is challenging, especially for some small publishers that have normally fewer than 10,000 articles each year. In this paper, we consider to transfer knowledge from a larger text corpus. In our proposed solution, an effective article recommendation engine can be established with a small number of target publisher articles by transferring knowledge from a large corpus of text with a different distribution. Specifically, we leverage noise contrastive estimation techniques to learn the word conditional distribution given the context words, where the noise conditional distribution is pre-trained from the large corpus. Our solution has been deployed in a commercial recommendation service. The large-scale online A/B testing on two commercial publishers demonstrates up to 9.97% relative overall performance gain of our proposed model on the recommendation click-though rate metric over the non-transfer learning baselines.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82199554","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: 4

Maintaining Densest Subsets Efficiently in Evolving Hypergraphs 演化超图中最密集子集的有效维护

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132907

Shuguang Hu, Xiaowei Wu, T-H. Hubert Chan

{"title":"Maintaining Densest Subsets Efficiently in Evolving Hypergraphs","authors":"Shuguang Hu, Xiaowei Wu, T-H. Hubert Chan","doi":"10.1145/3132847.3132907","DOIUrl":"https://doi.org/10.1145/3132847.3132907","url":null,"abstract":"In this paper we study the densest subgraph problem, which plays a key role in many graph mining applications. The goal of the problem is to find a subset of nodes that induces a graph with maximum average degree. The problem has been extensively studied in the past few decades under a variety of different settings. Several exact and approximation algorithms were proposed. However, as normal graph can only model objects with pairwise relationships, the densest subgraph problem fails in identifying communities under relationships that involve more than 2 objects, e.g., in a network connecting authors by publications. We consider in this work the densest subgraph problem in hypergraphs, which generalizes the problem to a wider class of networks in which edges might have different cardinalities and contain more than 2 nodes. We present two exact algorithms and a near-linear time r-approximation algorithm for the problem, where r is the maximum cardinality of an edge in the hypergraph. We also consider the dynamic version of the problem, in which an adversary can insert or delete an edge from the hypergraph in each round and the goal is to maintain efficiently an approximation of the densest subgraph. We present two dynamic approximation algorithms in this paper with amortized polog update time, for any ε > 0. For the case when there are only insertions, the approximation ratio we maintain is r(1+ε), while for the fully dynamic case, the ratio is r2(1+ε). Extensive experiments are performed on large real datasets to validate the effectiveness and efficiency of our algorithms.","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":"81361861","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: 36

J-REED: Joint Relation Extraction and Entity Disambiguation J-REED:联合关系提取与实体消歧

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133090

Dat Ba Nguyen, M. Theobald, G. Weikum

引用次数: 10

A Neural Collaborative Filtering Model with Interaction-based Neighborhood 基于交互邻域的神经协同过滤模型

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133083

Ting Bai, Ji-Rong Wen, Jun Zhang, Wayne Xin Zhao

引用次数: 93

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection 一种非负对称编码器-解码器社区检测方法

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132902

Bing-Jie Sun, Huawei Shen, Jinhua Gao, W. Ouyang, Xueqi Cheng

{"title":"A Non-negative Symmetric Encoder-Decoder Approach for Community Detection","authors":"Bing-Jie Sun, Huawei Shen, Jinhua Gao, W. Ouyang, Xueqi Cheng","doi":"10.1145/3132847.3132902","DOIUrl":"https://doi.org/10.1145/3132847.3132902","url":null,"abstract":"Community detection or graph clustering is crucial to understanding the structure of complex networks and extracting relevant knowledge from networked data. Latent factor model, e.g., non-negative matrix factorization and mixed membership block model, is one of the most successful methods for community detection. Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes. Existing latent factor models are mainly based on reconstructing a network from the representation of its nodes, namely network decoder, while constraining the representation to have certain desirable properties. These methods, however, lack an encoder that transforms nodes into their representation. Consequently, they fail to give a clear explanation about the meaning of a community and suffer from undesired computational problems. In this paper, we propose a non-negative symmetric encoder-decoder approach for community detection. By explicitly integrating a decoder and an encoder into a unified loss function, the proposed approach achieves better performance over state-of-the-art latent factor models for community detection task. Moreover, different from existing methods that explicitly impose the sparsity constraint on the representation of nodes, the proposed approach implicitly achieves the sparsity of node representation through its symmetric and non-negative properties, making the optimization much easier than competing methods based on sparse matrix factorization.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90817189","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: 56

Interactive Social Recommendation 互动社交推荐

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3132880

Xin Wang, S. Hoi, Chenghao Liu, M. Ester

{"title":"Interactive Social Recommendation","authors":"Xin Wang, S. Hoi, Chenghao Liu, M. Ester","doi":"10.1145/3132847.3132880","DOIUrl":"https://doi.org/10.1145/3132847.3132880","url":null,"abstract":"Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is beneficial for improving recommendation accuracy, especially when dealing with cold-start users who lack sufficient past behavior information for accurate recommendation. However, it is nontrivial to use such information, since some of a person's friends may share similar preferences in certain aspects, but others may be totally irrelevant for recommendations. Thus one challenge is to explore and exploit the extend to which a user trusts his/her friends when utilizing social information to improve recommendations. On the other hand, most existing social recommendation models are non-interactive in that their algorithmic strategies are based on batch learning methodology, which learns to train the model in an offline manner from a collection of training data which are accumulated from users? historical interactions with the recommender systems. In the real world, new users may leave the systems for the reason of being recommended with boring items before enough data is collected for training a good model, which results in an inefficient customer retention. To tackle these challenges, we propose a novel method for interactive social recommendation, which not only simultaneously explores user preferences and exploits the effectiveness of personalization in an interactive way, but also adaptively learns different weights for different friends. In addition, we also give analyses on the complexity and regret of the proposed model. Extensive experiments on three real-world datasets illustrate the improvement of our proposed method against the state-of-the-art algorithms.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91348920","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

SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities SMASC 2017:首届智慧城市社交媒体分析国际研讨会

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management Pub Date : 2017-11-06 DOI: 10.1145/3132847.3133199

Manjira Sinha, Xiangnan He, A. Bozzon, Sandya Mannarswamy, P. Murukannaiah, Tridib Mukherjee

{"title":"SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities","authors":"Manjira Sinha, Xiangnan He, A. Bozzon, Sandya Mannarswamy, P. Murukannaiah, Tridib Mukherjee","doi":"10.1145/3132847.3133199","DOIUrl":"https://doi.org/10.1145/3132847.3133199","url":null,"abstract":"In an increasingly digital urban setting, connected & concerned Citizens typically voice their opinions on various civic topics via social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is essential to the development of smart cities. The very nature of the data: heterogeneity and dynamism, the scarcity of gold standard annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, and civic authorities, to develop scalable and practical systems for capturing and acting upon real world issues of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.","PeriodicalId":20449,"journal":{"name":"Proceedings of the 2017 ACM on Conference on Information and Knowledge Management","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80648217","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