Proceedings of the Ninth ACM International Conference on Web Search and Data Mining最新文献_第5页

Session details: Leveraging Users 会话细节:利用用户

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253876

Y. Maarek

引用次数: 0

Geographic Segmentation via Latent Poisson Factor Model 基于潜泊松因子模型的地理分割

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835806

Rose Yu, A. Gelfand, Suju Rajan, C. Shahabi, Yan Liu

引用次数: 4

WSDM Cup 2016: Entity Ranking Challenge WSDM杯2016:实体排名挑战赛

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2855119

Alex D Wade, Kuansan Wang, Yizhou Sun, Antonio Gullì

引用次数: 25

The Troll-Trust Model for Ranking in Signed Networks 签名网络中排名的巨魔-信任模型

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835816

Zhaoming Wu, C. Aggarwal, Jimeng Sun

引用次数: 58

User Modeling in Large Social Networks 大型社交网络中的用户建模

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2855087

Yuxiao Dong

引用次数: 3

A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering 基于语义图的社区问答问题检索主题模型

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835809

Long Chen, J. Jose, Haitao Yu, Fajie Yuan, Dell Zhang

{"title":"A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering","authors":"Long Chen, J. Jose, Haitao Yu, Fajie Yuan, Dell Zhang","doi":"10.1145/2835776.2835809","DOIUrl":"https://doi.org/10.1145/2835776.2835809","url":null,"abstract":"Community Question Answering (CQA) services, such as Yahoo! Answers and WikiAnswers, have become popular with users as one of the central paradigms for satisfying users' information needs. The task of question retrieval aims to resolve one's query directly by finding the most relevant questions (together with their answers) from an archive of past questions. However, as the text of each question is short, there is usually a lexical gap between the queried question and the past questions. To alleviate this problem, we present a hybrid approach that blends several language modelling techniques for question retrieval, namely, the classic (query-likelihood) language model, the state-of-the-art translation-based language model, and our proposed semantics-based language model. The semantics of each candidate question is given by a probabilistic topic model which makes use of local and global semantic graphs for capturing the hidden interactions among entities (e.g., people, places, and concepts) in question-answer pairs. Experiments on two real-world datasets show that our approach can significantly outperform existing ones.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75923164","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: 34

Long-tail Vocabulary Dictionary Extraction from the Web Web长尾词汇词典提取

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835778

Zhe Chen, Michael J. Cafarella, H. Jagadish

{"title":"Long-tail Vocabulary Dictionary Extraction from the Web","authors":"Zhe Chen, Michael J. Cafarella, H. Jagadish","doi":"10.1145/2835776.2835778","DOIUrl":"https://doi.org/10.1145/2835776.2835778","url":null,"abstract":"A dictionary --- a set of instances belonging to the same conceptual class --- is central to information extraction and is a useful primitive for many applications, including query log analysis and document categorization. Considerable work has focused on generating accurate dictionaries given a few example seeds, but methods to date cannot obtain long-tail (rare) items with high accuracy and recall. In this paper, we develop a novel method to construct high-quality dictionaries, especially for long-tail vocabularies, using just a few user-provided seeds for each topic. Our algorithm obtains long-tail (i.e., rare) items by building and executing high-quality webpage-specific extractors. We use webpage-specific structural and textual information to build more accurate per-page extractors in order to detect the long-tail items from a single webpage. These webpage-specific extractors are obtained via a co-training procedure using distantly-supervised training data. By aggregating the page-specific dictionaries of many webpages, Lyretail is able to output a high-quality comprehensive dictionary. Our experiments demonstrate that in long-tail vocabulary settings, we obtained a 17.3% improvement on mean average precision for the dictionary generation process, and a 30.7% improvement on F1 for the page-specific extraction, when compared to previous state-of-the-art methods.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"66 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80219306","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: 38

Ensemble Models for Data-driven Prediction of Malware Infections 数据驱动的恶意软件感染预测集成模型

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835834

Chanhyun Kang, Noseong Park, B. Prakash, Edoardo Serra, V. S. Subrahmanian

{"title":"Ensemble Models for Data-driven Prediction of Malware Infections","authors":"Chanhyun Kang, Noseong Park, B. Prakash, Edoardo Serra, V. S. Subrahmanian","doi":"10.1145/2835776.2835834","DOIUrl":"https://doi.org/10.1145/2835776.2835834","url":null,"abstract":"Given a history of detected malware attacks, can we predict the number of malware infections in a country? Can we do this for different malware and countries? This is an important question which has numerous implications for cyber security, right from designing better anti-virus software, to designing and implementing targeted patches to more accurately measuring the economic impact of breaches. This problem is compounded by the fact that, as externals, we can only detect a fraction of actual malware infections. In this paper we address this problem using data from Symantec covering more than 1.4 million hosts and 50 malware spread across 2 years and multiple countries. We first carefully design domain-based features from both malware and machine-hosts perspectives. Secondly, inspired by epidemiological and information diffusion models, we design a novel temporal non-linear model for malware spread and detection. Finally we present ESM, an ensemble-based approach which combines both these methods to construct a more accurate algorithm. Using extensive experiments spanning multiple malware and countries, we show that ESM can effectively predict malware infection ratios over time (both the actual number and trend) upto 4 times better compared to several baselines on various metrics. Furthermore, ESM's performance is stable and robust even when the number of detected infections is low.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81832523","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: 30

Learning Distributed Representations of Data in Community Question Answering for Question Retrieval 面向问题检索的社区问答数据分布式表示学习

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/2835776.2835786

Kai Zhang, Wei Wu, Fang Wang, M. Zhou, Zhoujun Li

{"title":"Learning Distributed Representations of Data in Community Question Answering for Question Retrieval","authors":"Kai Zhang, Wei Wu, Fang Wang, M. Zhou, Zhoujun Li","doi":"10.1145/2835776.2835786","DOIUrl":"https://doi.org/10.1145/2835776.2835786","url":null,"abstract":"We study the problem of question retrieval in community question answering (CQA). The biggest challenge within this task is lexical gaps between questions since similar questions are usually expressed with different but semantically related words. To bridge the gaps, state-of-the-art methods incorporate extra information such as word-to-word translation and categories of questions into the traditional language models. We find that the existing language model based methods can be interpreted using a new framework, that is they represent words and question categories in a vector space and calculate question-question similarities with a linear combination of dot products of the vectors. The problem is that these methods are either heuristic on data representation or difficult to scale up. We propose a principled and efficient approach to learning representations of data in CQA. In our method, we simultaneously learn vectors of words and vectors of question categories by optimizing an objective function naturally derived from the framework. In question retrieval, we incorporate learnt representations into traditional language models in an effective and efficient way. We conduct experiments on large scale data from Yahoo! Answers and Baidu Knows, and compared our method with state-of-the-art methods on two public data sets. Experimental results show that our method can significantly improve on baseline methods for retrieval relevance. On 1 million training data, our method takes less than 50 minutes to learn a model on a single multicore machine, while the translation based language model needs more than 2 days to learn a translation table on the same machine.","PeriodicalId":20567,"journal":{"name":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86304431","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: 34

Session details: Entities and Structure 会话详细信息:实体和结构

Proceedings of the Ninth ACM International Conference on Web Search and Data Mining Pub Date : 2016-02-08 DOI: 10.1145/3253880

Lada A. Adamic

引用次数: 0