Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining最新文献

Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction 基于指针网络的航线行程预测深度选择模型

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098005

Alejandro Mottini, Rodrigo Acuna-Agost

{"title":"Deep Choice Model Using Pointer Networks for Airline Itinerary Prediction","authors":"Alejandro Mottini, Rodrigo Acuna-Agost","doi":"10.1145/3097983.3098005","DOIUrl":"https://doi.org/10.1145/3097983.3098005","url":null,"abstract":"Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Some common applications are not only filtering and sorting alternatives, but also changing certain attributes in real-time (e.g., changing the price). In this paper, we concentrate with the problem of modeling air passenger choices of flight itineraries. This problem has historically been tackled using classical Discrete Choice Modelling techniques. Traditional statistical approaches, in particular the Multinomial Logit model (MNL), is widely used in industrial applications due to its simplicity and general good performance. However, MNL models present several shortcomings and assumptions that might not hold in real applications. To overcome these difficulties, we present a new choice model based on Pointer Networks. Given an input sequence, this type of deep neural architecture combines Recurrent Neural Networks with the Attention Mechanism to learn the conditional probability of an output whose values correspond to positions in an input sequence. Therefore, given a sequence of different alternatives presented to a customer, the model can learn to point to the one most likely to be chosen by the customer. The proposed method was evaluated on a real dataset that combines on-line user search logs and airline flight bookings. Experimental results show that the proposed model outperforms the traditional MNL model on several metrics.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114282416","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: 49

A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics 一个时间异质性生存框架及其在社会行为动力学中的应用

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098189

Linyun Yu, Peng Cui, Chaoming Song, T. Zhang, Shiqiang Yang

{"title":"A Temporally Heterogeneous Survival Framework with Application to Social Behavior Dynamics","authors":"Linyun Yu, Peng Cui, Chaoming Song, T. Zhang, Shiqiang Yang","doi":"10.1145/3097983.3098189","DOIUrl":"https://doi.org/10.1145/3097983.3098189","url":null,"abstract":"Social behavior dynamics is one of the central building blocks in understanding and modeling complex social dynamic phenomena, such as information spreading, opinion formation, and social mobilization. While a wide range of models for social behavior dynamics have been proposed in recent years, the essential ingredients and the minimum model for social behavior dynamics is still largely unanswered. Here, we find that human interaction behavior dynamics exhibit rich complexities over the response time dimension and natural time dimension by exploring a large scale social communication dataset. To tackle this challenge, we develop a temporal Heterogeneous Survival framework where the regularities in response time dimension and natural time dimension can be organically integrated. We apply our model in two online social communication datasets. Our model can successfully regenerate the interaction patterns in the social communication datasets, and the results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines. Meanwhile, the learnt parameters and discovered statistical regularities can lead to multiple potential applications.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114515623","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: 13

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments 肮脏的一打:在线控制实验中12个常见的度量解释陷阱

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098024

Pavel A. Dmitriev, Somit Gupta, Dong Woo Kim, G. J. Vaz

{"title":"A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments","authors":"Pavel A. Dmitriev, Somit Gupta, Dong Woo Kim, G. J. Vaz","doi":"10.1145/3097983.3098024","DOIUrl":"https://doi.org/10.1145/3097983.3098024","url":null,"abstract":"Online controlled experiments (e.g., A/B tests) are now regularly used to guide product development and accelerate innovation in software. Product ideas are evaluated as scientific hypotheses, and tested in web sites, mobile applications, desktop applications, services, and operating systems. One of the key challenges for organizations that run controlled experiments is to come up with the right set of metrics [1] [2] [3]. Having good metrics, however, is not enough. In our experience of running thousands of experiments with many teams across Microsoft, we observed again and again how incorrect interpretations of metric movements may lead to wrong conclusions about the experiment's outcome, which if deployed could hurt the business by millions of dollars. Inspired by Steven Goodman's twelve p-value misconceptions [4], in this paper, we share twelve common metric interpretation pitfalls which we observed repeatedly in our experiments. We illustrate each pitfall with a puzzling example from a real experiment, and describe processes, metric design principles, and guidelines that can be used to detect and avoid the pitfall. With this paper, we aim to increase the experimenters' awareness of metric interpretation issues, leading to improved quality and trustworthiness of experiment results and better data-driven decisions.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129674092","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: 91

Supporting Employer Name Normalization at both Entity and Cluster Level 在实体和集群级别支持雇主名称规范化

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098093

Qiaoling Liu, F. Javed, Vachik S. Dave, Ankita Joshi

{"title":"Supporting Employer Name Normalization at both Entity and Cluster Level","authors":"Qiaoling Liu, F. Javed, Vachik S. Dave, Ankita Joshi","doi":"10.1145/3097983.3098093","DOIUrl":"https://doi.org/10.1145/3097983.3098093","url":null,"abstract":"In the recruitment domain, the employer name normalization task, which links employer names in job postings or resumes to entities in an employer knowledge base (KB), is important to many business applications. In previous work, we proposed the CompanyDepot system, which used machine learning techniques to address the problem. After applying it to several applications at CareerBuilder, we faced several new challenges: 1) how to avoid duplicate normalization results when the KB is noisy and contains many duplicate entities; 2) how to address the vocabulary gap between query names and entity names in the KB; and 3) how to use the context available in jobs and resumes to improve normalization quality. To address these challenges, in this paper we extend the previous CompanyDepot system to normalize employer names not only at entity level, but also at cluster level by mapping a query to a cluster in the KB that best matches the query. We also propose a new metric based on success rate and diversity reduction ratio for evaluating the cluster-level normalization. Moreover, we perform query expansion based on five data sources to address the vocabulary gap challenge and leverage the url context for the employer names in many jobs and resumes to improve normalization quality. We show that the proposed CompanyDepot-V2 system outperforms the previous CompanyDepot system and several other baseline systems over multiple real-world datasets. We also demonstrate the large improvement on normalization quality from entity-level to cluster-level normalization.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123188634","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: 9

Quick Access: Building a Smart Experience for Google Drive 快速访问:建立一个智能体验的谷歌驱动器

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098048

Sandeep Tata, Alexandrin Popescul, Marc Najork, Mike Colagrosso, Julian Gibbons, Alan Green, Alexandre Mah, Michael Smith, Divanshu Garg, Cayden Meyer, Reuben Kan

引用次数: 24

HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network 基于结构化异构信息网络的Android恶意软件智能检测系统HinDroid

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098026

Shifu Hou, Yanfang Ye, Yangqiu Song, Melih Abdulhayoglu

{"title":"HinDroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network","authors":"Shifu Hou, Yanfang Ye, Yangqiu Song, Melih Abdulhayoglu","doi":"10.1145/3097983.3098026","DOIUrl":"https://doi.org/10.1145/3097983.3098026","url":null,"abstract":"With explosive growth of Android malware and due to the severity of its damages to smart phone users, the detection of Android malware has become increasingly important in cybersecurity. The increasing sophistication of Android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect Android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more effort for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for Android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115854841","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: 223

Predicting Clinical Outcomes Across Changing Electronic Health Record Systems 通过不断变化的电子健康记录系统预测临床结果

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098064

Jen J. Gong, Tristan Naumann, Peter Szolovits, J. Guttag

引用次数: 35

Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines 信心调度:露天矿机器学习、优化与仿真的集成

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098178

Kosta Ristovski, Chetan Gupta, Kunihiko Harada, Hsiu-Khuern Tang

{"title":"Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines","authors":"Kosta Ristovski, Chetan Gupta, Kunihiko Harada, Hsiu-Khuern Tang","doi":"10.1145/3097983.3098178","DOIUrl":"https://doi.org/10.1145/3097983.3098178","url":null,"abstract":"Open pit mining operations require utilization of extremely expensive equipment such as large trucks, shovels and loaders. To remain competitive, mining companies are under pressure to increase equipment utilization and reduce operational costs. The key to this in mining operations is to have sophisticated truck assignment strategies which will ensure that equipment is utilized efficiently with minimum operating cost. To address this problem, we have implemented truck assignment approach which integrates machine learning, linear/integer programming and simulation. Our truck assignment approach takes into consideration the number of trucks and their sizes, shovels and dump locations as well as stochastic activity times during the operations. Machine learning is used to predict probability distributions of equipment activity duration. We have validated the approach using data collected from two open pit mines. Our experimental results show that our approach offers increase of 10% in efficiency. Presented results demonstrate that machine learning can bring significant value to mining industry.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131513425","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

Stock Price Prediction via Discovering Multi-Frequency Trading Patterns 通过发现多频率交易模式预测股票价格

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098117

Liheng Zhang, C. Aggarwal, Guo-Jun Qi

{"title":"Stock Price Prediction via Discovering Multi-Frequency Trading Patterns","authors":"Liheng Zhang, C. Aggarwal, Guo-Jun Qi","doi":"10.1145/3097983.3098117","DOIUrl":"https://doi.org/10.1145/3097983.3098117","url":null,"abstract":"Stock prices are formed based on short and/or long-term commercial and trading activities that reflect different frequencies of trading patterns. However, these patterns are often elusive as they are affected by many uncertain political-economic factors in the real world, such as corporate performances, government policies, and even breaking news circulated across markets. Moreover, time series of stock prices are non-stationary and non-linear, making the prediction of future price trends much challenging. To address them, we propose a novel State Frequency Memory (SFM) recurrent network to capture the multi-frequency trading patterns from past market data to make long and short term predictions over time. Inspired by Discrete Fourier Transform (DFT), the SFM decomposes the hidden states of memory cells into multiple frequency components, each of which models a particular frequency of latent trading pattern underlying the fluctuation of stock price. Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion. Modeling multi-frequency trading patterns can enable more accurate predictions for various time ranges: while a short-term prediction usually depends on high frequency trading patterns, a long-term prediction should focus more on the low frequency trading patterns targeting at long-term return. Unfortunately, no existing model explicitly distinguishes between various frequencies of trading patterns to make dynamic predictions in literature. The experiments on the real market data also demonstrate more competitive performance by the SFM as compared with the state-of-the-art methods.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133012598","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: 265

Internet Device Graphs 互联网设备图

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098114

Matthew Malloy, P. Barford, Enis Ceyhun Alp, Jonathan Koller, Adria Jewell

{"title":"Internet Device Graphs","authors":"Matthew Malloy, P. Barford, Enis Ceyhun Alp, Jonathan Koller, Adria Jewell","doi":"10.1145/3097983.3098114","DOIUrl":"https://doi.org/10.1145/3097983.3098114","url":null,"abstract":"Internet device graphs identify relationships between user-centric internet connected devices such as desktops, laptops, smartphones, tablets, gaming consoles, TV's, etc. The ability to create such graphs is compelling for online advertising, content customization, recommendation systems, security, and operations. We begin by describing an algorithm for generating a device graph based on IP-colocation, and then apply the algorithm to a corpus of over 2.5 trillion internet events collected over the period of six weeks in the United States. The resulting graph exhibits immense scale with greater than 7.3 billion edges (pair-wise relationships) between more than 1.2 billion nodes (devices), accounting for the vast majority of internet connected devices in the US. Next, we apply community detection algorithms to the graph resulting in a partitioning of internet devices into 100 million small communities representing physical households. We validate this partition with a unique ground truth dataset. We report on the characteristics of the graph and the communities. Lastly, we discuss the important issues of ethics and privacy that must be considered when creating and studying device graphs, and suggest further opportunities for device graph enrichment and application.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127065771","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: 10