Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining最新文献_第3页

STAR: A System for Ticket Analysis and Resolution STAR:票证分析和解析系统

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098190

Wubai Zhou, Wei Xue, Ramesh Baral, Qing Wang, Chunqiu Zeng, Tao Li, Jian Xu, Zheng Liu, L. Shwartz, G. Grabarnik

{"title":"STAR: A System for Ticket Analysis and Resolution","authors":"Wubai Zhou, Wei Xue, Ramesh Baral, Qing Wang, Chunqiu Zeng, Tao Li, Jian Xu, Zheng Liu, L. Shwartz, G. Grabarnik","doi":"10.1145/3097983.3098190","DOIUrl":"https://doi.org/10.1145/3097983.3098190","url":null,"abstract":"In large scale and complex IT service environments, a problematic incident is logged as a ticket and contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary. In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets, along with their quality scores obtained from the resolution quality quantification, are then used to train a deep neural network ranking model that outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115320971","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

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform TFX:基于tensorflow的生产规模机器学习平台

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098021

Denis Baylor, Eric Breck, Heng-Tze Cheng, Noah Fiedel, Chuan Yu Foo, Zakaria Haque, Salem Haykal, M. Ispir, Vihan Jain, L. Koc, C. Koo, Lukasz Lew, Clemens Mewald, A. Modi, N. Polyzotis, Sukriti Ramesh, Sudip Roy, Steven Euijong Whang, M. Wicke, Jarek Wilkiewicz, Xin Zhang, Martin A. Zinkevich

{"title":"TFX: A TensorFlow-Based Production-Scale Machine Learning Platform","authors":"Denis Baylor, Eric Breck, Heng-Tze Cheng, Noah Fiedel, Chuan Yu Foo, Zakaria Haque, Salem Haykal, M. Ispir, Vihan Jain, L. Koc, C. Koo, Lukasz Lew, Clemens Mewald, A. Modi, N. Polyzotis, Sukriti Ramesh, Sudip Roy, Steven Euijong Whang, M. Wicke, Jarek Wilkiewicz, Xin Zhang, Martin A. Zinkevich","doi":"10.1145/3097983.3098021","DOIUrl":"https://doi.org/10.1145/3097983.3098021","url":null,"abstract":"Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components---a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present TensorFlow Extended (TFX), a TensorFlow-based general-purpose machine learning platform implemented at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes disruptions. We present the case study of one deployment of TFX in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying TFX led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134459681","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: 355

A Taxi Order Dispatch Model based On Combinatorial Optimization 基于组合优化的出租车订单调度模型

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098138

Lingyu Zhang, Tao Hu, Yue Min, Guobin Wu, Junying Zhang, Pengcheng Feng, Pinghua Gong, Jieping Ye

{"title":"A Taxi Order Dispatch Model based On Combinatorial Optimization","authors":"Lingyu Zhang, Tao Hu, Yue Min, Guobin Wu, Junying Zhang, Pengcheng Feng, Pinghua Gong, Jieping Ye","doi":"10.1145/3097983.3098138","DOIUrl":"https://doi.org/10.1145/3097983.3098138","url":null,"abstract":"Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories. We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133104970","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: 180

Inferring the Strength of Social Ties: A Community-Driven Approach 推断社会关系的强度:社区驱动的方法

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098199

Polina Rozenshtein, Nikolaj Tatti, A. Gionis

{"title":"Inferring the Strength of Social Ties: A Community-Driven Approach","authors":"Polina Rozenshtein, Nikolaj Tatti, A. Gionis","doi":"10.1145/3097983.3098199","DOIUrl":"https://doi.org/10.1145/3097983.3098199","url":null,"abstract":"Online social networks are growing and becoming denser.The social connections of a given person may have very high variability: from close friends and relatives to acquaintances to people who hardly know. Inferring the strength of social ties is an important ingredient for modeling the interaction of users in a network and understanding their behavior. Furthermore, the problem has applications in computational social science, viral marketing, and people recommendation. In this paper we study the problem of inferring the strength of social ties in a given network. Our work is motivated by a recent approach by Sintos et. al [24], which leverages the Strong Triadic Closure} STC principle, a hypothesis rooted in social psychology. To guide our inference process, in addition to the network structure, we also consider as input a collection of tight communities. Those are sets of vertices that we expect to be connected via strong ties. Such communities appear in different situations, e.g., when being part of a community implies a strong connection to one of the existing members. We consider two related problem formalizations that reflect the assumptions of our setting: small number of STC violations and strong-tie connectivity in the input communities. We show that both problem formulations are NP-hard. We also show that one problem formulation is hard to approximate, while for the second we develop an algorithm with approximation guarantee. We validate the proposed method on real-world datasets by comparing with baselines that optimize STC violations and community connectivity separately.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114374181","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

A Practical Exploration System for Search Advertising 一个实用的搜索广告探索系统

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098041

P. Shah, Ming Yang, Sachidanand Alle, A. Ratnaparkhi, B. Shahshahani, Rohit Chandra

引用次数: 11

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes 青光眼视网膜厚度与视敏的多视点学习

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098194

Toshimitsu Uesaka, K. Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, R. Asaoka, K. Yamanishi

{"title":"Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes","authors":"Toshimitsu Uesaka, K. Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, R. Asaoka, K. Yamanishi","doi":"10.1145/3097983.3098194","DOIUrl":"https://doi.org/10.1145/3097983.3098194","url":null,"abstract":"Dense measurements of visual-field, which is necessary to detect glaucoma, is known as very costly and labor intensive. Recently, measurement of retinal-thickness can be less costly than measurement of visual-field. Thus, it is sincerely desired that the retinal-thickness could be transformed into visual-sensitivity data somehow. In this paper, we propose two novel methods to estimate the sensitivity of the visual-field with SITA-Standard mode 10-2 resolution using retinal-thickness data measured with optical coherence tomography (OCT). The first method called Affine-Structured Non-negative Matrix Factorization (ASNMF) which is able to cope with both the estimation of visual-field and the discovery of deep glaucoma knowledge. While, the second is based on Convolutional Neural Networks (CNNs) which demonstrates very high estimation performance. These methods are kinds of multi-view learning methods because they utilize visual-field and retinal thickness data simultaneously. We experimentally tested the performance of our methods from several perspectives. We found that ASNMF worked better for relatively small data size while CNNs did for relatively large data size. In addition, some clinical knowledge are discovered via ASNMF. To the best of our knowledge, this is the first paper to address the dense estimation of the visual-field based on the retinal-thickness data.","PeriodicalId":314049,"journal":{"name":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122659621","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

Estimation of Recent Ancestral Origins of Individuals on a Large Scale 大规模个体近世祖先起源的估计

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098042

Ross E. Curtis, A. Girshick

引用次数: 2

A Quasi-experimental Estimate of the Impact of P2P Transportation Platforms on Urban Consumer Patterns P2P交通平台对城市消费模式影响的准实验研究

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098058

Zhe Zhang, Beibei Li

引用次数: 15

Resolving the Bias in Electronic Medical Records 解决电子病历中的偏见

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098149

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

引用次数: 45

Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data 并非所有的传球都是平等的:从跟踪数据客观地衡量足球中传球的风险和回报

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Pub Date : 2017-08-13 DOI: 10.1145/3097983.3098051

P. Power, Héctor Ruiz, Xinyu Wei, P. Lucey

引用次数: 82