M. Kirchgessner, V. Leroy, S. Amer-Yahia, Shashwati Mishra
Understanding customer buying patterns is of great interest to the retail industry. Association rule mining is a common technique for extracting correlations such as people in the South of France buy rosé wine or customers who buy paté also buy salted butter and sour bread. Unfortunately, sifting through a high number of buying patterns is not useful in practice, because of the predominance of popular products in the top rules. As a result, a number of "interestingness" measures (over 30) have been proposed to rank rules. However, there is no agreement on which measures are more appropriate for retail data. Moreover, since pattern mining algorithms output thousands of association rules for each product, the ability for an analyst to rely on ranking measures to identify the most interesting ones is crucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), a framework that provides analysts with the ability to compare different rule rankings. We report on how we used C A PA to compare 34 interestingness measures applied to patterns extracted from customer receipts of more than 1,800 stores for a period of one year.
了解顾客的购买模式对零售业来说是非常重要的。关联规则挖掘是一种用于提取相关性的常用技术，例如法国南部的人购买玫瑰红葡萄酒，或者购买pat的顾客也购买咸黄油和酸面包。不幸的是，筛选大量的购买模式在实践中是没有用的，因为热门产品在顶级规则中占主导地位。因此，人们提出了许多“有趣”的衡量标准(超过30个)来对规则进行排名。然而，对于哪种衡量方法更适合零售数据，目前还没有达成一致。此外，由于模式挖掘算法为每个产品输出数千个关联规则，因此分析人员依靠排名度量来识别最有趣的规则的能力至关重要。在本文中，我们开发了CAPA(模式比较分析)，这是一个为分析人员提供比较不同规则排名能力的框架。我们报告了我们如何使用C A PA来比较34个有趣的度量，这些度量适用于从1800多家商店的顾客收据中提取的模式，为期一年。
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A/B testing is one of the most successful applications of statistical theory in the Internet age. A crucial problem of Null Hypothesis Statistical Testing (NHST), the backbone of A/B testing methodology, is that experimenters are not allowed to continuously monitor the results and make decisions in real time. Many people see this restriction as a setback against the trend in the technology toward real time data analytics. Recently, Bayesian Hypothesis Testing, which intuitively is more suitable for real time decision making, attracted growing interest as a viable alternative to NHST. While corrections of NHST for the continuous monitoring setting are well established in the existing literature and known in A/B testing community, the debate over the issue of whether continuous monitoring is a proper practice in Bayesian testing exists among both academic researchers and general practitioners. In this paper, we formally prove the validity of Bayesian testing under proper stopping rules, and illustrate the theoretical results with concrete simulation illustrations. We point out common bad practices where stopping rules are not proper, and discuss how priors can be learned objectively. General guidelines for researchers and practitioners are also provided.
{"title":"Continuous Monitoring of A/B Tests without Pain: Optional Stopping in Bayesian Testing","authors":"Alex Deng, Jiannan Lu, Shouyuan Chen","doi":"10.1109/DSAA.2016.33","DOIUrl":"https://doi.org/10.1109/DSAA.2016.33","url":null,"abstract":"A/B testing is one of the most successful applications of statistical theory in the Internet age. A crucial problem of Null Hypothesis Statistical Testing (NHST), the backbone of A/B testing methodology, is that experimenters are not allowed to continuously monitor the results and make decisions in real time. Many people see this restriction as a setback against the trend in the technology toward real time data analytics. Recently, Bayesian Hypothesis Testing, which intuitively is more suitable for real time decision making, attracted growing interest as a viable alternative to NHST. While corrections of NHST for the continuous monitoring setting are well established in the existing literature and known in A/B testing community, the debate over the issue of whether continuous monitoring is a proper practice in Bayesian testing exists among both academic researchers and general practitioners. In this paper, we formally prove the validity of Bayesian testing under proper stopping rules, and illustrate the theoretical results with concrete simulation illustrations. We point out common bad practices where stopping rules are not proper, and discuss how priors can be learned objectively. General guidelines for researchers and practitioners are also provided.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115212799","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}
Thanh-Binh Nguyen, Vu Nguyen, Nguyen Cong Thuong, S. Venkatesh, Mohan J. Kumar, Dinh Q. Phung
Inferring abstract contexts and activities from heterogeneous data is vital to context-aware ubiquitous applications but still remains one of the most challenging problems. Recent advances in Bayesian nonparametric machine learning, in particular the theory of topic models based on Hierarchical Dirichlet Process (HDP), has provided an elegant solution towards these challenges. However, limited existing methods have addressed the problem of inferring latent multifaceted activities and contexts from heterogeneous data sources such as those collected from mobile devices. In this paper, we extend the original HDP to model heterogeneous data using a richer structure of the base measure being a product-space. The proposed model, called product-space HDP (PS-HDP), naturally handles the heterogeneous data from multiple sources and identify the unknown number of latent structures in a principle way. Although this framework is generic, our current work primarily focuses on inferring (latent) threefold activities of who-when-where simultaneously, which corresponds to inducing activities from data collected for identity, location and time. We demonstrate our model on synthetic data as well as on a real-world dataset – the StudentLife dataset. We report results and provide analysis on the discovered activities and patterns to demonstrate the merit of the model. We also quantitatively evaluate the performance of PS-HDP model using standard metrics including F1-score, NMI, RI, purity, and compare them with well-known existing baseline methods.
{"title":"Learning Multifaceted Latent Activities from Heterogeneous Mobile Data","authors":"Thanh-Binh Nguyen, Vu Nguyen, Nguyen Cong Thuong, S. Venkatesh, Mohan J. Kumar, Dinh Q. Phung","doi":"10.1109/DSAA.2016.48","DOIUrl":"https://doi.org/10.1109/DSAA.2016.48","url":null,"abstract":"Inferring abstract contexts and activities from heterogeneous data is vital to context-aware ubiquitous applications but still remains one of the most challenging problems. Recent advances in Bayesian nonparametric machine learning, in particular the theory of topic models based on Hierarchical Dirichlet Process (HDP), has provided an elegant solution towards these challenges. However, limited existing methods have addressed the problem of inferring latent multifaceted activities and contexts from heterogeneous data sources such as those collected from mobile devices. In this paper, we extend the original HDP to model heterogeneous data using a richer structure of the base measure being a product-space. The proposed model, called product-space HDP (PS-HDP), naturally handles the heterogeneous data from multiple sources and identify the unknown number of latent structures in a principle way. Although this framework is generic, our current work primarily focuses on inferring (latent) threefold activities of who-when-where simultaneously, which corresponds to inducing activities from data collected for identity, location and time. We demonstrate our model on synthetic data as well as on a real-world dataset – the StudentLife dataset. We report results and provide analysis on the discovered activities and patterns to demonstrate the merit of the model. We also quantitatively evaluate the performance of PS-HDP model using standard metrics including F1-score, NMI, RI, purity, and compare them with well-known existing baseline methods.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1965 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127482525","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}
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