Download or read book Choice Models for Products with Not Fully Comparable Or Missing Attributes Application of Hierarchical Bayes Modeling for Internet Recommender Engine and Bundles written by Jaihak Chung and published by . This book was released on 2001 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: The dissertation consists of two essays on the development of two different consumer choice models using Hierarchical Bayesian Methodology for internet recommender inference model and multicategory bundle choice.
Download or read book Choice Models for Products with Fully Comparable Or Missing Attributes Application of Hierarchical Bayes Modeling for Internet Recommender Engine and Bundles written by Jaihak Chung and published by . This book was released on 2001 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Final Degree List written by Cornell University. Graduate School and published by . This book was released on 2001-05-27 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Choice Models in Marketing written by Sandeep R. Chandukala and published by Now Publishers Inc. This book was released on 2008 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choice Models in Marketing examines recent developments in the modeling of choice for marketing and reviews a large stream of research currently being developed by both quantitative and qualitative researches in marketing. Choice in marketing differs from other domains in that the choice context is typically very complex, and researchers' desire knowledge of the variables that ultimately lead to demand in marketplace. The marketing choice context is characterized by many choice alternatives. The aim of Choice Models in Marketing is to lay out the foundations of choice models and discuss recent advances. The authors focus on aspects of choice that can be quantitatively modeled and consider models related to a process of constrained utility maximization. By reviewing the basics of choice modeling and pointing to new developments, Choice Models in Marketing provides a platform for future research.
Download or read book American Doctoral Dissertations written by and published by . This book was released on 2000 with total page 816 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book improving online product recommendations by including nonrated items written by yuanping ying, michel wedel, and fred feinberg and published by . This book was released on with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A General Consumer Preference Model for Experience Products written by Chung Jaihak and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a general consumer preference model for experience products that overcomes the limitations of consumer choice models, especially, when it is not easy to consider some qualitative attributes of a product or too many attributes relative to the available amount of preference data, by capturing the effects of unobserved product attributes with the residuals of reference consumers for the same product. For this purpose, we decompose the deterministic component of product utility into two parts: the observed component accounted for by observed attributes and the unobserved component due to non-observed attributes. The unobserved component is estimated by relating it to the corresponding residuals of virtual experts representing homogeneous groups of persons who had experienced the product earlier and evaluated it. Our methodology involves identifying such virtual experts and determining the relative importance to be given to them in the estimation of the target person's residuals. Using Bayesian estimation methods and MCMC simulation inference, we applied our approach to two types of consumer preference data: (1) online consumer ratings (stated preferences) data for Internet recommendation services and (2) offline consumer viewership (revealed preferences) data for movies. We empirically show that our new approach outperforms several alternative collaborative filtering and attribute-based preference models with both in-sample and out of sample fits. Our model is applicable to both Internet recommendation services and consumer choice studies.
Download or read book Recommender Systems written by Charu C. Aggarwal and published by Springer. This book was released on 2016-03-28 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
Download or read book No Customer Left Behind written by Yi Qian and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In marketing applications, it is common that some key covariates in a regression model are subject to missingness. Notably, in the estimation of discrete choice models using scanner data, the prices and promotion values for non-purchased products are often missing. In consumer relationship management (CRM), some important consumer profiles may be missing. The convenient method that excludes the consumers with missingness in any covariate can result in substantial loss in efficiency, and may lead to strong selection bias in the estimation of consumer preference and sensitivity. In this paper, we propose a new Bayesian distribution-free approach to handle the missing covariates problem. This approach allows for flexible modeling of a joint distribution of multi-dimensional covariates that can contain both continuous and discrete variables. At the same time it minimizes the impact of distributional assumptions involved in covariates modeling because the method does not require researchers to specify parametric distributions for covariates and can automatically generate suitable distributions for missing covariates. We develop an MCMC algorithm for inference. The MCMC procedure contains an efficient Hybrid Monte Carlo (HMC) sampler to update parameters in the covariate model. Besides robustness and flexibility, the proposed approach reduces the marketing analysts' modeling and computational efforts associated with missing covariates, and therefore makes the missing covariate problem easier to handle. We illustrate the method in two real data examples where missing covariates occur: a mixed multinomial logit discrete choice model in a ketchup dataset and a hierarchical probit purchase incidence model in a retail store dataset. We also evaluate the performance of the proposed method in repeated samples using extensive simulation studies. These analyses demonstrate that the proposed method possesses several unique features and benefits for handling missing covariate problems, as compared with alternative approaches. The method is useful to correct for the selection bias due to covariate missingness, and can substantially improve the efficiency of analysis. Using the proposed method, researchers can make better managerial decisions with the current available marketing databases. Our method also ensures that no customer is left behind for CRM and individualized marketing. The proposed method is general and can be applied to a wide range of marketing applications. Although our applications focus on consumer-level data, the proposed method can be applied to other marketing applications where other types of marketing players are the units of analysis.
Download or read book Choice Models in Marketing written by Sandeep R. Chandukala and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a review of choice models in marketing from the perspective of a utility maximizing consumer subject to budgetary restrictions. Marketing models of choice have undergone many transformations over the last 20 years, and the advent to hierarchical Bayes models indicate that simple, theoretically grounded models work well when applied to understanding individual choices. Thus, we use economic theory to provide the foundation from which future trends are discussed. We begin our discussion with descriptive models of choice that raises a number of debatable issues for model improvement. We then look to economic theory as a basis for guiding model development, and conclude with a discussion of promising areas for future work.
Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Download or read book Handbook of Choice Modelling written by Stephane Hess and published by Edward Elgar Publishing. This book was released on 2014-08-29 with total page 721 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Choice Modelling, composed of contributions from senior figures in the field, summarizes the essential analytical techniques and discusses the key current research issues. The book opens with Nobel Laureate Daniel McFadden calling for d
Download or read book Modeling Ordered Choices written by William H. Greene and published by Cambridge University Press. This book was released on 2010-04-08 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is increasingly common for analysts to seek out the opinions of individuals and organizations using attitudinal scales such as degree of satisfaction or importance attached to an issue. Examples include levels of obesity, seriousness of a health condition, attitudes towards service levels, opinions on products, voting intentions, and the degree of clarity of contracts. Ordered choice models provide a relevant methodology for capturing the sources of influence that explain the choice made amongst a set of ordered alternatives. The methods have evolved to a level of sophistication that can allow for heterogeneity in the threshold parameters, in the explanatory variables (through random parameters), and in the decomposition of the residual variance. This book brings together contributions in ordered choice modeling from a number of disciplines, synthesizing developments over the last fifty years, and suggests useful extensions to account for the wide range of sources of influence on choice.
Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
Download or read book Multiple Imputation of Missing Data Using SAS written by Patricia Berglund and published by SAS Institute. This book was released on 2014-07-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.
Download or read book Federal Statistics Multiple Data Sources and Privacy Protection written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-01-27 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.
