Browsing by Author "Zhou, Mingyuan (Assistant professor)"
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Item Clicks prediction with L1 regularized logistic regression and a study on poisson factorization recommender evaluation(2014-08) Zhan, Wenjing; Zhou, Mingyuan (Assistant professor)The key task for a search engine advertising system is, for each query that the search engine receives, to choose what advertisement should be displayed, and in what order. This ranking order has a strong impact on the revenue the search engine receives from the ads. Meanwhile, showing the user an advertisement that they prefer to click on improves user satisfaction. Therefore, it is reasonable to set up click-through rate (CTR) as a weighted ranking criteria. In this project, we aim to develop a model capable of accurately predicting CTR of ads in the system. For ads that have been displayed repeatedly, this is empirically measurable, but for new ads, other means must be used. Combining logistic regression model with L1 regularization we are able to predict CTR for new ads based on self-defined features. The ultimate goal is to improve the convergence and performance of our advertising system, consequently increasing both revenue and user satisfaction. Second part of this report is about a study on poisson factorization recommender evaluation. As recommender systems become more and more popular, many approaches have been suggested to evaluate the performance of traditional recommenders based on Gaussian distribution. However, few evaluating approaches were designed for poisson factorization recommender. This study checked some most common evaluation methods and discussed about their appropriateness in poisson factorization recommender evaluation.Item Implementing the multimodel generalized beta estimator in stata and its application(2016-05) Duan, Yutong; Von Hippel, Paul T.; Zhou, Mingyuan (Assistant professor)The multimodel generalized beta estimator (MGBE) described by von Hippel, Scarpino and Hola (2014) provides researchers with an improved way to estimate inequality from binned incomes. To extend the application of MGBE, the mgbe command is developed in Stata. In this report, the implementation and performance of mgbe are discussed.Item Predicting success of bank telemarketing with classification trees and logistic regression(2016-05) Yang, Chuanfeng; Zhou, Mingyuan (Assistant professor); Gawande, KishoreSuccess of bank marketing campaign is predicted with customer features, campaign information and economic attributes. To predict whether or not clients will subscribe long-term deposit, logistic regression is applied with backward variable selection and principal components analysis. Random forests and stochastic gradient boosting, as members of classification trees, are also built as comparisons. Based on visualization and quantitative predictive performance, gradient boosting (AUC = 0.791) is slightly better than the other two models. Variable importance from 3 models remains consistent for most variables. Social and economic attributes, such as euribor3m, are among top important variables.