Publications by Vijaya Cherukuri
Data608_Module-5
Part 1A Enter the text for reverse: Reverse Part 1B Enter the number: Compute Part 2A Part 2B Enter the name: Get Weight and Height ...
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Data608_Hw1
Principles of Data Visualization and Introduction to ggplot2 I have provided you with data about the 5,000 fastest growing companies in the US, as compiled by Inc. magazine. lets read this in: inc <- read.csv("https://raw.githubusercontent.com/charleyferrari/CUNY_DATA_608/master/module1/Data/inc5000_data.csv", header= TRUE) And lets preview this...
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Data606_FinalProject
This text is displayed verbatim / preformatted Defining libraries: library(tidyr) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(psych) library(s...
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Data605_FinalProject
Problem 1 Using R, generate a random variable X that has 10,000 random uniform numbers from 1 to N, where N can be any number of your choosing greater than or equal to 6. Then generate a random variable Y that has 10,000 random normal numbers with a mean of μ=σ=(N+1)/2. set.seed(10000) N <- 25 X <- round(runif(10000, 1, N)) Y <- round(rnorm...
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Data612_Research_Discussion-1
Assignment Now that we have covered basic techniques for recommender systems, choose one commercial recommender and describe how you think it works (content-based, collaborative filtering, etc). Does the technique deliver a good experience or are the recommendations off-target? You may also choose one of the three non-personalized recommenders (b...
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Data612_Project3
Objective • Your task is implement a matrix factorization method—such as singular value decomposition (SVD) or Alternating Least Squares (ALS)—in the context of a recommender system. • You may approach this assignment in a number of ways. You are welcome to start with an existing recommender system written by yourself or someone else. Rem...
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Data612-Research Discussion 2
Reseacrh Discussion 2 For this discussion item, please watch the following talk and summarize what you found to be the most important or interesting points. The first half will cover some of the mathematical techniques covered in this unit’s reading and the second half some of the data management challenges in an industrial-scale recommendatio...
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Data612-Project4
Objective The goal of this assignment is give you practice working with accuracy and other recommender system metrics. Deliverables As in your previous assignments, compare the accuracy of at least two recommender system algorithms against your offline data. Implement support for at least one business or user experience goal such as increased se...
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Data612-Research Discussion 4
Mitigating the Harm of Recommender Systems Objective Read one or more of the articles below and consider how to counter the radicalizing effects of recommender systems or ways to prevent algorithmic discrimination. Up Next: A Better Recommendation System YouTube, the Great Radicalizer Social Influence Bias in Recommender Systems: A Methodology f...
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Data612-Research Discussion 3
In what ways do you think Recommender Systems reinforce human bias? Today, recommendation engines are perhaps the biggest threat to societal cohesion on the internet and, as a result, one of the biggest threats to societal cohesion in the offline world, too. The recommendation engines we engage with are broken in ways that have grave consequence...
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