Publications by Habib Khan
Data 612 - Research Discussion 4
Instruction Mitigating the Harm of Recommender Systems 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. Renee Diresta, Wired.com (2018): Up Next: A Better Recommendation System Zeynep Tufekci, The New York Times (2018): YouTube, the Gr...
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Data 612 - Research Discussion 3
Instruction As more systems and sectors are driven by predictive analytics, there is increasing awareness of the possibility and pitfalls of algorithmic discrimination. In what ways do you think Recommender Systems reinforce human bias? Reflecting on the techniques we have covered, do you think recommender systems reinforce or help to prevent une...
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Data 612 - Research Discussion 2
Instruction 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 recommendation system. V...
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Data 612 - Final Project
# loading libraries library(tidyverse) library(tidyr) library(readr) library(data.table) library(dplyr) library(recommenderlab) library(knitr) library(kableExtra) library(ggplot2) Presentation https://www.youtube.com/watch?v=va2P-ZvXh3U&feature=youtu.be Introduction In this project, we are going to use collaborative filtering in which ...
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Data 612 - Research Discussion 1
Part 1 - Commercial Recommender Systems 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? Response As per my previous work experi...
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Data 612 - Final Project Proposal
Objective Find an interesting dataset and describe the system you plan to build out. If you would like to use one of the datasets you have already worked with, you should add a unique element or incorporate additional data. (i.e. explicit features you scrape from another source, like image analysis on movie posters). The overall goal, however, w...
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Data 624 - HW1
library(fpp) library(fpp2) library(ggplot2) library(kableExtra) Question 2.1 Use the help function to explore what the series gold, woolyrnq and gas represent. Use autoplot() to plot each of these in seperate plots. What is the frequency of each series? Hint: apply the frequency() function. Use which.max() to spot the outlier in the gold ser...
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Data 621 - HW 1
# loading libraries library(tidyr) library(tidyverse) library(knitr) library(kableExtra) library(DT) library(reshape2) library(naniar) # for missing values library(corrplot) # for plotting correlation matrix library(imputeTS) library(e1071) # To check skewness library(caret) library(jtools) library(VIM) # Prints out the summary table...
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Data 624 - HW2
# loading libraries library(fpp2) ## Loading required package: ggplot2 ## Loading required package: forecast ## Registered S3 method overwritten by 'quantmod': ## method from ## as.zoo.data.frame zoo ## Loading required package: fma ## Loading required package: expsmooth #library(ggplot2) library(gridExtra) Exercise 3.1 For us...
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Data 624 - HW3
library(fpp2) library(scales) library(ggplot2) library(seasonal) # for X11 library(gridExtra) Problem 6.2 The plastics dataset consists of the monthly sales (in thousands) of product A for a plastics manufacturer for five years. (a) Plot the time series of sales of product A. Can you identify seasonal fluctuations and/or a trend cycle? # Ba...
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