Publications by sufian
DAT 612: Week 1 - Discussion of Attacks on Recommender Systems
General Discussion on preventing attacks on CF recommender systems Background There are 3 main motivations for attacks: Monetary value of recommendation list by pushing items on recommendation list, decreasing ranking of items or simply sabotage Manipulation of “Internet Opinion”; malevolent users trying to influence an election for instanc...
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DAT 612 - Project 2
In Project 2, we implmented 2 different types of Collaborative Filtering systems: User Based Collaborative Filtering Systems (UBCF) Item Based Collaborative Filtering Systems (IBCF) A comparative analysis was then performed on the two different approaches using these different algorithms Note: We shall follow the spirit & methodologies as presc...
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DAT 612 - Discussion 4
"Mitigating the Hidden Side Effects of Recommendation Systems" This disucssion is loosley based on an article that appeared on MIT Sloan's Mgmt Review by Gediminas Adomavicious, Jesse Boackstedt, Shawn P. Curly, Jingjing Zhang and Sam Ransbotham, Nov 13, 2018. Reference: (1) https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-peo...
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DAT 612 - Final Project Proposal
Project Scope and Description The Final project will rely more on python packages and libraries instead of R’s much-vaunted Recommenderlabs mainly to learn more about other packages besides R’s Recommenderlabs package. To that extent, this is an opportunity to venture out of the book’s procedural-driven guidance and discover for ourselves ...
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DAT 612 - Project 4
Loading Libraries library(Matrix) library(reshape2) library(data.table) library(tidyr) library(dplyr) library(kableExtra) library("scales") library("recommenderlab") library(tidytext) library(psych) library(knitr) library(ggplot2) require(ggthemes) Loading Data from built-in Database of Recommenderlab Using the Jester5K database thi...
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ISYE 6501 Intro to Modeling HW3
Problem 5.1 library(scales) library(tidyr) library(dplyr) library(knitr) require(ggthemes) library(ggplot2) library(tidyverse) library(gridExtra) library("scatterplot3d") library(outliers) library(reshape2) library(lubridate) library(plotly) library(qcc) Sample view of Crimes Data Taking only the crime column Quick Statistical Su...
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DAT 612 - Discussion 3
"Understanding Biases in Search & Recommender Systems" This article appeared on Search Engine Journal (SEJ) by Greg Jarboe/ Dec 2019. Reference: (1) https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf (2) https://cacm.acm.org/magazines/2018/6/228035-bias-on-the-web/fulltext (3) htt...
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DAT 612 - Project 5
Loading Libraries library(Matrix) library(reshape2) library(data.table) library(tidyr) library(dplyr) library(kableExtra) library("scales") library("recommenderlab") library(tidytext) library(psych) library(knitr) library(ggplot2) require(ggthemes) library(tictoc) library(stringr) suppressWarnings(suppressMessages(library(recommen...
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ISYE 6501 Intro to Modeling HW1
Prob 2.1 Describe a situation or problem from your job, everyday life, current events, etc., for which a classification model would be appropriate. List some (up to 5) predictors that you might use. Answer: I work for a utility company and we have situations in the field whereby we send drones to monitor field assets such as poles, power generati...
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ISYE 6501 Intro to Modeling HW2
Summary of Cross-Validations I chose the Train.KKNN method to first fine-tuned the optimal K-value (48) and to determine the best kernel ("optimal") based on Training data. The motivation was to expose the model to as much training as possible, after obtaining the "best" sets of hyper-parameters from cross-validation, before rolling out the fina...
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