Publications by Zhi Ying Chen (Sec#1), Mengqin Cai (Sec#3), Fan Xu (Sec#4), Sin Ying Wong (Sec#4)
DATA_612_Discussion_4
Code Show All Hide All Data_612_Discussion_4 Question Answer Reference Fan Xu 7/15/2020 Question 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 Ne...
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DATA_612_Discussion_3
Code Show All Hide All Data_612_Discussion_3 Question Answer Reference Fan Xu 7/15/2020 Question 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 ...
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DATA_612_Discussion_1
Code Show All Hide All Data_612_Discussion_1 Questions Part I Part II Answers Part I Reference Part II Reference Fan Xu 7/14/2020 Questions Part I 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 th...
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DATA_612_Project_5_Implementing a Recommender System on Spark
Code Show All Hide All DATA 612 Project 5 - Implementing a Recommender System on Spark Instruction Introduction Load Packages Import Data Read Data Data Exploration Build Model in RecommenderLab Sampling Train-Test Splitting with Cross Validation Train Model Make Prediction RMSE Compute Run Time Build Model in Spark Set Spark Configuration ...
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DATA_612_Final Project Proposal
Code Show All Hide All DATA 612 Final Project Proposal Project Goal Dataset Introduction Design Steps Reference Sin Ying Wong, Zhi Ying Chen, Fan Xu 7/02/2020 Project Goal In this final project, we will work in a small group to build out a recommender system using a large dataset containing 1M+ ratings, or 10k+ users with 10k+ items. Usin...
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DATA_612_Project_4_Accuracy and Beyond
Code Show All Hide All DATA 612 Project 4 - Accuracy and Beyond Instruction Introduction Load Packages Read Data Data Exploration Data Preparation Building Recommendation Models UBCF Models IBCF Models SVD Models Random Models Increasing Serendipity UBCF vs HYBRID ICBF vs HYBRID SVD vs HYBRID Conclusion Sin Ying Wong, Zhi Ying Chen, Fan X...
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DATA_612_Discussion_2
Code Show All Hide All Data_612_Discussion_2 Question Answer Fan Xu 7/14/2020 Question 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 som...
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DATA605_Assignment 2
Code Show All Hide All DATA605 ASSIGNMENT 2 1 Problem set 1 1.1 Question 1 1.2 Question 2 2 Problem set 2 Fan Xu 9/5/2020 library(tidyverse) 1 Problem set 1 Please typeset your response using LaTeX mode in RStudio. If you do it in paper, please either scan or take a picture of the work and submit it. Please ensure that your image is leg...
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DATA605_Assignment 1
Code Show All Hide All DATA605 ASSIGNMENT 1 1 Problem set 1 1.1 Question 1 1.2 Question 2 1.3 Question 3 1.4 Question 4 2 Problem set 2 Fan Xu 8/30/2020 library(tidyverse) 1 Problem set 1 You can think of vectors representing many dimensions of related information. For instance, Netflix might store all the ratings a user gives to movies...
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DATA605_Assignment 6
Code Show All Hide All DATA605 ASSIGNMENT 6 1 Problem Set 1 1.1 Answer 2 Problem Set 2 2.1 Answer 3 Problem Set 3 3.1 Answer 4 Problem Set 4 4.1 Answer 5 Problem Set 5 5.1 Answer 6 Problem Set 6 6.1 Answer 7 Problem Set 7 7.1 Answer 8 Problem Set 8 8.1 Answer 9 Problem Set 9 9.1 Answer 10 Problem Set 10 10.1 Answer 11 Problem Set 11...
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