Publications by Ashish Kumar
data-612-Project2
data-612-Project2 Ashish Kumar 06/13/2020 Project 2 MovieLense Recommendation System in R Collaborative filtering is a technique used by recommender systems for predicting the interests of one user based on the preference information of other users. This project is an implementation of a Movie Recommender System that uses the following techniqu...
6284 sym R (14670 sym/74 pcs) 15 img
data-612-Final-Project
data-612-Final-Project Ashish Kumar 07/13/2020 Final Project Background Case Steam is the world’s most popular PC Gaming hub, with over 6,000 games and a community of millions of gamers. With a massive collection that includes everything from AAA blockbusters to small indie titles, great discovery tools are a highly valuable asset for Steam. ...
5577 sym R (17166 sym/63 pcs) 9 img
data-612-Discussion4
data-612-Discussion4 Ashish Kumar 07/02/2020 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 N...
2547 sym
data-612-Discussion3
data-612-Discussion3 Ashish Kumar 06/29/2020 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 system...
2187 sym
data-612-Project1
data-612-Project1 Ashish Kumar 06/03/2020 Project 1 Briefly describe the recommender system that you’re going to build out from a business perspective, e.g. “This system recommends data science books to readers.” This system recommends movies to Users Find a dataset, or build out your own toy dataset. As a minimum requirement for complex...
2238 sym R (11461 sym/24 pcs)
data-612-Discussion2
data-612-Discussion2 Ashish Kumar 06/18/2020 Certain systems are going to work efficiently with small datasets But, for companies like Spotify where their catalog consists of 40 million songs a recommender system that is scalable is crucial. Christopher Johnson explains problems Spotify faced on when developing their recommender system and how c...
1430 sym
data-612-Project3
data-612-Project3 Ashish Kumar 06/24/2020 Project 3 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 writ...
2421 sym R (14631 sym/68 pcs) 3 img
data-612-Project4
data-612-Project4 Ashish Kumar 07/01/2020 Project 4 1. As in your previous assignments, compare the accuracy of at least two recommender system algorithms against your offline data. 2. Implement support for at least one business or user experience goal such as increased serendipity, novelty, or diversity. 3. Compare and report on any change i...
3381 sym R (9164 sym/58 pcs) 10 img
data-612-Final-Project-Proposal
data-612-Final-Project-Proposal Ashish Kumar 07/03/2020 Project-Proposal The goal for your final project is for you to build out a recommender system using a large dataset (ex: 1M+ ratings or 10k+ users, 10k+ items. There are three deliverables, with separate dates: [1] Planning Document Find an interesting dataset and describe the system you p...
2091 sym R (903 sym/6 pcs)
data-608-Module1
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...
1690 sym R (7777 sym/33 pcs) 5 img