This document discusses building recommender systems using Azure ML. It provides an overview of recommender systems including examples of how Netflix, Amazon, and Best Buy use them. It then outlines an agenda to understand recommender systems, load a movie dataset from GroupLens, clean and prepare the data, train a Matchbox recommender model in Azure ML, test the model, and ways to improve predictive modeling. The presentation aims to provide hands-on experience with recommender systems in Azure ML Studio.

1. Building A Recommender
System- Azure ML
Microsoft Connect- 2018
-Dev Raj Gautam-

2. Stories
• Up to 75% of what consumers watch on Netflix comes from the
company’s recommender system – McKinsey
• Amazon credits recommender systems with 35% of their revenue
• Best Buy decided to focus on their online sales, and in 2016’s second
quarter they reported a 23.7% increase
• Saves Billions of USD in Advertisement

3. Agenda
• Understand Recommender Systems
• Hands on with The Following in Azure ML Studio

4. What are Recommender System?
• that seeks to predict the "rating" or "preference" a user would give
to an item- Wiki
• Inputs a large pool of available data & make decision-making process
easier by providing a few targeted selections

5. Types
• Collaborative Filtering
• Content Based Filtering
• Demographic Based Filtering
• Utility Based Filtering
• Knowledge Based Filtering
• Hybrid Filtering

6. Dataset
• https://grouplens.org/datasets/movielens/
• Small: 100,000 ratings and 3,600 tag applications applied to 9,000
movies by 600 users. Last updated 9/2018.
• Upload the Dataset

7. Clean, Prepare & Manipulate Data
• Select Column in Datasets
• Edit Metadata

8. Train Model
• Split Data via Recommender Split
• training set—a subset to train a model.
• test set—a subset to test the trained model.
• Train The Model
• Matchbox recommender : Combines collaborative filtering with a content-
based approach. It is therefore considered a hybrid recommender.

9. Test Data
• Score Your Algorithm
• Score Matchbox Recommender (Rating Prediction)
• Evaluate
• Evaluate Recommender
• RMSE is the square root of the variance, known as the standard error
• Negatively-oriented scores, which means lower values are better

10. Improve your Predictive Modelling!
• Resampling
• Baseline Performance
• Spots Check
• Making Data Right
• Get More Quality Data
• Generate More Data
• Data Cleaning
• Reframing Problem
• Transform Your Data :Gaussian
• Select Your Features Right
• Engineer Your Features

11. Thank You- Dev Raj Gautam
I love writing reusable components, solving technical problems for team
& Designing Architecture of solutions along with managing projects in
scrum.
https://f5blogs.wordpress.com/
https://medium.com/@f5blogs
https://www.linkedin.com/in/dev-raj-gautam/
devraj.np@gmail.com