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Byte-Sized-Chunks: Recommendation Systems

Build a movie recommendation system in Python - master both theory and practice
Instructor:
Loony Corn
2,968 students enrolled
English [Auto-generated]
Identify use-cases for recommendation systems
Design and Implement recommendation systems in Python
Understand the theory underlying this important technique in machine learning

Note: This course is a subset of our 20+ hour course ‘From 0 to 1: Machine Learning & Natural Language Processing’ so please don’t sign up for both:-)

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.

Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

  • Recommendation Engines perform a variety of tasks – but the most important one is to find products that are most relevant to the user.
  • Content based filtering finds products relevant to a user – based on the content of the product (attributes, description, words etc).
  • Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to Recommendations
  • Neighborhood models – also known as Memory based approaches – rely on finding users similar to the active user. Similarity can be measured in many ways – Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.
  • Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization.
  • Recommendation Systems in Python!
  • Movielens is a famous dataset with movie ratings.
  • Use Pandas to read and play around with the data.
  • Also learn how to use Scipy and Numpy

Would You Recommend To A Friend?

1
You, This Course, and Us!
2
What do Amazon and Netflix have in common?
Recommendations - good quality, personalized recommendations - are the holy grail for many online stores. What is the driving force behind this quest?
3
Recommendation Engines - A look inside
Recommendation Engines perform a variety of tasks - but the most important one is to find products that are most relevant to the user. Content based filtering, collaborative filtering and Association rules are common approaches to do so.
4
What are you made of? - Content-Based Filtering

Content based filtering finds products relevant to a user - based on the content of the product (attributes, description, words etc).

5
With a little help from friends - Collaborative Filtering
Collaborative Filtering is a general term for an idea that users can help each other find what products they like. Today this is by far the most popular approach to Recommendations
6
A Neighbourhood Model for Collaborative Filtering

Neighbourhood models - also known as Memory based approaches - rely on finding users similar to the active user. Similarity can be measured in many ways - Euclidean Distance, Pearson Correlation and Cosine similarity being a few popular ones.

7
Top Picks for You! - Recommendations with Neighbourhood Models
We continue with Neighbourhood models and see how to predict the rating of a user for a new product. Use this to find the top picks for a user.
8
Discover the Underlying Truth - Latent Factor Collaborative Filtering

Latent factor methods identify hidden factors that influence users from user history. Matrix Factorization is used to find these factors. This method was first used and then popularized for recommendations by the Netflix Prize winners. Many modern recommendation systems including Netflix, use some form of matrix factorization.

9
Latent Factor Collaborative Filtering contd.

Matrix Factorization for Recommendations can be expressed as an optimization problem. Stochastic Gradient Descent or Alternating least squares can then be used to solve that problem.

10
Gray Sheep and Shillings - Challenges with Collaborative Filtering
Gray Sheep, Synonymy, Data Sparsity, Shilling Attacks etc are a few challenges that people face with Collaborative Filtering.
11
The Apriori Algorithm for Association Rules

Association rules help you find recommendations for products that might complement the user's choices. The seminal paper on association rules introduced an efficient technique for finding these rules - The Apriori Algorithm

Recommendation Systems in Python

1
Installing Python - Anaconda and Pip

Anaconda's iPython is a Python IDE. The best part about it is the ease with which one can install packages in iPython - 1 line is virtually always enough. Just say '!pip'

2
Back to Basics : Numpy in Python
Numpy arrays are pretty cool for performing mathematical computations on your data.
3
Back to Basics : Numpy and Scipy in Python

We continue with a basic tutorial on Numpy and Scipy

4
Movielens and Pandas

Movielens is a famous dataset with movie ratings. Use Pandas to read and play around with the data.

5
Code Along - What's my favorite movie? - Data Analysis with Pandas
We continue playing with Movielens data - lets find the top n rated movies for a user.
6
Code Along - Movie Recommendation with Nearest Neighbour CF

Let's find some recommendations now. We'll use neighbour based collaborative filtering to find the users most similar to a user and then predict their rating for a movie

7
Code Along - Top Movie Picks (Nearest Neighbour CF)
We've predicted the user's rating for all movies. Let's pick the top recommendations for a user.
8
Code Along - Movie Recommendations with Matrix Factorization
Matrix Factorization was first used for recommendations during the Netflix challenge. Let's implement this on the Movielens data and find some recommendations!
9
Code Along - Association Rules with the Apriori Algorithm
The Apriori algorithm was introduced in a seminal paper that described how to mine large datasets for association rules efficiently. Let's work through the algorithm in Python.
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