Build a Collaborative Filtering Recommender System in Python

Build a Collaborative Filtering Recommender System in Python

Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.
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What will you learn

What are Recommender systems
Distance based metric
Cosine similarity
Centered cosine similarity
Loading and exploring data
Exploratory Data Analysis
Visualizations
Encoding the data
Normalising ratings
Creating user-item matrix
Identifying similar users
Recommending products to a user

Project Description

Recommender systems are an integral part of many online systems. From e-commerce to online streaming platforms. Recommender systems employ the past purchase pattern on its user to predict which other products they may be interested in and likely to purchase. Recommending the right products gives a significant advantage to the business. A major portion of the revenue is generated through recommendations.

The Collaborative Filtering algorithm is very popular in online streaming platforms and e-commerce sites where the customer interacts with each product (which can be a movie/ song or consumer products) by either liking/ disliking or giving a rating of sorts. One of the requirements to be able to apply collaborative filtering is that sufficient number of products need ratings associated with not them. User interaction is required.

This machine learning project walks you through the implementation of collaborative filtering using memory based technique of distance proximity using cosine distances and nearest neighbours.

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Curriculum For This Mini Project

What are Recommender Systems
05m
Distance Metric - Cosine Similarity
12m
Centered Cosine Similarity
08m
Loading and Exploring the Data
07m
Visualizations - Exploratory Data Analysis
11m
Visualizations - Understanding Ratings per Product
06m
Encoding Data - Normalized Ratings
10m
Filtering Process
05m
Creating User Item Matrix
04m
Identify Top k Similar Users
07m
Recommend Top k products
07m
Evaluate the Recommender Model
10m

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