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PROJECT
Collaborative Filtering Recommendation System
In this project, we'll learn to create a memory-based collaborative filtering recommendation system based on user similarity. We'll use the Streamlit library to create a simple application to view the working system.
You will learn to:
Process data using Python libraries.
Create and manipulate the interaction matrix in Python.
Create a recommendation system.
Create a basic Streamlit application.
Skills
Interactive Real-time Web Applications
Recommendation System
Prerequisites
Basic understanding of Python
Familiarity with recommendation systems
Familiarity with scikit-learn library
Familiarity with Streamlit
Technologies
NumPy
Python
Pandas
Streamlit
Scikit-learn
Project Description
Recommendation systems are widely used in a number of applications to give a personalized user experience. Collaborative filtering uses the information of other users or items in the system to filter out information. A user-based (user-user) collaborative filtering recommendation system is a memory-based approach that utilizes the users’ interactions with the system to find similar users and recommend them the items that similar users have liked.
In this project, we’ll work with an IMDB movie dataset to create a recommendation system for the users using the scikit-learn library, and then we’ll use the Streamlit library to build a simple recommender application.
Project Tasks
1
Getting Started
Task 0: Get Started
Task 1: Import Modules
2
Dataset Preparation
Task 2: Import the Dataset
Task 3: Explore the Dataset
Task 4: Create an Interaction Matrix
Task 5: Explore the Interaction Matrix
3
Creating a Recommendation System
Task 6: Create a Similarity Matrix
Task 7: Provide Recommendations
Task 8: View the Provided Recommendations
4
The Movie Recommender
Task 9: Create a Wrapper Function
Task 10: Create an Application
Task 11: Display the Recommendation Details
Congratulations
Relevant Courses
Use the following content to review prerequisites or explore specific concepts in detail.