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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.

Collaborative Filtering Recommendation 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.


Interactive Real-time Web Applications

Recommendation System


Basic understanding of Python

Familiarity with recommendation systems

Familiarity with scikit-learn library

Familiarity with Streamlit







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


Getting Started

Task 0: Get Started

Task 1: Import Modules


Dataset Preparation

Task 2: Import the Dataset

Task 3: Explore the Dataset

Task 4: Create an Interaction Matrix

Task 5: Explore the Interaction Matrix


Creating a Recommendation System

Task 6: Create a Similarity Matrix

Task 7: Provide Recommendations

Task 8: View the Provided Recommendations


The Movie Recommender

Task 9: Create a Wrapper Function

Task 10: Create an Application

Task 11: Display the Recommendation Details