Movie Recommender System

A Recommender System that suggests top 5 most similar movies to the one selected. This is a content based movie recommender system & a streamlit web application.

Content Based Recommender Systems

• These use characteristic information and take item attributes into consideration
• These recommender systems hypothesize that if a user was interested in an item in the past, they will be interested in something similar in the future
• Items similar to the watched/selected item are selected
• Each item is represented in the form of a vector
• Recommendation are based on cosine similarity values between these vectors
• Problem arises due to making obvious recommendations based on excessive specialization. Exploration of other variety is missing.

Download Dataset

• Dataset link

How to run?

STEP 01: Clone the repo and create a conda environment

git clone https://github.com/mon28/Movie-Recommender-System.git
cd Movie-Recommender-System
conda create -n movie-rec python=3.7.10 -y
conda activate movie-rec

STEP 02: Install requirements

pip install -r requirements.txt

STEP 03: Run the Streamlit app

streamlit run app.py

Note: Before clicking on "Show Recommendations" for the first time, click on "Train Recommender System" for generating model