Project: Movie Recommender System Using Machine Learning!

Recommendation systems are becoming increasingly important in today’s extremely busy world. People are always short on time with the myriad tasks they need to accomplish in the limited 24 hours. Therefore, the recommendation systems are important as they help them make the right choices, without having to expend their cognitive resources.

The purpose of a recommendation system basically is to search for content that would be interesting to an individual. Moreover, it involves a number of factors to create personalised lists of useful and interesting content specific to each user/individual. Recommendation systems are Artificial Intelligence based algorithms that skim through all possible options and create a customized list of items that are interesting and relevant to an individual. These results are based on their profile, search/browsing history, what other people with similar traits/demographics are watching, and how likely are you to watch those movies. This is achieved through predictive modeling and heuristics with the data available.

Types of Recoomendation system

Content based
Collabrative Filtering Based
Hybrid based

Content Based -

Recommend based on similiarty of content. Example Iron Man and Iron Man 2 are similar.

Collabrative Filtering Based -

lets say customer 1 and customer 2 have similar kind of interest becuase for most of the movies they have given simmilar ratings. So for any movie/ movie criteria on which customer 1 has given good rating then its high chance that customer 2 also will give good rating for the same movie.

Hybrid Based -

Both content and collabrative filtering used in hybrid based recommendation

About this project:

This is a web application that recommends similar kinds movies to user based on its search input. It is using streamlit to provide UI and precalculated distnaces between vectors for recommendations. To calculate distance between two vectors we have used cosine similarity.
Note - Number of similar movies to be displayed is configurable due to parameters of "configs/config.yaml" file.

Also, have a look at deployed project,

Click here to run it live on server

Demo:

Dataset has been used:

Dataset link

How to run?

STEPS:

Clone the repository in local directory

git clone https://github.com/atulgaikwad12/Movie-Recommender-System-ML-ContentBased.git

STEP 01- Create a conda environment after opening the repository

conda create -n envmovie python=3.7.10 -y

conda activate envmovie

STEP 02- install the requirements

pip install -r requirements.txt

STEP 03- Open jupyter notebook run with current env

jupyter-notebook

STEP 04- Can Download data set from below location and unzip in data folder

https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata/download

STEP 05- Run below jupyter notebook to do data preprocessing, generate artifacts of distances, movies that can be used further to get end result.

Note - Find generated pickel files in artifcats folder

Movie Recommender System Data Analysis.ipynb

STEP 06- To run application use below command

streamlit run app.py

STEP 07- Create repository on github and execute below commands to push code

git init
git add .
git commit -m "first commit"
git branch -M main
git remote add <origin your_new_github_repo_url>
git push -u origin main

STEP 08- To push large files use below commands

Download and install the Git lfs command line extension.

git lfs install

select the file types you'd like Git LFS to manage

git lfs track "*.pkl"
git lfs track "*.png"
git lfs track "*.jpeg"

To make sure .gitattributes is tracked

git add .gitattributes

Add large files or simply all in staging area

git add .

Finally commit and push (may have to solve some conflict on push)

git commit -m "Add design file"
git push origin main

STEP 09- To deploy application on streamlit share

1. Signup in streamlit share using github account OAuth and Create New app

2. Select Github repository and branch

3. Add scerate tmdb_key in TOML format

tmdb_key = "API_KEY_STRING"

atulgaikwad12 / Movie-Recommender-System-ML-ContentBased