Creating recommendation engines based on genre, similar users and a hybrid of the two and then deployin them on a Netflix clone WebApp
In today’s technology driven world, recommender systems are socially and economically critical for ensuring that individuals can make appropriate choices surrounding the content they engage with on a daily basis. One application where this is especially true surrounds movie content recommendations; where intelligent algorithms can help viewers find great titles from tens of thousands of options.
Providing an accurate and robust solution to this challenge has immense economic potential, with users of the system being exposed to content they would like to view or purchase - generating revenue and platform affinity.
These are the packages used:
comet-ml==3.31.3
numpy
random
matplotlib.pyplot
pandas==1.3.5
pickle
plotly
scikit-learn
scikit-surprise==1.1.1
string
spacy==3.2.4Try the latest version on packages without versions
Perhaps a package you may be unfamiliar with above is Surprise. Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
Surprise was designed with the following purposes in mind:
- Give users perfect control over their experiments. To this end, a strong emphasis is laid on documentation, which we have tried to make as clear and precise as possible by pointing out every detail of the algorithms.
- Alleviate the pain of Dataset handling. Users can use both built-in datasets (Movielens, Jester), and their own custom datasets.
$ pip install numpy
$ pip install scikit-surprise
Install comet-ml (in my case comet-ml==3.31.3)There isn't much I'd like to change about this project. I would, however, like to learn some more about the Streamlit WebApp.
- Todo 1 - Deploy the recommender systems using an alternative to Streamlit Webapp, like Flask or Bokeh.
- Todo 2 - Begin analysis
Contributions and suggestions are always welcome!
You can contact me using my details below to get int touch.
Mlondi Shoba - Twitter: @ushoba_ Email: - msmshoba@gmail.com
Project Link: https://github.com/Mo-Shoba/King-MisuZulu-Coronation