LikeIt (Our Graduate Final Project)
focus on Machine Learning - Matrix Factorization and a Hybrid Recommendation Method (CB Filtering)
Repositories
are private
Technology
Full Stack Application:
Java (Retrofit2, Object Oriented Programming, Design Patterns, YouTube API, Facebook API etc.)
JavaScript (NodeJS, Express, MongoDB, Mongoose, mLab etc.)
Back End:
Python (Flask, IBM Watson, Pymongo, NumPay, Jupyter Notebook etc.)
Developed using with IPython
API List: ESPN, Newsapi, Fox Sports, SPORTbible
IDE:
Android Studio
WebStorm
PyCharm
Environments:
Windows and Linux (Remote Ubuntu Server with external GPU)
About Development
We run weekly sprints (Scrum Method) under our Supervisor Dr. Eliav Menachi and we used with Slack to communicate
LikeIt Application
https://play.google.com/store/apps/details?id=com.gal.galbenevgi.likeitapplicationfrontend
Videos & Presentations
https://youtu.be/jb1g7rWkSZQ
https://www.youtube.com/watch?v=iMq_VM7bmjA
http://snack.to/bhjs6pkx?UA_PHPSESSID=scf2ncvlplu9g1s8qv5353rau5
Developers
Bar Shmerling
Gal Ben-Evgi
Gal Malca
Moran Hazom