Skip to content

Latest commit

 

History

History
13 lines (10 loc) · 518 Bytes

README.md

File metadata and controls

13 lines (10 loc) · 518 Bytes

RecommenderSys2018_Competition

This repo was used for the Kaggle Competition of Politecnico di Milano students on a song recommender system. It contains a various number of recommender:

  • Item and User based collaborative filter algorithms
  • Content based algorithms
  • SLIM and ElasticNet models
  • Pure SVD and other Matrix Facotrizaion techniques
  • Hybrid models

Some other techniques such as XGBoost and Neural Netowrk models were tested and can be found in the project.

Federico Betti
Raffaele Bongo