A series of Ipython notebooks and python code to show prototypes of recommenders.
This repo is a prototype of different recommendation algorithms.
Movies Collaborative Filtering
Movies SVD Matrix Factorization
Movies non-personalized recommendation
An overview over recommenders with code
It contains also examples of the recommendation we've built for migros.
It contains an Import of google transaction data for recommendations.
It contains sample data:
online retail example
Link to DEMO or 30s VIDEO of the project
Link to POC demo or video: none exists
Link to the blogpost if it exists:
Is it protected with a psw/login: no
What is the benefit for the customer: A user gets recommended items.
What is the benefit for the business: A business owner can sell more items or show more relevant items.
How much MD did you use to build it: 10MD
Which data is it based on: Different datasets.
Which technologies were used: scikit learn, SVD, pandas
Which things could be build with this POC if you had more time: Integrate them in production systems, like we did for produkte.migros.ch/angebote