Flavorite

Favorite is simple (but still powerfull!) recommendation engine based on cosine similarity between items. It requies only sequence of tuples like (user_id, item_id, rating) and allows to find top N items closest to the given one (people-who-liked-that-also-liked-this kind of algorithm). This repository is packed with parser for Combosaurus data and can be directly used to find most similar shows (see below).

Installation

Install binary using pip:

pip install flavorite

or build it from source:

git clone https://github.com/faithlessfriend/flavorite
python setup.py install

Usage

Assuming that data is iterator of tuples like (user_id, item_id, rating) and item_id is some id of the item (normally string or number):

import flavorite as flv
recom = flv.Recommender()
recom.build(data)
# or just load existing model:
# recom.load(model_file)
recom.find_closest(item_id, 10)

Example Using Combosaurus Data

TODO: Upload recommender model somewhere TODO: Merge Usage and Example sections

The easiest way to try out recommender with Combosaurus data is to download precomputed recommender model and just load it into Recommender instanse:

import flv
recom = flv.Recommender()
recom.load(model_file)

Combosaurus data

...to be continued...