Recommender System using Python

Recommender systems are everywhere. Amazon recommends buys we like, google recommends searches, Youtube recommends systems and Facebook recommends people.

All of these implementations describe Recommender Systems.

There are 2 types of Recommender Systems.

Collaborative Systems - Systems that recommend information based on what you like and what the others like.

Content Based Systems- Based on what you have viewed

This is a simple ML based recommender System in python. It uses the LightFM library as the dataset import numpy as np from lightfm import LightFM from fetch_lastfm import fetch_lastfm data = fetch_lastfm() model = LightFM(loss='warp') model.fit(data['matrix'], epochs=30, num_threads=2) # Get recommendationns function def get_recommendations(model, coo_mtrx, users_ids): n_items = coo_mtrx.shape[1] for user in users_ids: # TODO create known positives # Artists the model predicts they will like scores = model.predict(user, np.arange(n_items)) top_scores = np.argsort(-scores)[:3] print 'Recomendations for user %s:' % user for x in top_scores.tolist(): for artist, values in data['artists'].iteritems(): if int(x) == values['id']: print ' - %s' % values['name'] print '\n' # Get it pretty user_1 = raw_input('Select user_1 (0 to %s): ' % data['users']) user_2 = raw_input('Select user_2 (0 to %s): ' % data['users']) user_3 = raw_input('Select user_3 (0 to %s): ' % data['users']) print '\n' # Get it pretty get_recommendations(model, data['matrix'], [user_1, user_2, user_3])