Implementation of the winning recommender system from the Netflix competition.
Uses matrix decomposition to derive a P and Q matrix which can be used to make predictions.
Uses gradient descent to arrive at the solution.
Predict using the ProductRecommender Class.
Can be used in two ways:
- First: To recommend using only a users x product matrix along with a k value for # of features to discover.
#import predictor
from recommender.matrix_factor_model import ProductRecommender
# fake data. (2 users, three products)
user_1 = [1, 2, 3]
user_2 = [0, 2, 3]
data = [user_1, user_2]
# train model
modelA = ProductRecommender()
modelA.fit(data)
# predict for user 2
modelA.predict_instance(1)
# prints array([ 0.9053102 , 2.02257811, 2.97001565])- Second: To recommend when you want to provide a list of features for movies and only learn P (user -> feature strength).
#import predictor
from recommender.matrix_factor_model import ProductRecommender
# fake data. (2 users, three products)
user_1 = [1, 2, 3]
user_2 = [0, 2, 3]
data = [user_1, user_2]
# product features (year made, height)
product_1 = [2014, 74]
product_2 = [2016, 89]
Q = [product_1, product_2]
# train model passing in Q
modelB = ProductRecommender()
modelB.fit(data, Q)
# predict for user 2
modelB.predict_instance(1)
# prints array([ 0.9053102 , 2.02257811, 2.97001565])| parameter | default value | description |
|---|---|---|
| user_x_product | Must provide | User x product Matrix |
| product_x_features | Optional (null) | Products_x_features matrix. Sets latent_features_guess to number of features here. |
| latent_features_guess | 2 | Features we want to learn |
| learning_rate | 0.0002 | Size of learning steps |
| steps | 5000 | Max number of steps until convergence |
| regularization_penalty | 0.02 | Penalty for over/under fitting |
| convergeance_threshold | 0.001 | Error amount to terminate (we solved the problem). Otherwise uses steps. |
MIT
William Falcon