SVD Recommendations using Tensorflow

svd_recommender_tensorflow.py

import numpy as np
import tensorflow as tf

# Set random seed for reproducibility
np.random.seed(1000)

nb_users = 5000
nb_products = 2000
nb_factors = 500
max_rating = 5
nb_rated_products = 500
top_k_products = 10

# Create a random User-Item matrix
uim = np.zeros((nb_users, nb_products), dtype=np.float32)

for i in range(nb_users):
    nbp = np.random.randint(0, nb_products, size=nb_rated_products)
    for j in nbp:
        uim[i, j] = np.random.randint(1, max_rating+1)

# Create a Tensorflow graph
graph = tf.Graph()

with graph.as_default():
    # User-item matrix
    user_item_matrix = tf.placeholder(tf.float32, shape=(nb_users, nb_products))
    
    # SVD
    St, Ut, Vt = tf.svd(user_item_matrix)
    
    # Compute reduced matrices
    Sk = tf.diag(St)[0:nb_factors, 0:nb_factors]
    Uk = Ut[:, 0:nb_factors]
    Vk = Vt[0:nb_factors, :]
    
    # Compute Su and Si
    Su = tf.matmul(Uk, tf.sqrt(Sk))
    Si = tf.matmul(tf.sqrt(Sk), Vk)
    
    # Compute user ratings
    ratings_t = tf.matmul(Su, Si)
    
    # Pick top k suggestions
    best_ratings_t, best_items_t = tf.nn.top_k(ratings_t, top_k_products)

# Create Tensorflow session
session = tf.InteractiveSession(graph=graph)

# Compute the top k suggestions for all users
feed_dict = {
    user_item_matrix: uim
}

best_items = session.run([best_items_t], feed_dict=feed_dict)

# Suggestions for user 1000, 1010
for i in range(1000, 1010):
    print('User {}: {}'.format(i, best_items[0][i]))

test.txt

for i in range(1000, 1010):
    print('User {}: {}'.format(i, best_items[0][i]))
    
User 1000: [ 412  867 1040  509 1311 1562  758 1796  636  556]
User 1001: [ 548   88 1299  175   81 1837  282 1555 1796 1902]
User 1002: [ 433  667  460  821 1762  775 1673  278  284 1540]
User 1003: [1823  602 1874   43 1979 1612 1755  857  891 1701]
User 1004: [1700 1312  892  621  194 1919  196 1746 1697 1192]
User 1005: [ 891  221 1112 1387  768 1697  916  485 1673 1515]
User 1006: [ 463  611 1986 1253  175 1362 1112 1811 1045  768]
User 1007: [1170   70 1886  757  412  606  892 1772 1540 1415]
User 1008: [ 757  855  509  329  410 1304 1900 1631  476  284]
User 1009: [ 888 1654    6 1453  735 1745  505  422 1878 1965]

hello Giuseppe Bonaccorso,

I want to know what is dataset? (the structure of data).
I 'm newbie. I want to put the real data in to it.

Thank you.