Explain with an example how to create a recommendation engine with keras?
This recipe explains with an example how to create a recommendation engine with keras
Recipe Objective
Creating a recommendation engine with keras
We have preprocessed the IMDB movie data set and made it perfect for the making a new function for recommendation.
Step 1- Importing Libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from keras.models import Model
from keras.layers import Input, Reshape, Dot
from keras.layers.embeddings import Embedding
from keras.optimizers import Adam
from keras.regularizers import l2
Step 2- Reading imdb file.
Reading and displaying the preprocessed imdb file.
ratings= pd.read_csv('path')
ratings.head()
Step 3- Defining the function.
def Recommender(n_users, n_movies, n_factors):
admin = Input(shape=(1,))
u = Embedding(n_users, n_factors, embeddings_initializer='he_normal',
embeddings_regularizer=l2(0.001))(admin)
u = Reshape((n_factors,))(u)
movie = Input(shape=(1,))
m = Embedding(n_movies, n_factors, embeddings_initializer='he_normal',
embeddings_regularizer=l2(0.001))(movie)
m = Reshape((n_factors,))(m)
x = Dot(axes=1)([u, m])
model = Model(inputs=[admin, movie], outputs=x)
opt = Adam(lr=0.001)
model.compile(loss='mean_squared_error', optimizer=opt)
return model
print(Recommender)
What Users are saying..
Ameeruddin Mohammed
ETL (Abintio) developer at IBM
I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More
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