Explain with an example how to create a recommendation engine with keras?

Explain with an example how to create a recommendation engine with keras?

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)

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