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How to implement K-fold in this recommendation system?
2019 Community Moderator ElectionHow do recommendation systems work?How to evaluate a Content-based Recommender SystemHow to Implement A Recommendation System?Good resources on recommender systems?Implementing recommendation system for unsupervised learningRecommendation System based on tagsRecommendation system designHow to Implement Recommendation system with WekaKeras AttributeError: 'list' object has no attribute 'ndim'What loss function to use for an Seq2Seq NMT?
0
anyone knows how to implement k-fold in this algoritm, try some methods but didnt work, can anyone help me ?
from sklearn.model_selection import train_test_split
train, test = train_test_split(dataset, test_size=0.2)
n_users, n_movies = len(dataset.userId.unique()), len(dataset.movieId.unique())
n_latent_factors = 3
movie_input = keras.layers.Input(shape=[1],name='Item')
movie_embedding = keras.layers.Embedding(n_movies + 1, n_latent_factors, name='Movie-Embedding')(movie_input)
movie_vec = keras.layers.Conv1D(64,kernel_size=1)(movie_embedding)
movie_vec = keras.layers.MaxPooling1D(pool_size = (1))(movie_vec)
movie_vec = keras.layers.Flatten(name='FlattenMovies')(movie_vec)
user_input = keras.layers.Input(shape=[1],name='User')
user_embedding=(keras.layers.Embedding(n_users + 1, n_latent_factors,name='User-Embedding')(user_input))
user_vec = keras.layers.Conv1D(64,kernel_size=1)(user_embedding)
user_vec = keras.layers.MaxPooling1D(pool_size = (1))(user_vec)
user_vec = keras.layers.Flatten(name='FlattenUsers')(user_vec)
prod = keras.layers.dot([movie_vec, user_vec], axes=1,name='DotProduct')
model = keras.Model([user_input, movie_input], prod)
model.compile('adam', 'mean_squared_error')
history = model.fit([train.userId, train.movieId], train.rating, epochs=40)
keras recommendation-engine
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
add a comment |
0
anyone knows how to implement k-fold in this algoritm, try some methods but didnt work, can anyone help me ?
from sklearn.model_selection import train_test_split
train, test = train_test_split(dataset, test_size=0.2)
n_users, n_movies = len(dataset.userId.unique()), len(dataset.movieId.unique())
n_latent_factors = 3
movie_input = keras.layers.Input(shape=[1],name='Item')
movie_embedding = keras.layers.Embedding(n_movies + 1, n_latent_factors, name='Movie-Embedding')(movie_input)
movie_vec = keras.layers.Conv1D(64,kernel_size=1)(movie_embedding)
movie_vec = keras.layers.MaxPooling1D(pool_size = (1))(movie_vec)
movie_vec = keras.layers.Flatten(name='FlattenMovies')(movie_vec)
user_input = keras.layers.Input(shape=[1],name='User')
user_embedding=(keras.layers.Embedding(n_users + 1, n_latent_factors,name='User-Embedding')(user_input))
user_vec = keras.layers.Conv1D(64,kernel_size=1)(user_embedding)
user_vec = keras.layers.MaxPooling1D(pool_size = (1))(user_vec)
user_vec = keras.layers.Flatten(name='FlattenUsers')(user_vec)
prod = keras.layers.dot([movie_vec, user_vec], axes=1,name='DotProduct')
model = keras.Model([user_input, movie_input], prod)
model.compile('adam', 'mean_squared_error')
history = model.fit([train.userId, train.movieId], train.rating, epochs=40)
keras recommendation-engine
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
add a comment |
0
0
0
anyone knows how to implement k-fold in this algoritm, try some methods but didnt work, can anyone help me ?
from sklearn.model_selection import train_test_split
train, test = train_test_split(dataset, test_size=0.2)
n_users, n_movies = len(dataset.userId.unique()), len(dataset.movieId.unique())
n_latent_factors = 3
movie_input = keras.layers.Input(shape=[1],name='Item')
movie_embedding = keras.layers.Embedding(n_movies + 1, n_latent_factors, name='Movie-Embedding')(movie_input)
movie_vec = keras.layers.Conv1D(64,kernel_size=1)(movie_embedding)
movie_vec = keras.layers.MaxPooling1D(pool_size = (1))(movie_vec)
movie_vec = keras.layers.Flatten(name='FlattenMovies')(movie_vec)
user_input = keras.layers.Input(shape=[1],name='User')
user_embedding=(keras.layers.Embedding(n_users + 1, n_latent_factors,name='User-Embedding')(user_input))
user_vec = keras.layers.Conv1D(64,kernel_size=1)(user_embedding)
user_vec = keras.layers.MaxPooling1D(pool_size = (1))(user_vec)
user_vec = keras.layers.Flatten(name='FlattenUsers')(user_vec)
prod = keras.layers.dot([movie_vec, user_vec], axes=1,name='DotProduct')
model = keras.Model([user_input, movie_input], prod)
model.compile('adam', 'mean_squared_error')
history = model.fit([train.userId, train.movieId], train.rating, epochs=40)
keras recommendation-engine
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
anyone knows how to implement k-fold in this algoritm, try some methods but didnt work, can anyone help me ?
from sklearn.model_selection import train_test_split
train, test = train_test_split(dataset, test_size=0.2)
n_users, n_movies = len(dataset.userId.unique()), len(dataset.movieId.unique())
n_latent_factors = 3
movie_input = keras.layers.Input(shape=[1],name='Item')
movie_embedding = keras.layers.Embedding(n_movies + 1, n_latent_factors, name='Movie-Embedding')(movie_input)
movie_vec = keras.layers.Conv1D(64,kernel_size=1)(movie_embedding)
movie_vec = keras.layers.MaxPooling1D(pool_size = (1))(movie_vec)
movie_vec = keras.layers.Flatten(name='FlattenMovies')(movie_vec)
user_input = keras.layers.Input(shape=[1],name='User')
user_embedding=(keras.layers.Embedding(n_users + 1, n_latent_factors,name='User-Embedding')(user_input))
user_vec = keras.layers.Conv1D(64,kernel_size=1)(user_embedding)
user_vec = keras.layers.MaxPooling1D(pool_size = (1))(user_vec)
user_vec = keras.layers.Flatten(name='FlattenUsers')(user_vec)
prod = keras.layers.dot([movie_vec, user_vec], axes=1,name='DotProduct')
model = keras.Model([user_input, movie_input], prod)
model.compile('adam', 'mean_squared_error')
history = model.fit([train.userId, train.movieId], train.rating, epochs=40)
keras recommendation-engine
keras recommendation-engine
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
edited Mar 8 at 14:12
Ioannis Nasios
3,78931035
3,78931035
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
asked Mar 7 at 18:40
Gustavo Barreto ResendeGustavo Barreto Resende
11
11
add a comment |
add a comment |
0
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