How to training the recurrent recommender system with LSTM?
$begingroup$
Recently, I read a paper about recurrent recommender system, I am very curious about how it training its network.
Assume I have the Netflix dataset as
UserID itemID timestamp
2 10 1244
2 13 895
2 6 1256
2 7 1865
2 11 256
1 9 1284
1 13 653
1 8 1200
1 4 1321
The paper says it split the data as training and test dataset based on time
assume we split the training set as
UserID(training) itemID(training) timestamp(training)
2 10 1244
2 13 895
2 6 1256
2 11 256
1 9 1284
1 13 653
1 8 1200
And test dataset as
UserID(test) itemID(test) timestamp(test)
2 7 1865
1 4 1821
Now we can start to train the network:
my question is
(1) how they feed above instance (user, item, timestamp) into above network?
(2) what if different has different number of item and how to represent $y_{i,t-2}, such as user1 has 10 items in the training data; user2 has only 2 items in the training data, then the length of LSTM will be different. does it matter.
(3) if want to using mini-batch gradient, How to split the training data? just randomly split or we need to split based on timestamp?
tensorflow lstm recommender-system recurrent-neural-net
asked 12 mins ago
jasonjason
1031
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add a comment |
$begingroup$
Recently, I read a paper about recurrent recommender system, I am very curious about how it training its network.
Assume I have the Netflix dataset as
UserID itemID timestamp
2 10 1244
2 13 895
2 6 1256
2 7 1865
2 11 256
1 9 1284
1 13 653
1 8 1200
1 4 1321
The paper says it split the data as training and test dataset based on time
assume we split the training set as
UserID(training) itemID(training) timestamp(training)
2 10 1244
2 13 895
2 6 1256
2 11 256
1 9 1284
1 13 653
1 8 1200
And test dataset as
UserID(test) itemID(test) timestamp(test)
2 7 1865
1 4 1821
Now we can start to train the network:
my question is
(1) how they feed above instance (user, item, timestamp) into above network?
(2) what if different has different number of item and how to represent $y_{i,t-2}, such as user1 has 10 items in the training data; user2 has only 2 items in the training data, then the length of LSTM will be different. does it matter.
(3) if want to using mini-batch gradient, How to split the training data? just randomly split or we need to split based on timestamp?
tensorflow lstm recommender-system recurrent-neural-net
asked 12 mins ago
jasonjason
1031
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Recently, I read a paper about recurrent recommender system, I am very curious about how it training its network.
Assume I have the Netflix dataset as
UserID itemID timestamp
2 10 1244
2 13 895
2 6 1256
2 7 1865
2 11 256
1 9 1284
1 13 653
1 8 1200
1 4 1321
The paper says it split the data as training and test dataset based on time
assume we split the training set as
UserID(training) itemID(training) timestamp(training)
2 10 1244
2 13 895
2 6 1256
2 11 256
1 9 1284
1 13 653
1 8 1200
And test dataset as
UserID(test) itemID(test) timestamp(test)
2 7 1865
1 4 1821
Now we can start to train the network:
my question is
(1) how they feed above instance (user, item, timestamp) into above network?
(2) what if different has different number of item and how to represent $y_{i,t-2}, such as user1 has 10 items in the training data; user2 has only 2 items in the training data, then the length of LSTM will be different. does it matter.
(3) if want to using mini-batch gradient, How to split the training data? just randomly split or we need to split based on timestamp?
tensorflow lstm recommender-system recurrent-neural-net
asked 12 mins ago
jasonjason
1031
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Recently, I read a paper about recurrent recommender system, I am very curious about how it training its network.
Assume I have the Netflix dataset as
UserID itemID timestamp
2 10 1244
2 13 895
2 6 1256
2 7 1865
2 11 256
1 9 1284
1 13 653
1 8 1200
1 4 1321
The paper says it split the data as training and test dataset based on time
assume we split the training set as
UserID(training) itemID(training) timestamp(training)
2 10 1244
2 13 895
2 6 1256
2 11 256
1 9 1284
1 13 653
1 8 1200
And test dataset as
UserID(test) itemID(test) timestamp(test)
2 7 1865
1 4 1821
Now we can start to train the network:
my question is
(1) how they feed above instance (user, item, timestamp) into above network?
(2) what if different has different number of item and how to represent $y_{i,t-2}, such as user1 has 10 items in the training data; user2 has only 2 items in the training data, then the length of LSTM will be different. does it matter.
(3) if want to using mini-batch gradient, How to split the training data? just randomly split or we need to split based on timestamp?
tensorflow lstm recommender-system recurrent-neural-net
tensorflow lstm recommender-system recurrent-neural-net
asked 12 mins ago
jasonjason
1031
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
jasonjason
1031
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited 7 mins ago
jason
asked 12 mins ago
jasonjason
1031
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 12 mins ago
jasonjason
1031
asked 12 mins ago
jasonjason
1031
1031
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
jason is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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