How do I recommend items to out of training users based on its recent views?
$begingroup$
I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings.
Since we have a lot of users in system, I had to sample some users to train model to avoid overfitting.
Now how do I construct user embeddings for out of training users. I tried constructing user embeddings by averaging item embeddings for user's items. But when I compared performance of average vector vs original user embeddings, it is not that great.
So how would I generate user embeddings using item matrix and rating matrix?
machine-learning recommender-system apache-spark embeddings matrix-factorisation
asked 2 hours ago
Sn.Sn.
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$begingroup$
I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings.
Since we have a lot of users in system, I had to sample some users to train model to avoid overfitting.
Now how do I construct user embeddings for out of training users. I tried constructing user embeddings by averaging item embeddings for user's items. But when I compared performance of average vector vs original user embeddings, it is not that great.
So how would I generate user embeddings using item matrix and rating matrix?
machine-learning recommender-system apache-spark embeddings matrix-factorisation
asked 2 hours ago
Sn.Sn.
61
New contributor
Sn. 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 |
$begingroup$
I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings.
Since we have a lot of users in system, I had to sample some users to train model to avoid overfitting.
Now how do I construct user embeddings for out of training users. I tried constructing user embeddings by averaging item embeddings for user's items. But when I compared performance of average vector vs original user embeddings, it is not that great.
So how would I generate user embeddings using item matrix and rating matrix?
machine-learning recommender-system apache-spark embeddings matrix-factorisation
asked 2 hours ago
Sn.Sn.
61
New contributor
Sn. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings.
Since we have a lot of users in system, I had to sample some users to train model to avoid overfitting.
Now how do I construct user embeddings for out of training users. I tried constructing user embeddings by averaging item embeddings for user's items. But when I compared performance of average vector vs original user embeddings, it is not that great.
So how would I generate user embeddings using item matrix and rating matrix?
machine-learning recommender-system apache-spark embeddings matrix-factorisation
machine-learning recommender-system apache-spark embeddings matrix-factorisation
asked 2 hours ago
Sn.Sn.
61
New contributor
Sn. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
Sn.Sn.
61
New contributor
Sn. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
Sn.Sn.
61
New contributor
Sn. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
Sn.Sn.
61
asked 2 hours ago
Sn.Sn.
61
61
New contributor
Sn. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Sn. is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Sn. 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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