What methods exist for recommendation based on implicit information?
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Assume we have a dataset of which products a user is using on a monthly basis. Let's further assume that the number of users is $n$ and the number of products is $p$ and that we are in the $pll n$ range (e.g. $p=100$ and $n=100.000$). The dataset is in the form of a matrix where the rows denote users and the columns denote whether the user has a given product this month (my first step is just to use the data for a given month but I'm also curious on how to extend it to older data).
I'm trying to come up with sales leads from this type of data. My first approach is to use some matrix factorisation method such as PCA and use the difference between the original matrix and the reconstructed matrix to find leads (i.e. the largest differences should indicate that this user "should" have that product or not and it could justify reaching out).
Another approach I considered is to select any column whatsoever as a variable and predict its value from the other columns using random forest or gradient boosting. Then I can train the classifier on all the rows except one and predict the value of the missing row and if the difference is large compared to the true value then this might be a lead. Could this be a better approach than factorisation?
Another approach is to do some sort of clustering to find customer arch types and try to come up with leads from that.
Are there any other standard methods for this type of problem? How effective do you think lead generation can be from this type of data?
recommender-system unsupervised-learning
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$begingroup$
Assume we have a dataset of which products a user is using on a monthly basis. Let's further assume that the number of users is $n$ and the number of products is $p$ and that we are in the $pll n$ range (e.g. $p=100$ and $n=100.000$). The dataset is in the form of a matrix where the rows denote users and the columns denote whether the user has a given product this month (my first step is just to use the data for a given month but I'm also curious on how to extend it to older data).
I'm trying to come up with sales leads from this type of data. My first approach is to use some matrix factorisation method such as PCA and use the difference between the original matrix and the reconstructed matrix to find leads (i.e. the largest differences should indicate that this user "should" have that product or not and it could justify reaching out).
Another approach I considered is to select any column whatsoever as a variable and predict its value from the other columns using random forest or gradient boosting. Then I can train the classifier on all the rows except one and predict the value of the missing row and if the difference is large compared to the true value then this might be a lead. Could this be a better approach than factorisation?
Another approach is to do some sort of clustering to find customer arch types and try to come up with leads from that.
Are there any other standard methods for this type of problem? How effective do you think lead generation can be from this type of data?
recommender-system unsupervised-learning
asked 5 mins ago
Haffi112Haffi112
1
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Haffi112 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
Assume we have a dataset of which products a user is using on a monthly basis. Let's further assume that the number of users is $n$ and the number of products is $p$ and that we are in the $pll n$ range (e.g. $p=100$ and $n=100.000$). The dataset is in the form of a matrix where the rows denote users and the columns denote whether the user has a given product this month (my first step is just to use the data for a given month but I'm also curious on how to extend it to older data).
I'm trying to come up with sales leads from this type of data. My first approach is to use some matrix factorisation method such as PCA and use the difference between the original matrix and the reconstructed matrix to find leads (i.e. the largest differences should indicate that this user "should" have that product or not and it could justify reaching out).
Another approach I considered is to select any column whatsoever as a variable and predict its value from the other columns using random forest or gradient boosting. Then I can train the classifier on all the rows except one and predict the value of the missing row and if the difference is large compared to the true value then this might be a lead. Could this be a better approach than factorisation?
Another approach is to do some sort of clustering to find customer arch types and try to come up with leads from that.
Are there any other standard methods for this type of problem? How effective do you think lead generation can be from this type of data?
recommender-system unsupervised-learning
asked 5 mins ago
Haffi112Haffi112
1
New contributor
Haffi112 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Assume we have a dataset of which products a user is using on a monthly basis. Let's further assume that the number of users is $n$ and the number of products is $p$ and that we are in the $pll n$ range (e.g. $p=100$ and $n=100.000$). The dataset is in the form of a matrix where the rows denote users and the columns denote whether the user has a given product this month (my first step is just to use the data for a given month but I'm also curious on how to extend it to older data).
I'm trying to come up with sales leads from this type of data. My first approach is to use some matrix factorisation method such as PCA and use the difference between the original matrix and the reconstructed matrix to find leads (i.e. the largest differences should indicate that this user "should" have that product or not and it could justify reaching out).
Another approach I considered is to select any column whatsoever as a variable and predict its value from the other columns using random forest or gradient boosting. Then I can train the classifier on all the rows except one and predict the value of the missing row and if the difference is large compared to the true value then this might be a lead. Could this be a better approach than factorisation?
Another approach is to do some sort of clustering to find customer arch types and try to come up with leads from that.
Are there any other standard methods for this type of problem? How effective do you think lead generation can be from this type of data?
recommender-system unsupervised-learning
recommender-system unsupervised-learning
asked 5 mins ago
Haffi112Haffi112
1
New contributor
Haffi112 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 5 mins ago
Haffi112Haffi112
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New contributor
Haffi112 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 5 mins ago
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asked 5 mins ago
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asked 5 mins ago
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