How to select random data for 2 different recommender systems?
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The business problems : we have 2 different vendors that offer personalized recommender engine and want to do A/B testing with them. The recommendation would give the user a personalized offer via a push message on the phone. During the testing period, we should give each provider a dataset with different details regarding the customers (purchase history, in-app events etc). Each vendor will receive a dataset with identical info but for different clients.
What is the best method to choose the 2 datasets so that they would be similar in terms of client behaviour?
I assumed that giving them random data from our database wouldn't be a rigorous method so one idea that I have in mind is applying dbScan clustering on our database and further randomly picking clients from each cluster - don't know is this is the best approach. The full database has 200k clients and each dataset should contain 5k clients.
ex: after dbScan clustering there are k = 10 clusters so I randomly pick elements from each cluster and split them in Dataset01 and Dataset02
Any suggestions?
dataset statistics recommender-system ab-test
asked 57 mins ago
Remus RaphaelRemus Raphael
61
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Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$begingroup$
The business problems : we have 2 different vendors that offer personalized recommender engine and want to do A/B testing with them. The recommendation would give the user a personalized offer via a push message on the phone. During the testing period, we should give each provider a dataset with different details regarding the customers (purchase history, in-app events etc). Each vendor will receive a dataset with identical info but for different clients.
What is the best method to choose the 2 datasets so that they would be similar in terms of client behaviour?
I assumed that giving them random data from our database wouldn't be a rigorous method so one idea that I have in mind is applying dbScan clustering on our database and further randomly picking clients from each cluster - don't know is this is the best approach. The full database has 200k clients and each dataset should contain 5k clients.
ex: after dbScan clustering there are k = 10 clusters so I randomly pick elements from each cluster and split them in Dataset01 and Dataset02
Any suggestions?
dataset statistics recommender-system ab-test
asked 57 mins ago
Remus RaphaelRemus Raphael
61
New contributor
Remus Raphael 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 |
$begingroup$
The business problems : we have 2 different vendors that offer personalized recommender engine and want to do A/B testing with them. The recommendation would give the user a personalized offer via a push message on the phone. During the testing period, we should give each provider a dataset with different details regarding the customers (purchase history, in-app events etc). Each vendor will receive a dataset with identical info but for different clients.
What is the best method to choose the 2 datasets so that they would be similar in terms of client behaviour?
I assumed that giving them random data from our database wouldn't be a rigorous method so one idea that I have in mind is applying dbScan clustering on our database and further randomly picking clients from each cluster - don't know is this is the best approach. The full database has 200k clients and each dataset should contain 5k clients.
ex: after dbScan clustering there are k = 10 clusters so I randomly pick elements from each cluster and split them in Dataset01 and Dataset02
Any suggestions?
dataset statistics recommender-system ab-test
asked 57 mins ago
Remus RaphaelRemus Raphael
61
New contributor
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
The business problems : we have 2 different vendors that offer personalized recommender engine and want to do A/B testing with them. The recommendation would give the user a personalized offer via a push message on the phone. During the testing period, we should give each provider a dataset with different details regarding the customers (purchase history, in-app events etc). Each vendor will receive a dataset with identical info but for different clients.
What is the best method to choose the 2 datasets so that they would be similar in terms of client behaviour?
I assumed that giving them random data from our database wouldn't be a rigorous method so one idea that I have in mind is applying dbScan clustering on our database and further randomly picking clients from each cluster - don't know is this is the best approach. The full database has 200k clients and each dataset should contain 5k clients.
ex: after dbScan clustering there are k = 10 clusters so I randomly pick elements from each cluster and split them in Dataset01 and Dataset02
Any suggestions?
dataset statistics recommender-system ab-test
dataset statistics recommender-system ab-test
asked 57 mins ago
Remus RaphaelRemus Raphael
61
New contributor
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 57 mins ago
Remus RaphaelRemus Raphael
61
New contributor
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 57 mins ago
Remus RaphaelRemus Raphael
61
New contributor
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 57 mins ago
Remus RaphaelRemus Raphael
61
asked 57 mins ago
Remus RaphaelRemus Raphael
61
61
New contributor
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Remus Raphael is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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Welcome @Remus Raphael :) - Your approach is a sound option.
More specifically, if a density-based algorithm was already working for you, I'd recommend the HDBSCAN clustering algo, which should have a better performance and has a unique built-in cluster validation (based on the DBCV algorithm).
Then your general pipe line could be:
- An optional pre-processing
- An optional NLP / TFIDF for meaningful text features
- An optional dimensionality reduction (I found TSNE and TruncatedSVD to
work nicely with HDBSCAN with textual processing) - HDBSCAN tuning for different params and distance metrics
- Finally, when you're satisfied with the clustering quality - you can simply start with the 2 largest clusters for your AB testing
I'd love to hear you feedback on your actual data :)
answered 3 mins ago
morkmork
26413
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1 Answer
1
active
oldest
votes
1 Answer
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active
oldest
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active
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votes
$begingroup$
Welcome @Remus Raphael :) - Your approach is a sound option.
More specifically, if a density-based algorithm was already working for you, I'd recommend the HDBSCAN clustering algo, which should have a better performance and has a unique built-in cluster validation (based on the DBCV algorithm).
Then your general pipe line could be:
- An optional pre-processing
- An optional NLP / TFIDF for meaningful text features
- An optional dimensionality reduction (I found TSNE and TruncatedSVD to
work nicely with HDBSCAN with textual processing) - HDBSCAN tuning for different params and distance metrics
- Finally, when you're satisfied with the clustering quality - you can simply start with the 2 largest clusters for your AB testing
I'd love to hear you feedback on your actual data :)
answered 3 mins ago
morkmork
26413
$endgroup$
add a comment |
$begingroup$
Welcome @Remus Raphael :) - Your approach is a sound option.
More specifically, if a density-based algorithm was already working for you, I'd recommend the HDBSCAN clustering algo, which should have a better performance and has a unique built-in cluster validation (based on the DBCV algorithm).
Then your general pipe line could be:
- An optional pre-processing
- An optional NLP / TFIDF for meaningful text features
- An optional dimensionality reduction (I found TSNE and TruncatedSVD to
work nicely with HDBSCAN with textual processing) - HDBSCAN tuning for different params and distance metrics
- Finally, when you're satisfied with the clustering quality - you can simply start with the 2 largest clusters for your AB testing
I'd love to hear you feedback on your actual data :)
answered 3 mins ago
morkmork
26413
$endgroup$
add a comment |
$begingroup$
Welcome @Remus Raphael :) - Your approach is a sound option.
More specifically, if a density-based algorithm was already working for you, I'd recommend the HDBSCAN clustering algo, which should have a better performance and has a unique built-in cluster validation (based on the DBCV algorithm).
Then your general pipe line could be:
- An optional pre-processing
- An optional NLP / TFIDF for meaningful text features
- An optional dimensionality reduction (I found TSNE and TruncatedSVD to
work nicely with HDBSCAN with textual processing) - HDBSCAN tuning for different params and distance metrics
- Finally, when you're satisfied with the clustering quality - you can simply start with the 2 largest clusters for your AB testing
I'd love to hear you feedback on your actual data :)
answered 3 mins ago
morkmork
26413
$endgroup$
Welcome @Remus Raphael :) - Your approach is a sound option.
More specifically, if a density-based algorithm was already working for you, I'd recommend the HDBSCAN clustering algo, which should have a better performance and has a unique built-in cluster validation (based on the DBCV algorithm).
Then your general pipe line could be:
- An optional pre-processing
- An optional NLP / TFIDF for meaningful text features
- An optional dimensionality reduction (I found TSNE and TruncatedSVD to
work nicely with HDBSCAN with textual processing) - HDBSCAN tuning for different params and distance metrics
- Finally, when you're satisfied with the clustering quality - you can simply start with the 2 largest clusters for your AB testing
I'd love to hear you feedback on your actual data :)
answered 3 mins ago
morkmork
26413
answered 3 mins ago
morkmork
26413
answered 3 mins ago
morkmork
26413
answered 3 mins ago
morkmork
26413
26413
add a comment |
add a comment |
Remus Raphael is a new contributor. Be nice, and check out our Code of Conduct.
Remus Raphael is a new contributor. Be nice, and check out our Code of Conduct.
Remus Raphael is a new contributor. Be nice, and check out our Code of Conduct.
Remus Raphael is a new contributor. Be nice, and check out our Code of Conduct.
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