Recommendation Evaluation: Precision@k
btibert
New Altair Community Member
In prep for my upcoming lecture, it is pretty straightforward how to demonstrate/discuss recommender systems via RM, though it's a bit unintuitive that we have to hand-enter item/user identification, but it's not the end of the world.
Having said that, in browsing the web for examples to provide, I noticed that there are times references to precision@k, but the only metrics that we get back as part of the performance operator are RMSE, MAE, and NMAE.
Is there an operator that is available to do this and I am just missing it, which is often the case on my end.
Having said that, in browsing the web for examples to provide, I noticed that there are times references to precision@k, but the only metrics that we get back as part of the performance operator are RMSE, MAE, and NMAE.
Is there an operator that is available to do this and I am just missing it, which is often the case on my end.
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Best Answer
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As is always the case, I was thinking about the problem incorrectly. Obviously this only applies to item-based, and above, I am doing rating-based. My mental model was more in line with what we do for classification or regression metrics, where the items are selectable.
This is a non-issue.1
Answers
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As is always the case, I was thinking about the problem incorrectly. Obviously this only applies to item-based, and above, I am doing rating-based. My mental model was more in line with what we do for classification or regression metrics, where the items are selectable.
This is a non-issue.1 -
Hi @btibert,
Just in case, there is an extension (to install from the marketplace) called "Recommender" dedicated to recommender systems (...logic
).
In this extension, you have especially the operator Performance(Item Recommendation) which allows to calculate the precision@k...
Hope this will help in the future...
Regards,
Lionel0
