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Debugging the MS Azure Recommendations Engine

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Debugging the MS Azure Recommendations Engine

The author shares with us some tips on how to help debug the MS Azure Recommendations Engine, especially when you're not getting any results back!

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Here, I wrote about how to set up and configure the MS Azure recommendations engine.

One thing that has become painfully apparent while working with recommendations is how difficult it is to work out what has gone wrong when you don’t get any recommendations. The following is a handy checklist for the next time this happens to me… so others may, or may not, find this useful.

Check that the Model Was Correctly Generated

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Once you have produced a recommendations model, you can access that model by simply navigating to it. The URL is in the following format:

{recommendations uri}/ui

For example:


This gives you a screen such as this:

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The status (listed in the center of the screen) tells you whether the build has finished and, if so, whether it succeeded or not.

If the build has failed, you can select that row and drill into it to find out why.

In the following example, there is a reference in the usage data, to an item that is not in the catalog.

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Other reasons that the model build may fail include invalid, corrupt, or missing data in either file.

Check the Recommendation in the Interface

In order to exclude other factors in your code, you can manually interrogate the model directly by simply clicking on the “Score” link above; you will be presented with a screen such as this:

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In here, you can request direct recommendations to see how the model behaves.


If you find that your score is consistently returning as zero, then the issue may be with the volume of usage data that you have provided. 1,000 rows of usage data is the sort of volume you should be dealing with; this statistic was based on a catalog of around 20-30 products. That's an arbitrary number, however, so your mileage may vary.


The number of users matters – for the above figures, a minimum of 15 users was necessary to get any scores back. If the data sample is across too small a user base, it won’t return anything.

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