Step 4: Evaluating a Solution Version - Amazon Personalize

Step 4: Evaluating a Solution Version

When you create a solution version, Amazon Personalize generates metrics that you can use to evaluate the performance of the model before you create a campaign and provide recommendations. Metrics allow you to view the effects of modifying a solution's hyperparameters. You can also use metrics to compare the results between solutions that use the same training data but were created with different recipes.

To get performance metrics, Amazon Personalize splits the input interactions data by randomly selecting 90% of users and their related interactions as training data and the other 10% as testing data. The solution version is then created using the training data. Afterwards, the solution version is given the oldest 90% of each user's testing data as input, and the recommendations it generates are compared against the real interactions given by the most recent 10% of testing data.

To generate a baseline for comparison purposes, we recommend using the Popularity-Count recipe, which recommends the top K most popular items.


In order for Amazon Personalize to generate solution version metrics, you must have at least 10 datapoints in your input dataset group.


You retrieve the metrics for a specific solution version by calling the GetSolutionMetrics operation.

Retrieve metrics using the AWS Python SDK

  1. Create a solution version. For more information, see Creating a Solution.

  2. Use the following code to retrieve metrics.

    import boto3 personalize = boto3.client('personalize') response = personalize.get_solution_metrics( solutionVersionArn = 'solution version arn') print(response['metrics'])

The following is an example of the output from a solution version created using the HRNN recipe with the default solution configuration.

{ "solutionVersionArn": "arn:aws:personalize:us-west-2:acct-id:solution/MovieSolution/<version-id>", "metrics": { "coverage": 0.27, "mean_reciprocal_rank_at_25": 0.0379, "normalized_discounted_cumulative_gain_at_5": 0.0405, "normalized_discounted_cumulative_gain_at_10": 0.0513, "normalized_discounted_cumulative_gain_at_25": 0.0828, "precision_at_5": 0.0136, "precision_at_10": 0.0102, "precision_at_25": 0.0091 } }

The above metrics are described below using the following terms:

  • Relevant recommendation refers to a recommendation that matches a value in the testing data for the particular user.

  • Rank refers to the position of a recommended item in the list of recommendations. Position 1 (the top of the list) is presumed to be the most relevant to the user.

  • Query refers to the internal equivalent of a GetRecommendations call.

For each metric, higher numbers are better.


The proportion of unique recommended items from all queries out of the total number of unique items in the interactions and items datasets.


The mean of the reciprocal ranks of the first relevant recommendation out of the top 25 recommendations over all queries.

This metric is appropriate if you're interested in the single highest ranked recommendation.


Discounted gain assumes that recommendations lower on a list of recommendations are less relevant than higher recommendations. Therefore, each recommendation is discounted (given a lower weight) by a factor dependent on its position. To produce the cumulative discounted gain (DCG) at K, each relevant discounted recommendation in the top K recommendations is summed together. The normalized discounted cumulative gain (NDCG) is the DCG divided by the ideal DCG such that NDCG is between 0 - 1. (The ideal DCG is where the top K recommendations are sorted by relevance.)

Amazon Personalize uses a weighting factor of 1/log(1 + position), where the top of the list is position 1.

This metric rewards relevant items that appear near the top of the list, because the top of a list usually draws more attention.


The number of relevant recommendations out of the top K recommendations divided by K.

This metric rewards precise recommendation of the relevant items.


The following is a simple example where, to generate metrics, a solution version produces a list of recommendations for a specific user. The second and fifth recommendations match records in the testing data for this user. These are the relevant recommendations. If K is set at 5, the following metrics are generated for the user.


Calculation: 1/2

Result: 0.5000


Calculation: (1/log(1 + 2) + 1/log(1 + 5)) / (1/log(1 + 1) + 1/log(1 + 2))

Result: 0.6241


Calculation: 2/5

Result: 0.4000

Now that you have evaluated your solution version, create a campaign by deploying the optimum solution version. For more information, see Creating a Campaign.