Step 4: Evaluating a solution version - Amazon Personalize

Step 4: Evaluating a solution version

You can evaluate the performance of your solution version through offline and online metrics. Online metrics are the empirical results you observe in your users' interactions with real-time recommendations. For example, you might record your users' click-through rate as they browse your catalog. You are responsible for generating and recording any online metrics.

Offline metrics are the metrics Amazon Personalize generates when you train a solution version. You can use offline metrics to evaluate the performance of the model before you create a campaign and provide recommendations. Offline metrics allow you to view the effects of modifying a solution's hyperparameters or compare results from solutions that use the same training data but use different recipes. For the rest of this section, the term metrics refers to offline metrics.

To get performance metrics, Amazon Personalize splits the input interactions data into two sets: a training set and a testing set. The training set consists of 90% of your users and their interactions data. The testing set consists of the remaining 10% of users and their interactions data. Amazon Personalize then creates the solution version using the training set.

When training completes, Amazon Personalize gives the solution version the oldest 90% of each user’s data from the testing set as input. Amazon Personalize then calculates metrics by comparing the recommendations the solution version generates to the actual interactions in the newest 10% of each user’s data from the testing set.

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

Important

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

Metrics

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.

coverage

An evaluation metric that tells you the proportion of unique items that Amazon Personalize might recommend using your model out of the total number of unique items in Interactions and Items datasets. To make sure Amazon Personalize recommends more of your items, use a model with a higher coverage score. Recipes that feature item exploration, such as user-personalization, have higher coverage than those that don’t, such as popularity-count.

mean reciprocal rank at 25

An evaluation metric that assesses the relevance of a model’s highest ranked recommendation. Amazon Personalize calculates this metric using the average accuracy of the model when ranking the most relevant recommendation out of the top 25 recommendations over all requests for recommendations.

normalized discounted cumulative gain (NCDG) at K (5/10/25)

An evaluation metric that tells you about the relevance of your model’s highly ranked recommendations, where K is a sample size of 5, 10, or 25 recommendations. Amazon Personalize calculates this by assigning weight to recommendations based on their position in a ranked list, where each recommendation is discounted (given a lower weight) by a factor dependent on its position. The normalized discounted cumulative gain at K assumes that recommendations that are lower on a list are less relevant than recommendations higher on the list.

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.

precision at K

An evaluation metric that tells you how relevant your model’s recommendations are based on a sample size of K (5, 10, or 25) recommendations. Amazon Personalize calculates this metric based on the number of relevant recommendations out of the top K recommendations, divided by K, where K is 5, 10, or 25.

This metric rewards precise recommendation of the relevant items.

Example

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.

reciprocal_rank

Calculation: 1/2

Result: 0.5000

normalized_discounted_cumulative_gain_at_5

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

Result: 0.6241

precision_at_5

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.