Welcome to the next step in your journey to understanding recommendation systems. You should be well familiar with classical machine learning metrics like MSE, MAE, accuracy, precision, recall, AUCROC. If not, you can check out our Introduction to Machine Learning with SKLearn course path. These metrics provide insight into how well a recommendation system is performing, but there's more to consider than just accuracy when evaluating such systems. We want to make sure our recommendation systems suggest users diverse but interesting content. In this course, we will focus on metrics that we might track to ensure our recommendation systems bring users joy and excitement.

In this lesson, we'll focus on a crucial metric known as coverage. Coverage measures how diverse and inclusive the recommendations provided by a system are. It's important because a recommendation system that only suggests a limited selection of items is not necessarily useful or engaging for all users. By understanding coverage, we can assess whether our system recommends various items, making it more appealing and fulfilling to users with different tastes and preferences.

Before diving into calculating coverage, it's essential to set up our initial data. We prepared a small sample prediction dataset to consider as an example:

Python
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1# Example Data Setup
2all_possible_items = [1, 2, 3, 4, 5, 6, 7, 8]
3user_predictions = {
4    'User1': [1, 2, 3],
5    'User2': [3, 4, 5],
6    'User3': [5, 6, 7]
7}

In this setup:

• all_possible_items represents the complete set of items that could be recommended to users.
• user_predictions is a dictionary where each key is a user, and the corresponding value is a list of items recommended to that user.

Let's break down what coverage means in our recommendation system context. Coverage is the proportion of unique items the system recommends out of all possible items. A high coverage score indicates a diverse array of recommendations.

The formula to calculate coverage is simple:

$\text{Coverage} = \frac{\text{Number of unique recommended items}}{\text{Total number of items}}$

In this context:

• Unique recommended items are the different items suggested across all users.
• Total number of items refers to every potential item present in the all_possible_items list.

Now, let's implement the calculation of the coverage metric using a step-by-step walkthrough of the provided solution code.

Here's the code we'll be looking at:

Python
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1def coverage(predictions, all_items):
2    recommended_items = set()
3    for user_preds in predictions.values():
4        for pred in user_preds:
5            recommended_items.add(pred)
6    return len(recommended_items) / len(all_items)
7
8
9# Example data
10all_possible_items = [1, 2, 3, 4, 5, 6, 7, 8]
11user_predictions = {
12    'User1': [1, 2, 3],
13    'User2': [3, 4, 5],
14    'User3': [5, 6, 7]
15}
16
17coverage_score = coverage(user_predictions, all_possible_items)
18print(f"Coverage: {coverage_score:.2f}")  # Coverage: 0.88

Let's break it down:

1. Function coverage: This function takes two parameters — predictions (the dictionary of user predictions) and all_items (the list of all possible items).
2. Collecting Recommended Items:
   • We use a set comprehension to gather all unique items that have been recommended. This is done by iterating over each user's predictions.
   • The set automatically handles duplicates, ensuring that only unique items are collected.
3. Calculating Coverage:
   • The coverage is calculated by dividing the length of the recommended_items set by the length of all_items.
4. Example Calculation:
   • Using our example data, the coverage score is calculated. The result is printed in a nicely formatted string, displaying the coverage to two decimal places.

This output indicates that approximately 88% of the possible items have been recommended across all users, suggesting a reasonably diverse recommendation system.

To recap, in this lesson, you learned about the importance of the coverage metric in evaluating recommendation systems. By calculating coverage, we can determine how widely our system's recommendations span across all potential items. A high coverage score indicates variety and diversity, which are vital for user satisfaction.

You've seen how we set up our data, understood the formula for coverage, and walked through the code to calculate the coverage score using practical examples.

As you move on to the next section, which includes exercises and practices, focus on applying the concepts you've learned here. Experiment with different datasets and observe how they affect the coverage score. Keep in mind the balance between coverage and other metrics as you fine-tune your recommendation systems.

Congratulations on reaching this milestone in your learning journey. Your understanding of recommendation system metrics is growing, and this will be incredibly valuable as you create systems that cater to diverse user needs and preferences. Good luck with your practice!