Confusing remarks between score and rating in this tutorial!

I am confused between `score` and `rating` (`Label`) in this tutorial as they provide very different results!

"You can then use the Score, or the predicted rating, to determine whether you want to recommend the movie with movieId 10 to user 6. The higher the Score, the higher the likelihood of a user liking a particular movie. In this case, let’s say that you recommend movies with a predicted rating of > 3.5."

The page mentions using "rating of > 3.5", which is the prediction `Label`, but the code uses the score and therefore a conflicting wrong result between the 2. I printed out the result as follows:
```
=============== Making a prediction ===============
Rating: 0 Score: 3.737763
Movie 10 is not recommended for user 6
```
How I interpret this is that the `score` tells how accurate the prediction `rating (Label) is. Therefore, if the `score` is higher than the threshold (therefore higher accuracy), then if the `rating` is high, recommend the input data. Otherwise, do not recommend it.

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#### Document Details

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* ID: 0888bc80-798f-ebdd-459a-7543be1efdad
* Version Independent ID: c24ded82-d56c-e8c7-fd6a-e39929227177
* Content: [Tutorial: Build a movie recommender - matrix factorization - ML.NET](https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/movie-recommendation)
* Content Source: [docs/machine-learning/tutorials/movie-recommendation.md](https://github.com/dotnet/docs/blob/main/docs/machine-learning/tutorials/movie-recommendation.md)
* Product: **dotnet-ml**
* GitHub Login: @briacht
* Microsoft Alias: **luquinta**