Recommender Systems: An Applied Approach using Deep Learning - Accuracy Versus Recommendations

The video tutorial demonstrates how to plot and analyze the accuracy of top 100 and top 10 recommendations using Python's plotting library. It covers setting up the plot, calculating validation runs, and epochs, and then plotting both validation and training accuracies for top 100 and top 10 recommendations. The tutorial concludes with finalizing the plot with appropriate titles and labels, and analyzing the results to understand the model's performance.

What is the first step in setting up the plot for accuracy comparison?

• Determine the range of epochs

• Label the axes

• Set the plot size using PLT.figure

• Calculate the number of validation runs

How is the number of validation runs determined?

• By counting the epochs

• By using the length of validation factorized top K

• By setting a fixed number

• By using numpy's linspace function

What is plotted against epochs to show validation accuracy for top 100?

• Training accuracy for top 10

• Validation accuracy for top 10

• Validation accuracy for top 100

• Training accuracy for top 100

Which function is used to create a range for plotting training accuracy?

• numpy.random

• numpy.zeros

• numpy.arange

• numpy.linspace

What change is made to plot the top 10 recommendations?

• Adjust the frequency of epochs

• Replace 100 with 10 in the code

• Use a different dataset

• Change the plot size

What is the purpose of PLT.legend in the plot?

• To set the title of the plot

• To display the legend for the plot

• To calculate the accuracy

• To adjust the plot size

What is the next step after plotting the accuracy graphs?

• Make predictions and test the model

• Adjust the plot size

• Recalculate the validation runs

• Change the dataset