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  3. Building Recommendation Engines in Python
  • 1

    Introduction to Recommendation Engines

    Free

    What problems are recommendation engines designed to solve and what data are best suited for them? Discern what insightful recommendations can be made even with limited data, and learn how to create your own recommendations.

  • 2

    Content-Based Recommendations

    Discover how item attributes can be used to make recommendations. Create valuable comparisons between items with both categorical and text data. Generate profiles to recommend new items for users based on their past preferences.

  • 3

    Collaborative Filtering

    Discover new items to recommend to users by finding others with similar tastes. Learn to make user-based and item-based recommendations—and in what context they should be used. Use k-nearest neighbors models to leverage the wisdom of the crowd and predict how someone might rate an item they haven’t yet encountered.

  • 4

    Matrix Factorization and Validating Your Predictions

    Understand how the sparsity of real-world datasets can impact your recommendations. Leverage the power of matrix factorization to deal with this sparsity. Explore the value of latent features and use them to better understand your data. Finally, put the models you’ve discovered to the test by learning how to validate each of the approaches you’ve learned.

Initializing

Exercise

Comparing recommendation methods

In this course, you have predicted how you believe a user would rate movies they have not seen using multiple different methods (basic average ratings, KNN, matrix factorization). In this final exercise, you'll work through a comparison of the averaged ratings and matrix factorization using the mean_squared_error() as the measure of how well they are performing. The predictions based on averages have been loaded as avg_pred_ratings_df while the calculated predictions have been loaded as calc_pred_ratings_df. The ground truth values have been loaded as act_ratings_df.

Finally, the mean_squared_error() function has been imported for your use from sklearn.metrics.

Instructions

100 XP
  • Extract rows 0-20 and columns 0-100 (the areas that you want to compare) in the act_ratings_df, avg_pred_ratings_df, and calc_pred_ratings_df DataFrames.
  • Create a mask of the actual_values DataFrame that targets only non-empty cells.
  • Find the mean squared error between the two predictions and the ground truth values.