FunkSVD: math, code, prediction, and validation

Understand basic FunkSVD and focus on validation cases

Photo by Solé Bicycles on Unsplash
  1. Based on 1, make predictions, validation and will give emphasis on the different cases in order to help to understand the validation process
  1. The predicted ratings can be computed as R=HW, where R is the user-item rating matrix, H contains the user’s latent factors and W is the item’s latent factors. Specifically, the predicted rating r for user u will give to item i is computed as:
Pic from Wiki[1]
  • User’s latent matrix: which needs to construct
  • Item’s latent matrix: which needs to construct
Pic from Udacity course [2]
Pic from Udacity[2]
Pic from Udacity[2]
user_mat, movie_mat = FunkSVD(rating_mat, latent_features=12, learning_rate=0.005, iters=100)
  1. User in both training and validation set, but movie rating doesn’t exist in the training set, for example, user_id=29000, movie_id=287978
  2. User in the validation set, but not in the training set: for example, user_id=220, movie_id=1598822
  3. User in the training set, but not in the validation set:user_id=8, movie_id=1598822
  • Item (not from the user) has been rated in the training dataset, and item (from the user) exists in the validation dataset
  1. Udacity Data Scientist Nanodegree -Experimental Design and Recommendations
  2. Funk, Simon

passionate about data analysis and data science

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