C3_W2_Collaborative_RecSys_Assignment: Intuition of adding 13 movie ratings of one user to the collaborative movie matrix

Course Q&A Machine Learning Specialization Unsupervised Learning, Recommenders, Reinforcement
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Dear Raymond,

in section “6 - Recommendations“…

  1. …I understand that by adding one new user with 13 movie ratings to the overall collaborative movie matrix y^(i,j) with 443 users we predict new movie ratings also for unrated movies sorted by highest ratings (see my_predictions).
  2. …Also, we compare the predictions against the added new user ratings of 13 movies.

Is this correct?

If, yes…

  • What are the 100 features? Are these 100 different movie categories such as romantic, action?
  • Why are the predicted ratings using the calculated parameters w, x and b whereas these parameters are based on the new user’s ratings among 443 other users so close to the provided 13 movie ratings of the new user? How can the impact of one additional user be so big to be that close to the original user’s ratings? Is this because the new user has a mainstream rating?

Thanks!

BR, Daniel from Germany