In a collaborative filtering recommender system, you apply truncated SVD to a user-item rating matrix: A is approximately U_k * Sigma_k * V_k-transpose. What does each component represent?

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Answer

Sigma_k directly gives the predicted rating for each user-item pair.

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U_k encodes latent user factors, V_k-transpose encodes latent item factors, and Sigma_k weights the importance of each latent concept.

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U_k and V_k-transpose are identical matrices since the rating matrix is approximately square.

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U_k contains user ratings, Sigma_k contains item ratings, and V_k-transpose contains the predicted full rating matrix.

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In a collaborative filtering recommender system, you apply truncated SVD to a us...

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