Answer-first summary for fast verification
Answer: Matrix Factorization
Matrix Factorization is the correct choice for collaborative filtering tasks in recommendation systems. This technique decomposes the user-item interaction matrix into two matrices that represent users and items in a lower-dimensional space, enabling the model to uncover latent features that reflect user preferences and item attributes. Databricks MLlib includes matrix factorization as part of its collaborative filtering algorithms, making it a suitable option for such tasks.
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When developing a recommendation system within a machine learning project, which Databricks MLlib-supported algorithm is appropriate for implementing collaborative filtering?
A
Decision Trees
B
k-Means Clustering
C
Matrix Factorization
D
Naive Bayes
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