Your e-commerce company has collected only implicit-feedback signals such as clicks and purchase counts and must train a large-scale collaborative-filtering model for more than 10 million users and 2 million products on a distributed Spark cluster. The team is deciding between stochastic gradient descent (SGD) and Alternating Least Squares (ALS) for matrix factorization. Which property of ALS provides the strongest reason to choose it for this distributed workload?
ALS guarantees convergence to the global minimum of the non-convex matrix-factorization objective, ensuring higher accuracy than SGD.
ALS automatically enforces non-negative latent factors, a requirement for models trained on implicit-feedback data.
During each alternating step, the least-squares solves for different users (or items) are independent, so their factor vectors can be computed in parallel across cluster nodes.
ALS needs only a single pass over the interaction data and therefore avoids costly data shuffling in distributed streaming environments.
ALS splits matrix-factorization training into two sub-problems: fixing item factors and solving a regularized least-squares problem for every user, then fixing user factors and solving for every item. Within each of these two phases the individual least-squares solves are independent of one another, so the computations for different users (or items) can run simultaneously on separate executors. This fine-grained independence lets Spark partition the data, broadcast the fixed factor matrix, and update millions of user or item vectors in parallel, delivering good wall-clock performance at scale.
The other statements are incorrect:
ALS does not guarantee a global optimum for the non-convex factorization objective; it can converge to local minima just like SGD.
ALS still needs multiple full passes (iterations) over the interaction matrix, so it is not a single-pass algorithm and is not inherently better for streaming data.
Non-negative constraints are optional; they are applied only when the nonnegative parameter is set, so they are not automatic or required for implicit feedback.
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Why are computations for different users or items considered independent in ALS?
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How does Spark enable scaling ALS computations on millions of users or items?
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What is the benefit of ALS's two-phase alternating process for distributed workloads?