Alternating least squares

As explained in AI-Powered Search

ALS is the alternating least squares algorithm, which estimates factor matrices by iteratively optimizing one factor while holding the other fixed.

How AI-Powered Search covers it

How deep it goes

Explains alternating optimization and rank, then demonstrates data preparation, Spark training, evaluation, and recommendation indexing.

Examples

Uses clicks, carts, purchases, product identifiers, Spark recommendations, and indexed user–product boosts.

What it assumes

Assumes user–item interactions and introduces latent features, implicit ratings, integer indexing, rank, and alternating optimization.

What you can do after

Apply it via Spark to train recommendation models, evaluate RMSE, and produce personalized search boosts.

How it connects

Connects matrix factorization, latent features, collaborative filtering, implicit feedback, personalization, and search ranking.

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Related concepts

• matrix factorizationkind of · Matrix factorization is the decomposition or approximation of a matrix as a product of simpler, often lower-dimensional or structured matrices.
• hyperparameterconfigured by · A hyperparameter is a setting chosen outside ordinary parameter learning that controls a model, algorithm, or learning procedure.
• behavioral datauses · Behavioral data is information recording the observable actions, interactions, responses, or usage patterns of people interacting with a product, service, or system.
• baselinecontrasts with · A baseline is a reference value, model, condition, or performance level used to evaluate or compare measurements, outcomes, or methods.

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