Your team deploys an ALS matrix-factorization recommender that relies exclusively on historical viewing interactions. A catalog update introduces several new movies with no watch events, and stakeholders notice that these titles never surface in ranked results for any subscriber. To directly mitigate this new-item cold-start issue while keeping the collaborative model in place, which action is most appropriate?
Raise the regularization term in the ALS objective so the model generalizes better to unseen titles.
Augment the recommender with a content-based component that uses item metadata to infer latent factors for the new movies.
Apply SMOTE to oversample interaction rows for the new movies and retrain the model.
Evaluate the system with time-stratified k-fold cross-validation before the next model refresh.
Cold-start for new items arises because collaborative filtering cannot learn latent factors for products that lack interaction data. Injecting rich item metadata (genre, cast, director, synopsis, etc.) into a hybrid or content-boosted model lets the system estimate reasonable factors before any watches occur, so the movies can be ranked alongside existing titles. Merely tightening regularization or increasing factor count does not create information for unseen items-it only adjusts how the model fits known ones. Temporal cross-validation improves evaluation, not recommendation quality. SMOTE generates synthetic class samples for classification tasks; it is inapplicable to sparse, ordinal implicit-feedback matrices and would not yield meaningful rating rows for brand-new items.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
What is a collaborative filtering model in recommender systems?
Open an interactive chat with Bash
What is the cold-start problem in recommender systems?
Open an interactive chat with Bash
What is the role of item metadata in hybrid recommender systems?