Implicit Matrix Factorization

You have a user-item matrix from a video app. A 1 means the user watched at least 80% of a video. Blank means no recorded watch. You need to train a collaborative recommender. Matrix factorization learns user and item embeddings, but implicit feedback needs careful weighting of observed and unobserved pairs. The novice move is to convert every blank into a strong zero. That treats non-exposure as dislike and can drown the positive signal, especially for new items or users who have only seen a narrow slice of inventory. Step 1 Keep watched videos as positive observations with meaningful confidence.…

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