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Kudos AI

Recommender Systems

Two fitted offsets that deliver two thirds of the accuracy gain before any latent factor is learned, a model 1.28 times worse for the users who have told it least, and the blind spot that opens when a system only ever sees ratings for what it chose to show.

Intermediate330 XP~1 h90% to advance

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  1. The Baseline That Is Hard to Beat

    25 min · 100 XP

    A rating matrix that is 82% empty, the two offsets that explain most of what is in it, and the latent factors that earn the remaining third of the gain.

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  2. Cold Start and Who the Model Fails

    28 min · 110 XP

    The error is not spread evenly: it is 1.28 times worse for the users with fewest ratings, which is exactly the group a recommender most needs to convince.

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  3. The Loop That Teaches Itself

    30 min · 120 XP

    Only 30% of the catalogue ever appears in anyone’s top ten, with no explicit popularity term in the model at all, and after six rounds of learning from its own recommendations the system is 1.14 times worse exactly where it never looked.

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