Dot Product by Hand
Calculate a simple content-based recommendation score using binary feature vectors and dot products.
A user profile has active features: customer-success, renewal, conversation, short. Score three lessons by overlap: A has customer-success, renewal, conversation, short; B has sales, renewal, negotiation, long; C has finance, spreadsheet, reporting, short. Content-based scoring matches user and item vectors in a shared feature space. The common trap is to treat the score as model wisdom. It is just a function of chosen features, weights, and metadata quality, so a clean number can still reflect a bad taxonomy or missing tag. Step 1 Represent the user vector as four active features: customer-success = 1, renewal = 1, conversation = 1,…
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