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Candidate generation produces a manageable set of possible answers before a more expensive scoring or verification stage. In a recommender system, retrieving a few hundred items from millions lets a detailed ranking model examine each candidate. Candidates may come from several retrieval rules; their union can combine recent, popular and personally relevant items.^[ref]

One mechanism represents a query by a vector $q$ and each item by a vector $v_i$, then retrieves large inner products $q^T{v}_i$ using nearest neighbor search. If the vectors have unit Euclidean norm, maximizing $q^T{v}_i$ also minimizes $\Vert q-v_i{\Vert }^2=2-2q^T{v}_i$. Without normalization those rankings need not agree. A candidate omitted here cannot be restored by a later stage that only reorders the retrieved set, so retrieval coverage and computational cost must be assessed together.

Peter Norvig is a computer scientist whose work combines artificial intelligence, computer programming and technical education. With Stuart J. Russell, he wrote Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, which connects search algorithms, logical reasoning, probabilistic reasoning and machine learning through the design of intelligent agents.^[ref]

His programming explanations also connect compact working examples with general ideas: his spelling-correction tutorial illustrates how candidate generation and a probability model can cooperate in selecting a correction.^[ref]