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A 2022 research paper presenting algorithms for faster MIPS (Maximum Inner Product Search) in high-dimensional spaces. MIPS is crucial for recommendation systems, neural networks, and various machine learning applications.

A theoretical paper establishing the relationship between Maximum Inner Product Search and query-scaled nearest neighbor search. This connection enables applying NN techniques to MIPS problems with theoretical guarantees.

A vector similarity metric that calculates the dot product of two vectors, combining both magnitude and direction. Equivalent to cosine similarity when vectors are normalized, and commonly used for Maximum Inner Product Search (MIPS).

A search problem focused on finding vectors that maximize the inner product with a query vector. Common in recommendation systems and neural search where magnitude carries semantic meaning, requiring specialized algorithms like those in ScaNN.