Recommendation system with multi-dimensional discovery experience

Abstract

Example apparatus and methods perform matrix factorization (MF) on a collaborative filter based usage matrix to create a multi-dimensional latent space that embeds users, items, and features. A full distance matrix is extracted from the latent space. The full distance matrix may be extracted from the latent space by defining a distance metric between item pairs based on the multi-dimensional representation in the latent space. The full distance matrix may be populated with values computed for item pairs using the distance metric. A plurality of vectors associated with a multi-dimensional Euclidean space are produced from the full distance matrix. The plurality of vectors produce a navigable data set. The plurality of vectors may be produced in a manner that minimizes strain on the distances vectors. A representation of the navigable data set may be presented as, for example, a virtually traversable landscape that supports an interactive user experience.