Recommendation system with multi-dimensional discovery experience

摘要

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.

主权项

1. An apparatus, comprising:
a processor; a memory that stores data associated with making a recommendation to a user of an item to acquire; a set of logics that produce a navigable data set that supports an interactive recommendation experience for the user; and an interface to connect the processor, the memory, and the set of logics; the set of logics comprising:
a first logic that accesses a Q-dimensional latent space that embeds users and items;a second logic that extracts a full distance matrix from the Q-dimensional latent space; anda third logic that produces a plurality of vectors associated with a P-dimensional Euclidean space from the full distance matrix, where the navigable data set is based, at least in part, on the plurality of vectors, P being less than or equal to Q.