Recommendation system with multi-dimensional discovery experience

US9336546B2 · kind B2 · utility

Assignee

• MICROSOFT TECHNOLOGY LICENSING, LLC · US

Inventors

• Nir Nice · Kfar Vradim, IL
• Noam Koenigstein · Nes Ziona, IL
• Ulrich Paquet · Cambridge, GB
• Shahar Keren · Nes Ziona, IL
• Daniel Sitton · Nes Ziona, IL
• Amit Perelstein · Ramat HaSharon, IL

Key dates

• Filing date: Mar 27, 2014
• Grant date: May 10, 2016
• Expiry date: Oct 23, 2034

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q30/02
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

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.