US 2016/0259857 A1 · USER RECOMMENDATION USING A MULTI-VIEW DEEP LEARNING FRAMEWORK

Abstract

This disclosure describes systems and method for implementing a multi-view deep learning framework to map users and items to a latent space and determine similarities between users and preferred items. The multi-view deep learning framework can extract features from a domain space based at least in part on having an adequate interaction history to learn relevant user behavior patterns. The deep learning framework may leverage the learned user behavior patterns across multiple domain spaces to provide useful recommendations related to different domain spaces, including domain spaces of which a user has had little or no previous interaction. Example domain spaces include, but are not limited to, search engines, computing device applications, games, informational services, movie services, music services, and reading services.

Claims20

Dependent claims

A method comprising:

receiving user log-in credentials that correspond to a first domain space of a plurality of domain spaces;
identifying a second domain space of the plurality of domain spaces;
identifying a third domain space of the plurality of domain spaces;
extracting, by one or more processors, a first set of feature representations from the second domain space;
extracting, by one or more processors, a second set of feature representations from the third domain space;
determining a similarity between the second domain space and the third domain space based at least in part on the first set of feature representations and the second set of feature representations;
and
providing a recommendation within the first domain space based at least in part on the similarity.

5 more elements

A system comprising:

one or more processors;
a computer readable medium coupled to the one or more processors, including one or more modules that are executable by the one or more processors to:
receive user log-in credentials that correspond to a first domain space of a plurality of domain spaces;
identify a second domain space of the plurality of domain spaces;
extract, from the second domain space, a first set of feature representations having a first feature vector length;
identify at least one additional domain space other than the first domain space or the second domain space;
extract, from the at least one additional domain space, a second set of feature representations having a second feature vector length;
determine at least one viewing pair that corresponds to the second domain space and the at least one additional domain space;
determine a similarity that corresponds to the at least one viewing pair based at least in part on the first feature vector length and the second feature vector length;
and
determine a ranking of the second set of feature representations that correspond to the at least one additional domain space relative to the second domain space.

8 more elements

A computer storage medium having computer-executable instructions thereon, that upon execution, configure a device to perform operations comprising:

identifying a first domain space of a plurality of domain spaces;
identifying a second domain space other than the first domain space;
determining a first viewing pair comprising the first domain space and the second domain space;
ranking feature representations of the second domain space relative to the first domain space based at least in part on a cosine similarity of feature vectors that correspond to the second domain space and the first domain space;
determining a first convergence error rate associated with the ranking of the feature representations of the second domain space relative to the first domain space;
in response to determining that the first convergence error rate is greater than a predetermined error-rate threshold, identifying a third domain space from the plurality of domain spaces, the predetermined error-rate threshold corresponding to an upper limit of a convergence error rate that indicates convergence;
determining a second viewing pair comprising the first domain space and the third domain space;
ranking feature representations of the third domain space relative to the first domain space based at least in part on a cosine similarity of feature vectors that correspond to the third domain space and the first domain space;
and
determining a second convergence error rate associated with the ranking of the feature representations of the third domain space relative to the first domain space.

7 more elements

Dependencies are inferred from the claim text.

User recommendation using a multi-view deep learning framework

Life of this patent

User recommendation using a multi-view deep learning framework

Life of this patent

Abstract

Claims20