US 2020/0005196 A1 · PERSONALIZATION ENHANCED RECOMMENDATION MODELS

Abstract

Methods, systems, apparatuses, and computer program products are provided for a two-phase technique for generating content recommendations. In a first phase, a baseline recommender is configured to generate a baseline content recommendation using one or more content recommendation models, such as a Smart Adaptive Recommendations (SAR) model, Factorization Machine (FM) or Matrix Factorization (MF) models, collaborative filtering models, and/or any other machine-learning models or techniques. In a second phase, a personalized recommender implements a vector combiner configured to combine profile vectors, content vectors, and the baseline content recommendations to generate combined user vectors. A model generator may train a machine-learning model using the combined user vectors and training data comprising actual interaction behavior of the users, which may be then applied to identify a content recommendation for a particular user.

Claims20

Dependent claims

A system for generating a machine-learning model for providing a content recommendation, the system comprising:

at least one processor;
and
memory that stores program code configured to be executed by the at least one processor, the program code comprising:
a baseline recommender configured to generate baseline content recommendations using historical user-content interactions by a plurality of users;
a personalized recommender comprising:
a profile vector generator configured to generate profile vectors corresponding to the users based on user profile information of the users;
a content vector generator configured to generate content vectors corresponding to the users based on content interaction data of the users;
a vector combiner configured to generate user vectors for the users, each user vector generated for a user including a baseline content recommendation of the baseline content recommendations, a profile vector of the profile vectors, and a content vector of the content vectors corresponding to the user;
and
a model generator configured to:
retrieve interaction training data corresponding to tracked interactions, by the users, with content of a plurality of content types;
and
generate a recommendation model using a supervised machine-learning algorithm that receives the interaction training data and user vectors as inputs, the recommendation model configured to identify content for recommendation to users.

10 more elements

A method in a computing device for generating a machine-learning model for providing a content recommendation, the method comprising:

generating baseline content recommendations using historical user-content interactions by a plurality of users;
generating profile vectors corresponding to the users based on user profile information of the users;
generating content vectors corresponding to the users based on content interaction data of the users;
generating combined user vectors for the users, each user vector generated for a user including a baseline content recommendation of the baseline content recommendations, a profile vector of the profile vectors, and a content vector of the content vectors corresponding to the user;
retrieving interaction training data corresponding to tracked interactions, by the users, with content of a plurality of content types;
and
generating a recommendation model using a supervised machine-learning algorithm that receives the interaction training data and user vectors as inputs, the recommendation model configured to identify content for recommendation to users.

4 more elements

A computer-readable memory having computer program code recorded thereon that when executed by at least one processor causes the at least one processor to perform a method comprising:

generating baseline content recommendations using historical user-content interactions by a plurality of users;
generating profile vectors corresponding to the users based on user profile information of the users;
generating content vectors corresponding to the users based on content interaction data of the users;
generating combined user vectors for the users, each user vector generated for a user including a baseline content recommendation of the baseline content recommendations, a profile vector of the profile vectors, and a content vector of the content vectors corresponding to the user;
retrieving interaction training data corresponding to tracked interactions, by the users, with content of a plurality of content types;
and
generating a recommendation model using a supervised machine-learning algorithm that receives the interaction training data and user vectors as inputs, the recommendation model configured to identify content for recommendation to users.

4 more elements

Dependencies are inferred from the claim text.

Personalization enhanced recommendation models

Life of this patent

Personalization enhanced recommendation models

Life of this patent

Abstract

Claims20