US 2019/0236680 A1 · Systems and Methods for Providing Personalized Online Content

Abstract

A system for providing online-content includes a data-acquisition module configured to collect user data and product data. A configuration module determines a predictive algorithm and one or more filters based on received marketer-input. A recommendation engine generates recommendations in real-time, in response to a request. The recommendations are generated via the predictive algorithm, which generates scalar scores from the dot-product multiplication of at least one user-vector and at least one product-vector, the scalar score indicating the likelihood that a desired interaction will occur between the user and the product. The algorithm also determines a hierarchical list based on the scalar scores, and applies the filters to the hierarchical list so as to identify the recommendations. The recommendation engine can generate recommendations in two ways: recommend items based on users, and recommend users based on items. A rendering module generates a recommendation block based on the recommendations, which is transmitted to a user-computer in response to the request.

Claims19

Dependent claims

A system for providing personalized online-content, comprising:

a data-acquisition module configured to collect user data and product data;
a configuration module configured to determine a predictive algorithm and one or more filters based on received marketer-input;
a recommendation engine configured to generate one or more recommendations in real-time, in response to a request, wherein the recommendations are generated via the predictive algorithm, which includes the steps of:
generating scalar scores from the dot-product multiplication of at least one user-vector and at least one product-vector, the user-vector being generated from the user data, the product-vector being generated from the product data, and the scalar score indicating the likelihood that a desired interaction will occur between the at least one user and the at least one product,
determining a hierarchical list based on the scalar scores, and
applying the one or more filters to the hierarchical list so as to identify the one or more recommendations;
a rendering module configured to generate a recommendation block based on the one or more recommendations;
and
a network interface configured to transmit the generated recommendation block to a user-computer in response to the request.

6 more elements

A method for providing personalized online-content, the method comprising:

collecting user data and product data;
determine a predictive algorithm and one or more filters based on received marketer-input;
receiving a request for one or more recommendations;
generating one or more recommendations in real-time, in response to the request, wherein the one or more recommendations are generated via the predictive algorithm, which includes the steps of:
generating scalar scores from the dot-product multiplication of at least one user-vector and at least one product-vector, the user-vector being generated from the user data, the product-vector being generated from the product data, and the scalar score indicating the likelihood that a desired interaction will occur between the at least one user and the at least one product,
determining a hierarchical list based on the scalar scores, and
applying the one or more filters to the hierarchical list so as to identify the one or more recommendations;
generating a recommendation block based on the one or more recommendations;
and
transmitting the generated recommendation block to a user-computer in response to the request.

7 more elements

A non-transitory computer readable medium having instructions stored thereon the execution of which by a computer causes the computer to execute a method for providing personalized online-content, the method comprising:

collecting user data and product data;
determining a predictive algorithm and one or more filters based on received marketer-input;
receiving a request for one or more recommendations;
generating one or more recommendations in real-time, in response to the request, wherein the one or more recommendations are generated via the predictive algorithm, which includes the steps of:
generating scalar scores from the dot-product multiplication of at least one user-vector and at least one product-vector, the user-vector being generated from the user data, the product-vector being generated from the product data, and the scalar score indicating the likelihood that a desired interaction will occur between the at least one user and the at least one product,
determining a hierarchical list based on the scalar scores, and
applying the one or more filters to the hierarchical list so as to identify the one or more recommendations;
generating a recommendation block based on the one or more recommendations;
and
transmitting the generated recommendation block to a user-computer in response to the request.

7 more elements

Dependencies are inferred from the claim text.

Systems and Methods for Providing Personalized Online Content

Life of this patent

Systems and Methods for Providing Personalized Online Content

Life of this patent

Abstract

Claims19