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System and method for personalized content recommendations

  • US 10,521,824 B1
  • Filed: 01/02/2014
  • Issued: 12/31/2019
  • Est. Priority Date: 01/02/2014
  • Status: Active Grant
First Claim
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1. A method comprising:

  • collecting, via a network, user data associated with a plurality of users, wherein the user data comprises information relating to web-based interactions with content by the plurality of users;

    assigning a weight to each of the web-based interactions of the user data in view of an age of the web-based interactions;

    defining a plurality of user cluster types, wherein each user cluster type is associated with one or more interest categories;

    generating, for each of the plurality of users, a vector representation corresponding to each of the plurality of user cluster types, wherein the vector representation comprises a weighted value representing an interest level of a user corresponding to each of the one or more interest categories associated with each user cluster type;

    normalizing the vector representation based on an editorial bias representing a level of consumption of content associated with the one or more interest categories as a function of placement of the content on a webpage;

    determining a portion of the user data matches a first vector representation associated with a first cluster type based at least in part on the weight assigned to the web-based interactions, the first vector representation comprising a first weighted value for a first interest category associated with the first cluster type;

    defining a first cluster comprising a first plurality of users of the plurality of users, wherein the first plurality of users are associated with the first user data;

    generating a plurality of grades for a plurality of content recommendations, wherein a first grade of the plurality of grades is based on one or more user engagement indicators associated with the first plurality of users in the first cluster and a first content recommendation of the plurality of content recommendations;

    identifying, based on a comparison of the plurality of grades, the first content recommendation to provision to the first cluster, wherein the first grade corresponds to a first engagement level of the first cluster as it relates to the first content recommendation;

    loading, into a memory, the first content recommendation in association with the first cluster and a first publisher; and

    in response to a target user included in the first cluster accessing a webpage provided by the first publisher;

    adjusting the first grade associated with the first content recommendation and the first cluster comprising the first plurality of users based on user property data, user-specific action data and non-action data associated with the target user, wherein the user property data comprises a geographic location of the target user, wherein the action data identifies a plurality of web-based activities executed by the target user, and wherein the non-action data identifies passively generated data comprising information associated with the target user viewing a content link with no interaction with the content link; and

    populating, in view of the first grade, a first designated portion of the webpage accessed by the target user with the first content recommendation associated with the first cluster.

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