Outbrain Inc.

Colin Doody

Roy Bass

Daniel Sternlicht

Yaniv Gilad

Savitha Thankam Pal

Yaron Galai

Ido Tamir

Ronny Lempel

Sonya Liberman

Sagiv Gurevitch

Danny Rosenstein

Elad Bar

Ilan Orlov

Carmel Bregman

A personalized content recommendation provisioning method and system are described, according to various implementations. A token value allocation engine optimizes the allocation of content consumption revenue to publisher systems and recommendation source systems (e.g., the systems that provide the recommended content for presentation with the publisher system's native content) based on a model calculating and applying a life time or long term value (LTV) metric. The LTV metric for revenue is based on a detection of a how often a user system engages with content derived from or associated with a publisher system.

User lifetime revenue allocation associated with provisioned content recommendations

A personalized content recommendation provisioning method and system are described, according to various implementations. In an implementation, a user profile is established for each of multiple users within an electronic data environment (e.g., the Internet). The user profile may include information collected via a registration of the user and information identifying content consumed by the user based at least in part on information collected by an associated tracking cookie. The user profile may be used to generate a personalized grade associated with each of multiple candidate content recommendations in a high-quality candidate content recommendation pool, determine a content recommendation scope associated with a user in view of a personalized grade associated with each of the plurality of candidate content recommendations, and display the content recommendation scope to the user.

Provisioning personalized content recommendations

Display screen or portion thereof with graphical user interface

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New York, NY, United States of America

Colin Doody