Outbrain Inc.

Ido Tamir

Roy Bass

Yaniv Gilad

Daniel Sternlicht

Colin Doody

Yaron Galai

Ronny Lempel

Sonya Liberman

Sagiv Gurevitch

Danny Rosenstein

Elad Bar

Ilan Orlov

Carmel Bregman

A content recommendation method and system are described, according to various implementation. In an implementation, the method and system generate a record identifying a set of content items interacted with by users of a content system, where each content item is associated with one or more topics. A confidence score associated with each of a set of topics associated with the set of content items is generated, where the confidence score represents a quantity of instances a respective topic appears in a collection of content items. A portion of the set of topics having a confidence score that exceeds a threshold confidence level is determined. A search of the collection of content items is executed to identify a serendipitous candidate content item that includes multiple portions of the set of topics previously interacted with by a user. The method and system determines, based on the record, that the candidate serendipitous content item has not been interacted with by the user system. The method and system generates a plurality of content zones having a depth defined based on user engagement measurements, wherein the plurality of content zones includes a targeted content zone including the candidate serendipitous content item.

Provisioning serendipitous content recommendations in a targeted content zone

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

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Hod Hasharon, Israel

Ido Tamir