Livingsocial, Inc.

David Robert Gentzel

Sourabh Niyogi

Farhad Massoudi

A system and method for optimizing advertisements for display to users comprising receiving a request for an advertisement wherein the request comprises information about a user to whom the advertisement is to be displayed; determining at least one community of which the user is a member; determining a conditional probability that the user will interact with the advertisement; responsive to the conditional probability exceeding a threshold, determining community data to present with the advertisement and providing for display to the user the advertisement and the community data; responsive to the providing of the advertisement, receiving an indication of activity at a client system related to the advertisement or community data; and storing the indication in a storage medium.

Ad targeting and display optimization based on social and community data

Advertisements are generated and selected for display to users, wherein the advertisements include representations of subject individuals. These subject individuals can be friends with whom the user interacts on the Internet and/or any other contributors who may or may not have expertise with regard to the subject matter of the advertisement. A subject individual can be portrayed in an advertisement by including any type of representation of the individual. Ranks for the subject individuals are determined based on the subject individuals' interactions with advertisements and/or on other factors. An advertisement is selected and presented to a user based on a score derived from friends' and/or contributors' interactions with the advertisement. According to various embodiments of the invention, a method is provided for choosing which advertisement(s) to show to a user and which subject individuals to portray in the advertisements.

Generating and presenting targeted advertisements including representations of subject individuals

A server receives an interaction history comprising a set of one-to-one interactions between a first user and other users. The server determines an interaction score for each of the interactions. Each interaction score is based on a type of the respective one-to-one interaction and an age of the respective one-to-one interaction. The server then determines a friend rank for each of the other users based on the interaction history. The friend rank indicates a measure of a degree of influence the other users have over the first user and stores the friend rank for each of the other users. The server then receives, from a client, a request for an advertisement to display to the first user, selects one of the other users having a friend rank satisfying a predetermined threshold, and transmits, to the client, an advertisement portraying the selected additional user.

Ranking interactions between users on the internet

A server receives an interaction history comprising a set of one-to-one interactions between a first user and other users. The server determines an interaction score for each of the interactions, wherein each interaction score is based on a type of the respective one-to-one interaction and an age of the respective one-to-one interaction. The server then determines a friend rank for each of the other users based on the interaction history, wherein the friend rank indicates a measure of a degree of influence the other users have over the first user and stores the friend rank for each of the other users. The server then receives, from a client, a request for an advertisement to display to the first user, selects one of the other users having a friend rank satisfying a predetermined threshold, and transmits, to the client, an advertisement portraying the selected additional user.

Growing community of inventors

Mill Valley, CA, United States of America

David Robert Gentzel