The Nielsen Comapny (us), LLC

Yahoo! Inc.

The Nielson Company (us), LLC

Amitabh Seth

Brahmanand Reddy Shivampet

Shu-Yao Chien

George H John

Deepak Agarwal

John Thrall

Bee-Chung Chen

Nitin Motgi

Raghu Ramakrishnan

Pradheep K Elango

Howard Scott Roy

Vik Singh

Vijay Krishna Narayanan

Supratim Roy Chaudhury

Sharat Israni

Chandar Venkataraman

Amit Phadke

Michael Salisbury

Nikolai Rochnik

Christopher L Hogan

Joe Zachariah

An example method includes accessing a network communication at a database proprietor server, the network communication including one or more audience measurement entity cookie identifiers associated with one or more audience measurement entity cookies, the one or more audience measurement entity cookies from an audience measurement entity server and stored in a client device; generating one or more cookie mappings of the audience measurement entity cookies to respective one or more database proprietor cookies, the one or more database proprietor cookies from the database proprietor server and stored in the client device; and causing transmission of one or more re-direct messages to cause the client device to send the one or more cookie mappings in a batch to the audience measurement entity server, a re-direct message of the one or more re-direct messages including a first database proprietor cookie identifier and a first audience measurement entity cookie identifier.

Methods and apparatus to share online media impressions data

Methods and apparatus to collect impressions using media object characteristics are disclosed. An example apparatus includes an impressions monitor to receive a communication from a collector media object presented at a client device. The communication includes a first characteristic corresponding to the collector media object, and second characteristics respectively corresponding to a plurality of second media objects concurrently presented with the collector media object. The example apparatus further includes an attributor to associate demographic information with the first and second characteristics based on the first and second characteristics being received in the same communication from the client device.

Methods and apparatus to measure media using media object characteristics

Methods and apparatus to share online media impression data are disclosed. An example apparatus includes a communications interface; and a web browser to send a first request to an audience measurement entity via the communications interface; and send a second request via the communications interface to cause a database proprietor to send to the audience measurement entity a cookie mapping of an audience measurement entity cookie to a database proprietor cookie corresponding to a client.

Methods and apparatus to collect impressions using media object characteristics are disclosed. A disclosed example method involves, based on first instructions associated with a collector media object presented at a client device, collecting a first characteristic of the collector media object and collecting second characteristics corresponding to a plurality of second media objects that are presented at the client device concurrently with the collector media object. The example method also involves, based on second instructions associated with the collector media object, reporting multiple impressions corresponding to the collector media object and to the second media objects by sending the first and second characteristics and an identifier associated with the client device in a single communication to an audience measurement entity.

Methods and apparatus to share online media impression data are disclosed. An example apparatus includes a communications interface; and a processor to generate instructions to be included in a web site and to cause the communications interface to provide the instructions to a web server associated with the web site, the instructions to cause a client, upon execution of the instructions, to initiate a process including sending a first request to an audience measurement entity; and sending a second request to cause a database proprietor to send to the audience measurement entity a cookie mapping of an audience measurement entity cookie to a database proprietor cookie corresponding to the client.

Methods and apparatus to share online media impression data are disclosed. An example method includes receiving, at a database proprietor, a first request from a client device, the first request including an audience measurement entity cookie identifier of an audience measurement entity cookie; and determining, with a processor of the database proprietor, a cookie mapping of the audience measurement entity cookie to a database proprietor cookie that is assigned by the database proprietor and associated with the client device.

Methods and apparatus to share online media impression data are disclosed. An example method includes sending a response to a request, the response including an identification of a first cookie used by an audience measurement entity and an indication of a database proprietor, and receiving a mapping of the first cookie to a second cookie used by the database proprietor and demographic information associated with the second cookie by the database proprietor.

Embodiments of the present invention are directed to identifying a source of bias in a sample set and using statistical techniques to overcome the bias, thereby producing extrapolated data that accurately represents a population including the sample set. The method according to one embodiment comprises identifying a plurality of attributes corresponding to an entity, generating a plurality of clusters from the plurality of attributes, the plurality of clusters including varying numbers of the plurality of attributes, and performing measurements on each of the plurality of clusters to generate measurement data values for each of the plurality of clusters. A cluster with a greatest measurement data value is selected.

System and method for extrapolating data from a sample set

A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.

Customized today module

A method selects a predictor item that has a relevance to a user. The method receives a set of affinity items having affinity scores that relate the predictor item to the affinity items. The method filters the list of affinity items based on the affinity scores, and selects a first set of affinity items from the filtered items. For each selected affinity item, the method calculates a difference score from the predictor item, and selects a first affinity item based on the difference score for the first affinity item. Preferably, content is presented to the user based on the selected first affinity item. Additional embodiments include a system and/or computer readable medium having instructions for execution of the foregoing.

Engagement-oriented recommendation principle

A method of determining content relevance for a user sets a user preference, which is related to a first area of content. The method calculates a set of scores, by using a combination, of the user preference, affinity data, and a parametric weight. The method organizes the content by using the set of scores, such that the organization of the content has a desirable relationship to the user, and recommends the selected content. Preferably, the method precomputes the affinity data and/or the parametric weight to generate and store the precompiled data for later retrieval. The affinity data describes a relationship between a first item of content and a second item of content, and the parametric weight describes an attribute of the second item. Additional embodiments include a system implementation and computer readable medium.

User interactive precision targeting principle

In an embodiment, new traffic pattern data is received pertaining to a first time period having a first time increment. The traffic pattern data may be received as, or converted to, count information reflecting the probabilities that the user will select particular links services. An incremental table is accessed to determine stale traffic pattern count data, which is the traffic pattern count data in the incremental table over a second time period having the first time increment, wherein the second time period covers the oldest traffic pattern count data in the incremental table. Then a count table is updated to reflect removal of the stale traffic pattern count data, wherein the count table contains global count data at a higher level of granularity than the incremental table. The incremental table and count table are updated to reflect addition of the new traffic pattern data.

Growing community of inventors

Saratoga, CA, United States of America

Amitabh Seth