Yahoo! Inc.

Verizon Media Inc.

Raghu Ramakrishnan

Deepak Agarwal

Andrew Stephen Tomkins

Shanmugasundaram Ravikumar

Seung-Taek Park

Utkarsh Srivastava

Todd Beaupre

Brian Frank Cooper

Philip Lewis Bohannon

Bee-Chung Chen

Cameron A Marlow

Scott Roy

Ramana Yerneni

Wei Chu

Pradheep K Elango

Prabhakar Raghavan

Erik Nathan Vee

Daniel E Weaver

John Thrall

Andrew Feng

Sathiya Keerthi Selvaraj

Arun Sundaresan Iyer

Adam Silberstein

Amitabh Seth

Nitin Motgi

Michael David Bigby

Howard Scott Roy

Vik Singh

David Mark Ciemiewicz

Vijay Krishna Narayanan

Nilesh Dalvi

Vinay Kakade

Bryan Call

Arup Kumar Choudhury

Vipul Agarwal

Sharat Israni

Chandar Venkataraman

Amit Phadke

Michael Salisbury

David Lomax

Mani Abrol

Alok S Kirpal

Joe Zachariah

A social environment is provided by creating an object in response to recognition of an entity in a portion of web content, wherein the object represents the entity, the object is associated with a type selected from a set of types, and the type is associated with a schema selected from a set of schemas, where the social environment includes a set of objects including the object, wherein the objects are instances of corresponding types in a rich system of predefined types, the schemas are associated with the types, metadata is associated with the objects, and there is at least one relationship between at least two objects selected from the set of objects, where the set of objects and the metadata are extensible, such that extensions provided by a first user are available for use by a second user. In one example, metadata provided by a first user is only available to a second user having a relationship with the first user.

Creating secure social applications with extensible types

A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined. Based on user features of a user and items a user has consumed, a set of nearest neighbor items are identified as a set of candidate items, and affinity scores of candidate items are determined. Based on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

Determining user preference of items based on user ratings and user features

In a large-scale transaction such as the bulk loading of new records into an ordered, distributed database, a transaction limit such as an insert limit may be chosen, partitions on overfull storage servers may be designated to be moved to underfull storage servers, and the move assignments may be based, at least in part on the degree to which a storage server is underfull and the move and insertion costs of the partitions to be moved.

System and/or method for bulk loading of records into an ordered distributed database

The subject matter disclosed herein relates to a process for receiving, evaluating and selecting of content modules such as content summary boxes and landing pages for display on network-accessible search engine results page. In one particular example, potential content providers may be provided with incentives and guidelines for the preparation of content summary boxes and landing pages over an electronic communications network, and the resulting submissions can be ranked and published in conjunction with the display of a search engine results page over the network.

System and/or method for obtaining of user generated content boxes

Techniques are provided for the efficient location, processing, and retrieval of local product information derived from web pages generally locatable through form queries submitted to web pages often referred to as the 'deep' or 'hidden' web. In an embodiment, information such as product information and dealer-location information is located on a web page form such as a dealer-locator form. After location of a suitable web page form, editorial wrapping is performed to create an automated information extraction process. Using the automated information extractor, deep-web crawling is performed. A grid-based extraction of individual business records is performed, and matching and ingestion are performed in conjunction with a business listing database. Finally, metadata tags are added to entries in the business listing database. Metadata tags also may be added to entries in other databases.

Method and system for form-filling crawl and associating rich keywords

Content display policies are evaluated using two kinds of methods. In the first kind of method, using information, collected in a 'controlled' manner about user characteristics and content characteristics, truth models are generated. A simulator replays users' visits to the portal web page and simulates their interactions with content items on the page based on the truth models. Various metrics are used to compare different content item-selecting algorithms. In the second kind of method, no explicit truth models are built. Events from the controlled serving scheme are replayed in part or whole; content item-selection algorithms learn using the observed user activities. Metrics that measure the overall predictive error are used to compare different content-item selection algorithms. The data collected in a controlled fashion plays a key role in both the methods.

Framework to evaluate content display policies

A set of item-item affinities for a plurality of items is determined based on collaborative-filtering techniques. A set of an item's nearest neighbor items based on the set of item-item affinities is determined. A set of user feature-item affinities for the plurality of items and a set of user features is determined based on least squared regression. A set of a user feature's nearest neighbor items is determined based in part on the set of user feature-item affinities. Compatible affinity weights for nearest neighbor items of each item and each user feature are determined and stored. Based on user features of a particular user and items a particular user has consumed, a set of nearest neighbor items comprising nearest neighbor items for user features of the user and items the user has consumed are identified as a set of candidate items, and affinity scores of candidate items are determined. Based at least in part on the affinity scores, a candidate item from the set of candidate items is recommended to the user.

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

Methods and apparatuses are provided for dynamically reorganizing the data within a replicated database system. One method, for example, includes performing a split operation across a plurality of replicated databases with regard to an existing partition therein, wherein the existing partition comprises a plurality of data records and the two new partitions each include at least a portion of the plurality of data records, and allowing at least one type of access to the plurality of data records during the split operation.

Dynamic data reorganization to accommodate growth across replicated databases

Growing community of inventors

Los Altos, CA, United States of America

Raghu Ramakrishnan