Microsoft Technology Licensing, LLC

Google Inc.

Samuel Ieong

Rakesh Agrawal

Sreenivas Gollapudi

Ariel Fuxman

Raja Palani Velu

Alexandros Ntoulas

Anitha Kannan

Yifang Liu

Christopher Kenneth Harris

Roberto Javier Bayardo

Stelios Paparizos

Aranyak Mehta

John Christopher Shafer

Konstantinos Katsiapis

Krishnaram N G Kenthapadi

Xi Tan

Gabriel Florin Constantin

Shuai Ding

Systems and methods of a balancing data requests over a computer network are provided. The system can receive a request for content to be rendered in a content slot on a computing device. The content slot can be coded with a default timer value. The system can execute a real-time content selection process to identify content items that each have a magnitude. The system can determine a timer value based on the magnitudes, and transmit the timer value to the computing device to override the default timer value to cause the computing device to replace a first content item rendered in the content slot upon expiration of the timer.

Balancing data requests over a network

Multi-class classification by training a machine learning system based on training inputs each of which includes features and at least one class label. Each training input is assigned a membership value that can indicate if an entity having the features of the training input is a member of the class corresponding to the class label that is also included in the training input. To determine if an entity having test features is a member of several test classes, test inputs can be constructed where each input includes the test features and a class label corresponding to one of the test classes. The test inputs are processed by the trained machine learning system, which produces as outputs test membership values that represent the likelihood that the entity having the features in the test input belong to the test class corresponding to the test class label also included in the test input.

Classification system

Search history data such as browse trails are collected over time. The browse trails, including associated queries and domains, are processed to identify free tokens of the queries that are also modifiers. Attribute value pairs of a structured data source that correspond to the modifiers are determined based on the search history data and a frequency of the attribute value pairs in the structured data source. When a subsequent query is received, modifiers in the query are identified and replaced with the determined combinations of attribute value pairs that correspond to the modifiers in a structured query that is generated from the received query. The structured query is used to identify items and/or services in the structured data source that are responsive to the received query.

Reformulating query terms in structured search

A structured query may specify attribute values for attributes. An estimate of the number of items that will match the structured query if it is applied to a structured database is determined. If the estimated number of items is below a threshold, the structured query may be relaxed to form new candidate structured queries. The number of candidate queries may be determined based on a desired running time. Each of the candidate structured queries may be determined by changing one or more attribute values of the attributes of the structured query. Estimates of the number of items each of the candidate structured queries will match is determined, and the candidate structured query that has the highest matching estimation is used to query the database. The matching results may be output.

Relaxation for structured queries

Documents are replicated among servers comprising a search engine based on the value of each document by approximating its value as one of the top search results for one or more exemplary queries. Documents are allocated among servers comprising a search engine by calculating a relevance value for each document and then distributing the documents evenly to the servers. A subset of servers are selected from among a plurality of servers comprising a search engine using term-based, server-specific histograms reflecting the number of instances of the term in each document allocated to each server, and then selecting servers to service a query based on the documents on those servers.

Indexing for limited search server availability

A method, system, and medium are provided that are directed to providing a user with time-sensitive information that is usable to determine when to purchase a product. In accordance with embodiments of the technology, exemplary steps include using historical product information to generate time-sensitive information. Moreover, in response to receiving from a user a request to receive information describing a given product, time-sensitive information is caused to be presented. For example, time-sensitive information might be usable by the user to determine when to purchase the given product and an alternative product.

Providing time-sensitive information for purchase determinations

Methods, apparatus, and systems directed to receiving search queries, retrieving documents, computing the number of categories to present for a given query, computing the number of results to show in each category, computing an ordering of categories, and for all the result pages beyond the first page employing user interface elements that optionally allow the user to quickly zoom in on a specific category and get more results belonging to that category.

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Mountain View, CA, United States of America

Samuel Ieong