Microsoft Technology Licensing, LLC

Nadeem Anjum

Benjamin John McCann

Skylar Payne

Jerry Lin

Gio Borje

Apoorv Khandelwal

David DiCato

Erik Eugene Buchanan

Nagaraj Kota

Mohit Wadhwa

Venkatesh Duppada

In an example embodiment an approximate nearest neighbor framework is provided to query user activity data to find users who are similar to users who have been 'matched' to a particular piece of content but who otherwise would not have been matched on their own. The users who have been matched may be called a seed set of users, which are known in real-time, or near-real-time. Use of the approximate nearest neighbor framework allows the system to expand instantly the initial seed set of users to other similar users to rapidly distribute relevant pieces of content to active users, increasing liquidity of the system. Additionally, the target set of specific users to which a notification is sent about the pieces of content can also be expanded, increasing the recall rate.

Instant content notification with user similarity

Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.

Predicting feature values in a matrix

Negative sampling

Systems and methods for query term weighting are disclosed. A server receives a search query for employment candidates, the search query comprising a set of parameters, each parameter having a weight. The server generates, from a data repository storing records associated with professionals, a first set of search results based on the set of parameters and the weights of the parameters in the set. The server transmits the first set of search results. The server receives a response to search result(s) from the first set of search results, the search result(s) being associated with a set of factors, the response indicating a level of interest in the search result(s). The server adjusts the parameters in the set of parameters or adjusts the weights of the parameters based on the response to the search result(s). The server provides an output based on the adjusted parameters or the adjusted weights.

Query term weighting

Ranking job candidate search results

Machine learning to predict numerical outcomes in a matrix-defined problem space

Growing community of inventors

Santa Clara, CA, United States of America

Nadeem Anjum