Microsoft Technology Licensing, LLC

Raytheon Company

Palo Alto Research Center Inc.

Linkedin Corporation

Eric Huang

Shakti Dhirendraji Sinha

David Golland

Patrick A Chase

Alexandre Patry

Rong Zhou

Michael D Howard

Joel Daniel Young

Xu Miao

Deepak Khosla

Lijun Tang

David J Stein

Yitong Zhou

Sumit Rangwala

David L Ii

David Hardtke

Da Teng

Songxiang Gu

Chang-Ming Tsai

Lance M Wall

One embodiment of the present invention provides a system for generating a product recommendation. During operation, the system obtains data indicating vertices and edges of a graph. The vertices represent consumers and products and an edge represents an access relationship. The system may receive a query indicating an ego for determining a product recommendation. The system may then traverse the graph from a vertex representing the ego through a plurality of edges to a plurality of vertices representing products. The system may traverse the graph from the plurality of vertices representing products to a plurality of vertices representing other consumers. The system may then traverse the graph from the plurality of vertices representing other consumers to a plurality of vertices representing other products. The system may generate a recommendation that based on the plurality of vertices representing other products.

System and method for a real-time egocentric collaborative filter on large datasets

A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.

Automated measurement of content quality

A system and method for automatic topic tagging are provided. In example embodiments, input content is received, the content includes a plurality of terms. Term vectors are generated from the plurality of terms. Candidate topics are identified to assigned to the plurality of terms. Topics are assigned to the received content from the identified candidate topics.

Automatic content tagging

The disclosed embodiments provide a system for processing data. During operation, the system obtains a hierarchical representation containing a set of namespaces of a set of features shared by a set of statistical models. Next, the system uses the hierarchical representation to obtain, from one or more execution environments, a subset of the features for use in calculating the derived feature. The system then applies a formula from the hierarchical representation to the subset of the features to produce the derived feature. Finally, the system provides the derived feature for use by one or more of the statistical models.

Common feature protocol for collaborative machine learning

Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

Learning a ranking model using interactions of a user with a jobs list

One embodiment of the present invention provides a system for graph pruning. During operation, the system identifies a connected component in a graph comprising one or more vertices. A respective vertex of the graph represent an element in a data set, an edge between two vertices represents a type and strength of relationship between the vertices. The system identifies a connected component to be smaller than a minimum graph traversal threshold associated with a query for the graph. This minimum graph traversal threshold indicates a minimum number of traversal steps needed for the query. The system then generates a second graph by pruning the connected component from the graph. This second graph is processed to extract information of interest from the data set.

Graph pruning in hipergraph

To improve the scheduling and tasking of resources, the present disclosure describes an improved planning system and method for the allocation and management of resources. The planning system uses a branch and bound approach of tasking resources using a heuristic to expedite arrival at a deterministic solution. For each possible functional mode of the resources, an upper bound is determined. The upper bounds are employed in an objective function for the branch and bound process to determine the functional mode in which to place the resources and to determine movement paths for the resources, all in an environment where a hostile force may attempt to destroy the resources.

System and method for resource allocation and management

A hybrid probabilistic/deterministic planning system including a hybrid planning engine responsive to a null hybrid contingency plan including user defined probabilistic problem including goals, possible effect actions, and the initial state of one or more possible worlds configured to extract a deterministic planning problem therefrom. A deterministic planning engine responsive to the hybrid planning engine configured to generate one or more deterministic partial plans. The d hybrid planning engine converts the one or more deterministic partial plans into one or more hybrid partial plans and splices the one or more hybrid partial plans into selected null hybrid contingency plans to generate one or more hybrid contingency plans for each agent in each of the one or more possible worlds.

Hybrid probabilistic/deterministic planning system and method

Methods and apparatus to provide techniques for planners that records constraints over a set of numeric state variables for each fact and characterizes the set of states in which that fact is true to provide more information to reason about mutual exclusions among sets of states offering a tighter bound on the metric cost of a plan than existing planners currently attain. This more accurate estimate can avoid irrelevant states and decrease search times.

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San Francisco, CA, United States of America

Eric Huang