Microsoft Technology Licensing, LLC

Yafei Wang

Aastha Jain

Parag Agrawal

Ashwin Narasimha Murthy

Yan Wang

Ankan Saha

Eric M Lawrence

Myunghwan Kim

Aastha Nigam

Yiou Xiao

Matthew Hsing Hung Walker

Bohong Zhao

Albert Lingfeng Cui

Florent Bekerman

Abdulla Mohammad Al-Qawasmeh

David Sung

In an example embodiment, a single machine learned model that allows for ranking of entities across all of the different combinations of node types and edge types is provided. The solution calibrates the scores from Edge-FPR models to a single scale. Additionally, the solution may utilize a per-edge type multiplicative factor dictated by the true importance of an edge type, which is learned through a counterfactual experimentation process. The solution may additionally optimize on a single, common downstream metric, specifically downstream interactions that can be compared against each other across all combinations of node types and edge types.

Recommending edges via importance aware machine learned model

Techniques for generating latent representations for entities based on a network graph are provided. In one technique, an artificial neural network is trained based on feature vectors of entities and feature vectors of neighbors of those entities. The neighbors are determined based on a graph of nodes representing the entities. Two nodes are connected if, for example, the corresponding entities are connected in an online network, one entity transmitted an online communication to the other entity, or one entity interacted with content associated with the other entity. Once trained, the artificial neural network is used to generate latent representations for entities represented by the graph. Latent representations may be used in multiple ways. For example, a similarity between two latent representations may be used to determine an order of candidate content items to present to an entity corresponding to one of the latent representations.

Latent feature extraction from a network graph

Technologies for scoring and ranking cohorts containing content items using a machine-learned model are provided. The disclosed techniques include a cross-cohort optimization system that stores, within memory, cohort definition criteria for each cohort of a plurality of cohorts. The optimization system, for a particular user, for each cohort, identifies a plurality of content items that belong to the specific cohort based upon the cohort definition criteria. Using a machine-learned model, the optimization system generates a score for the specific cohort with respect to the particular user's intentions. The optimization system generates a ranking for the plurality of cohorts based on the respective scores of each cohort. The optimization system causes the plurality of content items of each cohort to be displayed concurrently on a computing device of the particular user. Display order for the plurality of cohorts is based on the ranking determined for the plurality of cohorts.

Intent based second pass ranker for ranking aggregates

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Sunnyvale, CA, United States of America

Yafei Wang