Microsoft Technology Licensing, LLC

Bohong Zhao

Aastha Jain

Parag Agrawal

Yan Wang

Ankan Saha

Eric M Lawrence

Ashwin Narasimha Murthy

Andrew S Yu

Michael Maczka

Aastha Nigam

Yafei Wang

Albert Lingfeng Cui

Peter Chng

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 and leveraging heterogenous edges in an online connection network are provided. In one technique, a particular user is identified. The identification may be made in response to a computing device of the particular user requesting data from a particular system. For each entity type of multiple entity types: (1) a set of entities of the entity type is identified based on one or more attributes of the particular user; (2) a ranking of the set of entities is generated based on one or more criteria; and (3) a subset of the set of entities is selected and included in a final set of entities. The final set of entities includes entities from different entity types of the multiple entity types. The final set of entities is transmitted over a computer network to be presented concurrently on a computing device of the particular user.

Heterogenous edges in an online network for building active online communities

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San Jose, CA, United States of America

Bohong Zhao