Microsoft Technology Licensing, LLC

Aastha Nigam

Aastha Jain

Ankan Saha

Parag Agrawal

Yan Wang

Shaunak Chatterjee

Eric M Lawrence

Ashwin Narasimha Murthy

Ye Tu

Yiou Xiao

Yafei Wang

Bohong Zhao

Albert Lingfeng Cui

Kathryn L Evans

Abdulla Mohammad Al-Qawasmeh

David Sung

Zhanglong Liu

In an example embodiment, a deep learning model is used to learn embedding representations of a heterogeneous information network, where the embedding represents entity-specific properties and network environment properties. Position-aware embeddings specific to the heterogeneous information network may be used as input features of the deep learning model. Furthermore, meta-path embedding specific to the heterogeneous information network may also be used as input features of the deep learning model. Modified embedding propagation methods are further designed to explore better ways to capture network meta-path properties.

Deep representation machine learned model for heterogeneous information networks

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

Operations for facilitating establishment of connections in an online network are disclosed. A set of connection recommendations for a first entity associated with the online network is accessed. For each connection recommendation in the set of connection recommendations, a ranking value associated with the connection recommendation is accessed, a utility value corresponding to the connection recommendation is determined, and an adjusted the ranking value for the connection recommendation is calculated. The utility value is a two-sided utility value that combines a prediction of a utility of the first entity and a prediction of a utility of a second entity with respect to a key performance indicator. A set of connection recommendations is communicated for presentation in an interactive user interface of a client device associated with the first entity in accordance with the adjusted ranking value of each connection recommendation.

Recommending network connections by optimizing for two-sided implicit value of an edge

Systems and methods for engagement mapping based on counterfactual experiments are provided. In example embodiments, a network system receives parameters for one or more counterfactual experiments or tests. Based on the parameters, the network system selects one or more users of a social network platform to subject to the test(s) and selects edges of a social network of each of the one or more users to block. The network system then filters out notifications and feed items from the selected edges of the one or more users. Behavior data of the one or more users based on the filtering out of the notifications and feed items is aggregated, whereby the behavior data indicates engagement of the one or more users on the social networking platform based on the filtering of the notifications and feed items. Recommendations are derived based on the aggregated behavior data and presented to the users.

Network liquidity to engagement mapping

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Sunnyvale, CA, United States of America

Aastha Nigam