Microsoft Technology Licensing, LLC

Linkedin Corporation

Lei Ni

Qi Guo

Xianren Wu

Erik Eugene Buchanan

Shan Zhou

Bo Hu

Annabel Fang Liu

Pierre Yannick Monestie

Prasad Kamalakar Gune

Lin Yang

Qi Liu

Rahul D Sule

Sridevi Kulasekaran

An indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. A plurality of user profiles in the social networking service is accessed. A first machine-learned model is used to learn embeddings for the plurality of different entities in a d-dimensional space. A second machine-learned model is used to learn an embedding for each of one or more query terms that are not contained in the indication of the plurality of different entities in the social networking service, using the embeddings for the plurality of different entities learned using the first machine-learned model, the second-machine learned model being a deep structured semantic model (DSSM). A similarity score between a query term and an entity is calculated by computing distance between the embedding for the query term and the embedding for the entity in the d-dimensional space.

Joint representation learning of standardized entities and queries

In an example, an indication of a plurality of different entities in a social networking service is received, including at least two entities having a different entity type. Then a plurality of user profiles in the social networking service are accessed. A machine-learned model is then used to calculate, based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred, a similarity score between a first node and second node by computing distance between the first node and the second node in a d-dimensional space on which a plurality of entities are mapped, the similarity score generated using a generalized linear mixed model having a global coefficient vector applied to global function pertaining to the co-occurrence counts and a first random effects coefficient vector applied to a random effects per-country function.

Smart suggestions personalization with GLMix

In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure having a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d&#xac;-dimensional space.

Standardized entity representation learning for smart suggestions

In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Personalized deep models for smart suggestions ranking

A method of sharing recruiting data between business units of an organization is disclosed. It is determined that a first business unit has a contract to use a first set of resources of a social-networking system to manage a first set of profiles. It is determined that a second business unit has a contract to use a second set of resources to manage a second set of profiles. It is determined that the first entity used the first set of resources to create a private data item and to associate the private data item with a profile of the first set of profiles. It is determined that the profile of the first set of profiles and a profile of the second set of profiles correspond to the same person. The second entity is provided with access to the private data item, but not control of the first set of resources.

Sharing recruiting data across business units of an organization

In order to prevent unauthorized access to information, a system may analyze and may selectively store the information provided based on requests from users that are not unauthorized to access the information. In particular, the system may receive a request for information associated with a document (such as a web page) from an authorized user, either in real-time (i.e., during live or online operation of the system) or offline (in which case the system may operate as a proxy for a live feed of requests). In response, the system may provide or replay the request, but may include the credentials of an unauthorized user. Then, the system may analyze the response to the request to determine if the response is substantive (i.e., includes information). If yes, the system may store the response for use in subsequent analysis and to guide remedial action.

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Belmont, CA, United States of America

Lei Ni