Microsoft Technology Licensing, LLC

Shan Zhou

Xianren Wu

Qi Guo

Bo Hu

Erik Eugene Buchanan

Lei Ni

Anish Ramdas Nair

Rahim Daya

Jordan Anthony Saints

Timothy Patrick Jordt

Gregory Alan Walloch

Zachary Tyler Piepmeyer

Zian Yu

Abhishek Gupta

Shakti Dhirendraji Sinha

Keqing Liang

Satya Kanduri

Wen Pu

Giorgio Paolo Martini

Lester Gilbert Cottle, Iii

Linda Fayad

Austin Qingfeng Lu

Patrick Cheung

Sponsored and organic pieces of content are displayed in accordance with a blending model that is used to first identify a pattern of slots to which to assign either sponsored or organic pieces of content. This blending model is applied to a combination of both sponsored and non-sponsored pieces of content being considered for display to a user. This pattern only determines the slot assignments. The actual ranking of the pieces of content, and more particularly the actual ranking of the organic pieces of content, is determined by an ordering other than the ranking determined by the blending model, such as by using the original ordering of the second list. The pieces of content are then displayed in the order of this actual ranking, but in the slots indicated as having been assigned to be either sponsored or organic in the pattern determined by the blending model.

Dynamic slotting using blending model

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 embodiment, two machine learned models are trained. One is trained to output a probability that a searcher having a member profile in a social networking service will select a potential search result. The other is trained to output a probability that a member corresponding to a potential search result will respond to a communication from a searcher. Features may be extracted from a query, information about the searcher, and information about the member corresponding to the potential search result and fed to the machine learned models. The outputs of the machine learned models can be combined and used to rank search results for returning to the searcher.

Factored model for search results and communications based on search results

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 system and method includes receiving a search query and obtaining, from a database, member data of a member. For each of a plurality of nonlinear models, the nonlinear model is traversed based on a comparison of characteristics to conditions to obtain a score, wherein, among the nonlinear models, at least one characteristic is an inferred characteristic based on at least one of: activities by the member in an online networking system; and connections by the member in the online networking system. The score obtained from each of the nonlinear models is combined to obtain a combined score and a user interface to displays information related to the member based, at least in part on the combined score.

Nonlinear models for member searching

System and techniques for search result refinement are described herein. Search results and a search context may be obtained. A context dependent facet set may be added to a search result in the search results. A user interface of the context dependent facet set may be presented in conjunction with displaying the search results. A selection of a facet in the context dependent facet set may be received from a user. The search results being displayed may be filtered such that search results that meet a measurement of the facet are included in the displayed search results and the remaining search results are excluded from the display.

Search result refinement

System and techniques for facet expansion are described herein. A user interface element may be presented on facet selection portion of a search result display including search results. Here, the user interface element is arranged to accept user input of a facet. Partial user input for a facet may be received. A peer entity to an entity corresponding to the facet may be obtained. A peer facet may be presented in a suggestion element in the facet selection portion in response to receiving the partial user input.

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San Jose, CA, United States of America

Shan Zhou