Abstract: In an example embodiment, a machine learning algorithm is used to train an objective prediction model to output a prediction value for an input member of a social networking service and a potential objective, based on member attribute information and action information. At prediction time, member attribute information and action information for a first user may be fed to the objective prediction model to obtain prediction values for a plurality of different potential objectives, one of which can be selected based on the prediction values. The selected objective can then be used to optimize coordinates, in a latent representation space, mapped to a plurality of different entities in a social network structure.

Abstract: A machine may be configured to personalize the content presented to a member of the social networking service visiting a web site. For example, the machine accesses a member identifier associated with a visitor to a web site. The member identifier identifies a particular member of a social networking service. The machine accesses member data associated with the member identifier in a database associated with the social networking service. The machine identifies one or more attributes associated with the particular member based on the member data. The machine selects an item of content for presentation to the particular member visiting the web site based on the one or more attributes associated with the particular member. The machine causes presentation of the item of content in a user interface of a widget included in the web site.

Abstract: In an example embodiment, a deep representation data structure is formed having first vectors representing titles from a social network data structure and second vectors representing skills from a social network data structure represented as coordinates in the deep representation data structure. One or more objective functions are applied to at least one combination of first vectors and second vectors in the deep representation data structure, causing an objective function output for each of the at least one combination. One or more coordinates for the vectors in the combination are clustered into occupation clusters, wherein each coordinate in each of the occupation clusters shares an occupation. A cluster representative is identified for each of the occupation clusters. Then, the cluster representative can be used to infer an occupation for a first member profile having a role that is ambiguous.

Abstract: A machine may be configured to personalize the content presented to a member of the social networking service visiting a web site. For example, the machine accesses a member identifier associated with a visitor to a web site. The member identifier identifies a particular member of a social networking service. The machine accesses member data associated with the member identifier in a database associated with the social networking service. The machine identifies one or more attributes associated with the particular member based on the member data. The machine selects an item of content for presentation to the particular member visiting the web site based on the one or more attributes associated with the particular member. The machine causes presentation of the item of content in a user interface of a widget included in the web site.