Microsoft Technology Licensing, LLC

Linkedin Corporation

David Hardtke

Bo Zhao

Eric Huang

Joel Daniel Young

Lijun Tang

Xu Miao

Yitong Zhou

Jacob Bollinger

This disclosure is directed to determining various economic graph indices and, in particular, to systems and methods that leverage a graph analytic engine and framework to determine values assigned to graph nodes extracted from one or more member profiles, and visualizing said values to correlate skills, geographies, and industries. The disclosed embodiments include a client-server architecture where a social networking server has access to a social graph of its social networking members. The social networking server includes various modules and engines that import the member profiles and then extracts certain defined attributes from the member profiles, such as employer (e.g., current employer and/or past employers), identified skills, educational institutions attended, and other such defined attributes. Using these attributes as nodes, the social networking server constructs a graph using various graph processing techniques. The resulting graph is then used to correlate and rank the various attributes that define the graph.

Constructing graphs from attributes of member profiles of a social networking service

Learning to rank modeling in the context of an on-line social network is described. A learning to rank model can learn from pairwise preference (e.g., job posting A is more relevant than job posting B for a particular member profile) thus directly optimizing for the rank order of job postings for each member profile. With ranking position taken into consideration during training, top-ranked job postings may be treated by a recommendation system as being of more importance than lower-ranked job postings. In addition, a learning to rank approach may also result in an equal optimization across all member profiles and help minimize bias towards those member profiles that have been paired with a larger number of job postings.

Learning a ranking model using interactions of a user with a jobs list

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Oakland, CA, United States of America

David Hardtke