Microsoft Technology Licensing, LLC

Linkedin Corporation

Jia Li

Deepak Agarwal

Shakti Dhirendraji Sinha

Dhruv Arya

Viet Thuc Ha

In an example embodiment, usage information is used to calculate one or more individual document historical information-deficient metrics (IDHIDMs) by combining values for the one or more metrics from multiple documents within the document corpus that share an identical combination of entities of the one or more entity types. A search query is segmented into a plurality of segments, wherein at least one of the plurality of segments is tagged as a first entity type and at least one of the plurality of segments is tagged as a second entity type. At least one for a combination of entities matching the tagged segments is used to rank one or more retrieved documents responsive to the query.

Entity-faceted historical click-through-rate

In an example, a plurality of member profiles in a social networking service are obtained, each member profile identifying a member and listing one or more skills the corresponding member has explicitly added to the member profile, the one or more skills indicating a proficiency by the member in the corresponding skill. A members-skills matrix is formed, wherein each cell in the matrix is assigned a value based on whether the corresponding member has the corresponding skill. The dot product of the members matrix and the skills matrix is then computed and used to identify one or more latent skills of a first member of the social networking service. Then a first digitally stored member profile is augmented with the one or more latent skills by combining the one or more latent skills with explicit skills for purposes of one or more searches that utilize member skills as an input variable.

Entity-aware features for personalized job search ranking

In an example embodiment, a plurality of member profiles in a social networking service are obtained, each member profile identifying a member and listing one or more skills the corresponding member has explicitly added to the member profile, the one or more skills indicating a proficiency by the member in the corresponding skill. A members-skills matrix is formed with members on one axis of the matrix and skills on another axis of the matrix, wherein each cell in the matrix is assigned a first value if the corresponding member explicitly lists the corresponding skill in the corresponding member profile and a second value if the corresponding member does not explicitly list the corresponding skill in the corresponding member profile. The members-skills matrix is factorized into a members matrix and a skills matrix in k-dimensional latent space, and then the dot product of the members matrix and the skills matrix is computed.

Growing community of inventors

Chicago, IL, United States of America

Jia Li