Linkedin Corporation

Microsoft Technology Licensing, LLC

Workday, Inc.

Michael Conover

Mathieu Bastian

Mario Sergio Rodriguez

Viet Thuc Ha

Jessica V Zuniga

Timothy Paul Jurka

Claire D McGinty

Quinn Z Shen

Samir Mavji Shah

Ankit Gupta

Peter Nicholas Skomoroch

Matthew Terence Hayes

Patrick A Chase

Andrew O Hatch

Scott Mitchell Blackburn

Alejandro Jorge Perez

Samuel Stokes

Gregory W Bayer

Yong-Yeol Ahn

Akshay D Kothari

Azadeh Nematzadeh Chekuvar

Ian Benjamin Wood

Jaehyuk Park

Yizhi Jing

The disclosed embodiments provide a system for processing data. During operation, the system uses one or more queries of a data set to aggregate one or more measurements of user engagement with a set of content items within a second-degree network of a member. Next, the system calculates one or more metrics from the one or more measurements. The system then ranks the set of content items by the one or more metrics. Finally, the system uses the ranking to present one or more of the content items in a content feed to the member.

Generating content feeds based on expanded network engagement

A system for disambiguation of noisy text includes an interface and processor. The interface is configured to receive a set of words. The processor is configured to determine a token from the set of words, wherein the token includes a subset of the set of words; determine candidates for the token; determine a ranking for the candidates based at least in part on a semantic similarity between the token and each of the candidates; determine whether to select a top ranked candidate or to abstain; and in response to a select determination to select the top ranked candidate, indicate the top ranked candidate is selected.

Fast entity linking in noisy text environments

Methods and systems for generating tailored user interface presentations based on microindustry clustering. According to various embodiments, the system accesses a set of entity profiles and a set of member profiles. The system determines a set of feature vectors for each entity of the set of entity profiles and identifies a set of movement data representing changes in association of one or more members from a first entity to a second entity. The system generates an entity graph for the set of entities and the set of members. The systems generate a first set of clusters in the entity graph, a second set of clusters by partitioning one or more of the first clusters, and a set of third clusters from the set of second clusters, combining one or more of the second clusters.

Data flow based feature vector clustering

The disclosed embodiments provide a system for processing data. During operation, the system calculates one or more quantiles from a distribution of historic user engagement with content within a member segment of a social network. Next, the system performs a comparison of the one or more quantiles with a current measurement of user engagement with a content item within the member segment. When the comparison indicates that the current measurement of user engagement exceeds a threshold, the system presents the content item to one or more members in the member segment.

Engagement-based selection of content for member segments of social networks

The disclosed embodiments provide a system for processing data. During operation, the system obtains one or more metrics associated with user interaction with a set of content items in a member segment of a social network. Next, the system ranks the set of content items by the one or more metrics. The system then generates a content pool for the member segment based on the ranking of the content items. Finally, the system creates a content feed from the content pool for presentation to a user in the member segment.

Segment-based content pools for inclusion in content feeds

For each of a group of nodes including a node corresponding to a first member and a plurality of nodes corresponding to members with which the first member is directly connected in a social graph, a plurality of reputation scores may be calculated for a node in the social graph, and the plurality of reputation scores for the node may be averaged into an importance score for the node. A keyness score for each of the plurality of nodes corresponding to members with which the first member is directly connected in the social graph can be calculated, the keyness score for each of the plurality of nodes being based on a connection strength for a connection between the node and the node corresponding to the first member and based on the importance score for the node. The keyness scores may be selected, averaged, and saved as a social capital score.

Quantifying social capital

The disclosed embodiments provide a reputation system. The reputation system includes a ranking apparatus that obtains a set of reputation scores for one or more dimensions of a set of items in the reputation system and generates a ranking of the items based on the reputation scores and the one or more quantiles. The reputation system also includes a query-processing apparatus that obtains a query comprising the one or more dimensions and one or more quantiles associated with the one or more dimensions and provides the ranking in a response to the query.

Querying of reputation scores in reputation systems

The disclosed embodiments provide a reputation system. The reputation system includes a statistical model associated with a set of items and a set of dimensions of the items in the reputation system, wherein the statistical model is trained using a positive class and a negative class. The reputation system also includes a scoring apparatus that applies the statistical model to a set of features for each of the items to estimate a set of reputation scores for the items. Finally, the reputation system includes a ranking apparatus that enables use of the set of reputation scores in the reputation system.

Estimating reputation scores in reputation systems

The disclosed embodiments provide a reputation system. The reputation system includes a scoring apparatus that provides a matrix of reputation scores for a set of items and a set of dimensions of the items in the reputation system, wherein the matrix comprises unknown values for a subset of the reputation scores. The reputation system also includes an inference apparatus that calculates a factorization of the matrix and uses the factorization to update the matrix with a set of inferred values for the set of reputation scores. Finally, the reputation system includes a ranking apparatus that uses the updated matrix to obtain a ranking of the items by one or more of the dimensions.

Generating rankings of reputation scores in reputation systems

Growing community of inventors

San Francisco, CA, United States of America

Michael Conover