Abstract: This disclosure provides for systems and methods directed to generating colleague recommendations within a social network service. The systems and methods may include identifying a sample member of a social network service and determining an organizational unit associated with the sample member. The sample member may be associated with prior member activity involving a source member and the organizational unit may represent a department within an organization. The systems and methods may also include identifying various candidate members, where the candidate members are associated with the organizational unit. The systems and methods may also include surfacing the plurality of candidate members to a computer system being operated by the source member.

Abstract: A system extracts data from profiles on a social networking system. The system writes the data to a database when the data exceeds a first threshold. The system then determines a degree of similarity between the data and other similar data, and writes the data and a first portion of the other similar data to the database when the degree of similarity between the data and the first portion of the other similar data exceeds a second threshold. The system then receives into the computer processor input from a plurality of users. The input relates to an agreement or disagreement regarding the degree of similarity between the data and the first portion of the other similar data. The system writes the data and a second portion of the other similar data to the database as a function of the agreement or disagreement of the plurality of users.

Abstract: 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.

Abstract: Techniques for presenting a personalized member profile page to a viewer are described. A highlight module can receive a request to view a profile page of a member in a social network. The highlight module can access viewer data of a viewer associated with the request, and access member data of the member. Additionally, the highlight module can determine a plurality of member attributes relevant to the viewer based on the viewer data, the plurality of member attributes being derived from the member data. Furthermore, the highlight module can calculate an overall score for a member attribute in the plurality of member attributes based on the viewer data and the member data. Subsequently, a profile generation module can cause a presentation, on a display of a device, of the member attribute on the profile page, when the overall score of the member attribute is higher than a predetermined threshold value.

Abstract: Network insights are bite-sized pieces of information about member's connections in the on-line social network that contain information that may be of interest to a viewing member but that is difficult or time-consuming to find. A network insights system generates and stores insights for each member in an on-line social network system. A network insights system also generates a set of personalization features for each member profile in the on-line social network. A set of personalization features for a member profile reflects the relevance of each possible value of a certain profile attribute with respect to the member profile. When a network insights system determines that a member (the viewer) is to be presented with one or more insights with respect to another member (the viewee) it selects insights of the viewee for presentation to the viewer based on the set of personalization features for the viewer.

Abstract: Techniques for presenting a personalized member profile page to a viewer are described. A highlight module can receive a request to view a profile page of a member in a social network. The highlight module can access viewer data of a viewer associated with the request, and access member data of the member. Additionally, the highlight module can determine a plurality of member attributes relevant to the viewer based on the viewer data, the plurality of member attributes being derived from the member data. Furthermore, the highlight module can calculate an overall score for a member attribute in the plurality of member attributes based on the viewer data and the member data. Subsequently, a profile generation module can cause a presentation, on a display of a device, of the member attribute on the profile page, when the overall score of the member attribute is higher than a predetermined threshold value.

Abstract: A system extracts data from profiles on a social networking system. The system writes the data to a database when the data exceeds a first threshold. The system then determines a degree of similarity between the data and other similar data, and writes the data and a first portion of the other similar data to the database when the degree of similarity between the data and the first portion of the other similar data exceeds a second threshold. The system then receives into the computer processor input from a plurality of users. The input relates to an agreement or disagreement regarding the degree of similarity between the data and the first portion of the other similar data. The system writes the data and a second portion of the other similar data to the database as a function of the agreement or disagreement of the plurality of users.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: This disclosure provides for systems and methods directed to generating colleague recommendations within a social network service. The systems and methods may include identifying a sample member of a social network service and determining an organizational unit associated with the sample member. The sample member may be associated with prior member activity involving a source member and the organizational unit may represent a department within an organization. The systems and methods may also include identifying various candidate members, where the candidate members are associated with the organizational unit. The systems and methods may also include surfacing the plurality of candidate members to a computer system being operated by the source member.

Abstract: 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.

Abstract: Disclosed in some examples is a method of inferring one or more attribute values of a member of a social networking service by collecting a plurality of attribute values associated with attributes of a plurality of members of a social networking service, the plurality of attribute values including member profile information; and inferring one or more attribute values regarding a particular member in the plurality of members based upon the collected plurality of attribute values and a plurality of collected attribute values of the particular member.