Abstract: Embodiments presented herein provide a partner authentication (PA) system that coordinates a network-based authorization process for an application. The PA system exchanges a series of messages with the application seeking an access token for a protected resource, an authorization server associated with the resource, and an agent executing on a device accessed by a user who wants the application to access the resource. The PA system and the agent communicate with the authorization server on behalf of the application throughout the authorization process. At the completion of the authorization process, the PA system receives an access token and a refresh token from the server on behalf of the application and sends a partner authorization (PA) token to the application. When the application seeks access to the resource that is available to authorized parties via the resource server, the application sends the PA token to the PA system and receives the access token in return.

Abstract: Techniques for enhancing usability and electronic resource efficiency using job relevance are disclosed herein. In some embodiments, a list of a predetermined number of top job openings for a member of the social networking system is generated. The list is communicated to a device of the user for presentation in a user interface on the device. The user interface allows the member to browse through and provide an indication of a lack of relevancy of each of the list of the predetermined number of top job openings to the member and an indication of a reason for the lack of relevancy. Based on the user providing the indication of the lack of relevancy and the reason, a modified relevancy assessment of each of the plurality of job openings is generated. The list of the predetermined number of top job openings for the member is regenerated and communicated to the device.

Abstract: Techniques for enhancing usability and electronic resource efficiency using job relevance are disclosed herein. In some embodiments, a plurality of members of the social networking system are identified for which the new job is relevant. The job posting is identified as a top job posting for each of a subset of the plurality of members for which the new job is relevant, the identifying of the job posting as a top job posting including accessing an online data store that includes enhanced relevancy data, the enhanced relevancy data including real-time feedback provided by one or more of the plurality of members with respect to the values of the attributes specified within the job posting. A specialized notification of the top job posting is generated for each of the identified subset of the identified plurality of members.

Abstract: A data management system manages secured data for a plurality of users. The data management system utilizes an access authorization system to authenticate users seeking access to the data management system. The access authorization system provides access tokens to authenticated users. The access tokens enable the authenticated users to access the data management system without again providing authentication data. The access authorization system includes, for each user, an access policy that governs whether the users can use the access tokens to access the data management system. The access tokens have a finite lifetime. If the users use the access tokens within the finite lifetime and if the users satisfy all of the access rules of the access policies, then the lifetime of the access tokens can be extended a finite number of times.

Abstract: In an example, a plurality of previously submitted confidential data values of a first confidential data type retrieved for a slice having one or more attributes. For a confidential data type, one or more submitted confidential data values of the confidential data type from the slice that are considered outliers based on an external data set or internal data set. A confidence score is calculated by multiplying a support score for the confidential data type in the slice by a non-outlier score for the confidential data type in the slice, the support score being equal to n?/(n?+c), where c is a smoothing constant and n? is the number of non-excluded submitted confidential data values of the confidential data type in the slice and the non-outlier score being equal to n?/n, where n is the total number of non-null submitted confidential data value of the confidential data type in the slice.

Abstract: In an example, a plurality of segments of percentile information indicating submitted confidential data values from users matching at least one attribute of a submitting user are retrieved. Then, for each of the segments, an interquartile range is calculated for a first and a second of a plurality of percentiles in the segment, an initial lower limit is computed for the segment by taking a maximum of zero or the difference between the value for the first of the plurality of percentiles and a product of a preset alpha parameter and the interquartile range, and interpolation is performed on values for the plurality of percentiles for the segment to obtain values for a third percentile. The initial lower limits and the interpolated values for the third percentiles are aggregated across the segments. A merged lower limit is determined by applying a function to the aggregated initial lower limits and aggregated interpolated values.

Abstract: In an example, a submission of a confidential data value of a first confidential data type is received from a first user with one or more attributes. A plurality of previously submitted confidential data values of a first confidential data type for a cohort matching the one or more attributes of the first user are retrieved. A plurality of percentiles for the confidential data values are calculated. Then, an interquartile range is calculated for a first and a second of the plurality of percentiles. A lower limit for the first confidential data type and the cohort is computed by taking a maximum of zero or the difference between the value for the first of the plurality of percentiles and a product of a preset alpha parameter and the interquartile range. Then it is determined if the confidential data value submitted by the user is lower than the lower limit.

Abstract: In an example, a plurality of segments of percentile information indicating submitted confidential data values from users matching at least one attribute of a submitting user are retrieved. Then, for each of the segments, an interquartile range is calculated for a first and a second of a plurality of percentiles in the segment, an initial lower limit is computed for the segment by taking a maximum of zero or the difference between the value for the first of the plurality of percentiles and a product of a preset alpha parameter and the interquartile range, and interpolation is performed on values for the plurality of percentiles for the segment to obtain values for a third percentile. The initial lower limits and the interpolated values for the third percentiles are aggregated across the segments. A merged lower limit is determined by applying a function to the aggregated initial lower limits and aggregated interpolated values.

Abstract: In an example, a submission of a confidential data value of a first confidential data type is received from a first user with one or more attributes. A plurality of previously submitted confidential data values of a first confidential data type for a cohort matching the one or more attributes of the first user are retrieved. A plurality of percentiles for the confidential data values are calculated. Then, an interquartile range is calculated for a first and a second of the plurality of percentiles. A lower limit for the first confidential data type and the cohort is computed by taking a maximum of zero or the difference between the value for the first of the plurality of percentiles and a product of a preset alpha parameter and the interquartile range. Then it is determined if the confidential data value submitted by the user is lower than the lower limit.

Abstract: In an example, a plurality of previously submitted confidential data values of a first confidential data type retrieved for a slice having one or more attributes. For a confidential data type, one or more submitted confidential data values of the confidential data type from the slice that are considered outliers based on an external data set or internal data set. A confidence score is calculated by multiplying a support score for the confidential data type in the slice by a non-outlier score for the confidential data type in the slice, the support score being equal to n?/(n?+c), where c is a smoothing constant and n? is the number of non-excluded submitted confidential data values of the confidential data type in the slice and the non-outlier score being equal to n?/n, where n is the total number of non-null submitted confidential data value of the confidential data type in the slice.

Abstract: A referral system is provided with an on-line social network system. The referral system selects a set of (connected profile, job posting) pairs for presentation to a member of the on-line social network system who is an employee of a particular target organization, based on each pair's respective presentation score. The presentation score for a (connected profile, job posting) pair reflects likelihood that the employee refers a candidate represented by the connected profile from the (connected profile, job posting) pair for the job represented by the job posting from the (connected profile, job posting) pair.

Abstract: A referral system is provided with an on-line social network system. The referral system accesses a member profile representing an employee of a certain company. For each job posting that represents a job at the target organization, the referral system generates a presentation score that reflects the likelihood of the employee referring someone from their network for the job represented by that job posting. The referral system uses respective presentation scores generated for the job postings to determine which of these job postings are to be selected for inclusion into a referral UI for presentation to the employee.

Abstract: A machine may be configured to determining key concepts in documents. For example, the machine accesses a universal concept graph that includes a first set of nodes that represent concept phrases derived from internal documents associated with a social networking service (SNS) and external documents that are external to the SNS, and a first set of edges that connect a plurality of nodes of the first set of nodes. The machine accesses a content object associated with the SNS. The machine generates an induced concept graph associated with the content object based on an analysis of the content object and the universal concept graph. The machine identifies one or more key concept phrases in the content object based on applying one or more key concept selection algorithms to the induced concept graph. The machine stores the one or more key concept phrases in a record of a database.

Abstract: A machine may be configured to generate a universal concept graph. For example, the machine generates an internal set of concept phrases based on an internal dataset including content from internal documents associated with a social networking service (SNS). The machine generates an external set of concept phrases based on an external dataset including content from external documents that are external to the SNS. The machine generates a set of nodes for a universal concept graph based on performing a union operation of the internal set of concept phrases and the external set of concept phrases. Each node corresponds to a particular concept phrase. The machine generates a set of edges among a plurality of nodes of the set of nodes based on one or more relationship indicators for pairs of nodes. The machine generates the universal concept graph based on the set of nodes and the set of edges.

Abstract: Methods and systems for generating queries from a behavioral index model are described. According to various embodiments, a system receives a query input including one or more search terms from a client device associated with a member profile. The system generates a query from the one or more search terms and one or more profile terms. The system causes one or more query machines to search one or more inverted indices using the query and receives one or more result of documents distributed across the inverted indices. The system generates an ordered list of the one or more result sets and causes presentation of the ordered list at a client device associated with the member profile.

Abstract: Methods and systems for developing a behavioral index model are described. According to various embodiments, the system accesses a set of behavioral data stored in association with a set of member profiles. The system generates a set of training data from the set of behavioral data and generates a set of feature vectors from the set of training data. The system generates a behavioral index model from the set of training data and the set of feature vectors. The system then generates a tuned behavioral index model from the behavioral index model by tuning a threshold of the behavioral index model using a precision recall curve.

Abstract: A referral system is provided with an on-line social network system. The referral system accesses a member profile representing an employee of a certain company and a target job posting representing a job opening at the same company. The referral system analyzes respective profiles of the member's connections in relation to the target job posting. For each connected member profile, the referral system generates a presentation score with respect to the job posting. The presentation score generated reflects likelihood that that particular employee refers a candidate represented by the connected member profile for the job represented by the target job posting and is used to determine which connected member profiles are to be presented to the employee.

Abstract: A referral system is provided with an on-line social network system. The referral system accesses a member profile representing an employee of a certain company (employee member profile) and a connected member profile representing the employee's connection in the on-line social network. The referral system then examines the job postings that represents job openings at the employee's company and selects a presentation set of job postings based on (1) fitness of the candidate represented by the connected member profile for a job represented by the job posting and (2) familiarity of the employee with the job.

Abstract: In an example embodiment, a method is provided where information regarding a member of a social networking service and a job opening are obtained. Then information about a plurality of courses available for the member to take is obtained, the information including cost, time, and a vector including, for each of a plurality of skills identified in a member profile of the member, an effect of taking the corresponding course. A subset selection algorithm is then used to select an optimal combination of courses selected from the plurality of courses, based on minimizing cost and time while maximizing cumulative increase in odds of the member successfully obtaining the job opening from taking the selected courses.