Microsoft Technology Licensing, LLC

Anthony Duane Duerr

Krishnaram Kenthapadi

Ahsan Latif Chudhary

Ryan Wade Sandler

Joseph Florencio

Parul Jain

Stephanie Chou

Kumaresh Pattabiraman

Kevin Kao

Caleb Timothy Johnson

Vijay K Dialani

Benjamin Hoan Le

Santosh Kumar Kancha

Keren Baruch

Sachit Kamat

Cory Scott

Kevin Chuang

Kunal Mukesh Cholera

Jeffrey Warren Lee

Jianbao Tao

Jana Vembunarayan

Prashanth Govindarajan

Oeystein Thorsen

Mohammad Aleagha

Dezhen Li

Minhtu Nguyen

Iain MacNaughton

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.

Specialized user interfaces and processes for increasing user interactions with job postings in a social network/top jobs

The disclosed embodiments provide a system for processing data. During operation, the system obtains events reflecting responses by a user to job recommendations outputted to the user. Next, the system updates a set of features for the user from the events. The system then includes the updated set of features in a feature repository for use by a statistical model in generating a ranking of jobs for the user. Finally, the system retrains the statistical model using the events prior to using the statistical model to update the outputted job recommendations using the ranking.

Nearline updates to personalized models and features

In an example embodiment, a fast and space-efficient ranking mechanism is used for vertical searches. A precedence is assigned to a plurality of features based on the relative importance of those features to the provider of a vertical search engine. In an example embodiment, this relative importance may be explicitly indicated by the provider of the vertical search engine. A unique data structure may then be created that incorporates the precedence of each feature.

Precedence-based fast and space-efficient ranking

A method of ingesting job postings into a social-networking system is disclosed. A candidate mapping is identified between a type of a data field corresponding to content items published on a third-party system and a type of a data field corresponding to job postings published on a social-networking system. A user interface is generated to prompt an administrator to identify the candidate mapping as a confirmed mapping. Based on the administrator identifying the candidate mapping as the confirmed mapping, the mapping is included in a data model corresponding to the content items published on the third-party system. Additional job postings to be published on the social-networking system are generated based on an application of the data model to additional content items published on the third-party system.

Code-free ingestion of job postings

In an example embodiment, a machine learning algorithm is used to train an engagement score model to calculate an engagement score for a particular member indicating a probability that the particular member would increase engagement with the social networking service if provided with statistical information about confidential data submitted by other members. Member usage information is obtained corresponding to a first member of a social networking service. Then a plurality of features are extracted from the member usage information corresponding to the first member. This plurality of features is inputted into the engagement model to obtain an engagement score for the first member. It is then determined whether or not to provide statistical information to the first member about confidential data submitted by other members based on the engagement score for the first member.

Machine learning algorithm for user engagement based on confidential data statistical information

In an example embodiment, a submission of confidential data is received from a user via a first computerized user interface. An identification of the user is obtained, and details regarding the submission are stored in a submission table. Then a request to display, to the user, statistical information derived from confidential data from users other than the user, is received from a second computerized user interface. Based on information stored in the submission table, a determination is made that the request to display, to the user, statistical information derived from confidential data from users other than the user should be granted. In response to the determining, the statistical information derived from confidential data from users other than the user is displayed via the second computerized user interface.

Tracking submission of confidential data in a computer system

In an example embodiment, a method for protecting against timestamp-based inference attacks in a computer system is provided. A timestamp corresponding to a time when confidential data is submitted to the computer system by a user is recorded. A modification value based on a frequency of submissions of confidential data to the computer system is selected. The timestamp is altered by adding the modification value to the timestamp.

Preventing timestamp-based inference attacks

In an example embodiment, confidential data submitted by a user and one or more attributes of the user are obtained from one or more submission tables in a confidential information database. It is then determined that the one or more attributes of the user correspond to a first slice. In response to the determining, a count associated with the first slice is incremented. The count associated with the first slice is compared with a first threshold. In response to a determination that the count associated with the first slice transgresses the first threshold, the confidential data and the one or more attributes are added to a backend queue. Then, extraction, transformation, and loading (ETL) of the confidential data in the backend queue is performed and the confidential data is placed in an ETL table, in the confidential information database corresponding to the first slice.

Anonymizing and grouping confidential data from members

In an example embodiment, a submission of confidential data is received from a user. A first service is queried using an identification of the user to obtain a member profile corresponding to the user in a social networking service. One or more primary attribute values are identified from the member profile. The one of the primary attribute values are used to query a second service to obtain a derived attribute value corresponding to the one or more primary attribute values. The confidential data, one or more of the primary attribute values, and the derived attribute value are stored in a first submission table in a confidential information database. Then the one or more of the primary attribute values and the derived attribute value are used to classify the user into one or more slices.

Generating an association between confidential data and member attributes

In an example embodiment, a method for protecting against confidential data-based inference attacks in a computer system is provided. A first confidential data value is received. Then a modification value is selected based on a level of privacy specified for the computer system. Then the first confidential data value is altered by adding the modification value to the first confidential data value.

Preventing inference attacks by joining on confidential data value

Techniques for standardizing and deduplicating unpaid job postings obtained from third-party systems are described. An unpaid job posting is obtained by a social networking service from a third-party system. The title and description of the unpaid job posting are standardized and combined into a standardized unpaid job posting. A deduplication process is performed to prevent the standardized unpaid job posting from replacing a paid job posting within the social networking service, and to prevent the standardized unpaid job posting from replacing a more authoritative, unpaid job posting within the social networking service.

Job posting standardization and deduplication

In an example embodiment, a submission of confidential data is received from a user. Then, the confidential data is encrypted using a first public key generated as part of a first public key-first private key pair. The encrypted confidential data is stored in a first column of a first submission table in a confidential information database. An identification of the user is encrypted using a second public key different than the first public key, the second public key generated as part of a first public key-first private key pair. Then, the encrypted identification of the user is stored in a second submission table in the confidential information database. The first private key is provided to a first component to decrypt the confidential information, without providing the second private key to the first component.

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Castro Valley, CA, United States of America

Anthony Duane Duerr