Patent application number | Description | Published |
20130031090 | METHODS AND SYSTEMS FOR IDENTIFYING SIMILAR PEOPLE VIA A BUSINESS NETWORKING SERVICE - Techniques for identifying and presenting member profiles similar to a source member profile are described. With some embodiments, a general recommendation engine is used to extract features from member profiles, and then store the extracted features, including any computed, derived or retrieved profile features, in an enhanced member profile. In real-time, the general recommendation engine processes client requests to identify member profiles similar to a source member profile by comparing select profile features stored in the enhanced member profile with corresponding profile features of the source member profile, where the comparison results in several similarity sub-scores that are then combined in accordance with directives set forth in a configuration file. Finally, the member profiles with the highest similarity scores corresponding with the user-selected member profile are selected, and in some instances, presented to a user. | 01-31-2013 |
20130318180 | LEVERAGING A SOCIAL GRAPH TO DELIVER RELEVANT RECOMMENDATIONS - Techniques for leveraging a social graph to identify senders and recipients of relevant recommendations and to facilitate the delivery of the relevant recommendations from the senders to the recipients are described. For example, a recommender is identified based on the recommender being a member of a social networking service who has interacted with an item of web-based content. A recommendee is identified based on the recommendee being another member of the social networking service who is connected to the recommender via a social graph maintained by the social networking service and based on having an affinity score for the item that exceeds a recommendee affinity score threshold and a connection strength to the recommender that exceeds a connection strength threshold. The recommender is sent a communication that invites the recommender to recommend the item to the recommendee. With some example embodiments, the communication is sent to the recommender within a pre-determined time measured from the time the recommender initiated an interaction with the item of web-based content. | 11-28-2013 |
20140136433 | REFERRING MEMBERS OF A SOCIAL NETWORK AS JOB CANDIDATES - Systems and methods for referring members of a social network as job candidates are described. In some examples, the systems and methods receive information associated with a job or a company associated with a job, identify members of a social network based on attributes for the members, and perform an action (e.g., send an email or update a widget on a profile page) associated with a member of the social network that is connected to the identified members and affiliated with the company. | 05-15-2014 |
20140136434 | REFERRING MEMBERS OF A SOCIAL NETWORK AS JOB CANDIDATES - Systems and methods for referring members of a social network as job candidates are described. In some examples, the systems and methods receive information associated with a job or a company associated with a job, identify members of a social network based on attributes for the members, and perform an action (e.g., send an email or update a widget on a profile page) associated with a member of the social network that is connected to the identified members and affiliated with the company. | 05-15-2014 |
20140143163 | USER CHARACTERISTICS-BASED SPONSORED JOB POSTINGS - A system may include a network interface, a user interface, and a recommendation engine. The user interface may be configured to receive a job characteristic of a job profile of a job posted to the social network and a job bid from an entity related to job to the social network. The recommendation engine may be configured to determine an aggregate job score for the user based on a relevance of the job characteristic to a user characteristic and the job bid. The network interface may be configured to transmit a message related to the job to the user based, at least in part, on the aggregate job score. | 05-22-2014 |
20140143164 | TECHNIQUES FOR QUANTIFYING THE JOB-SEEKING PROPENSITY OF MEMBERS OF A SOCIAL NETWORK SERVICE - Techniques are described herein for deriving, for each member of a social network service, a metric representing the job-seeking propensity of the member. Additionally, techniques for classifying each member with a job-seeking status (e.g., active job-seeker, passive job-seeker, or non-job-seeker) are described. A score-generating algorithm will analyze a variety of input data—including member profile data, social graph data, and activity or behavior data—to derive a job-seeker score, representing the job-seeking propensity of a member. | 05-22-2014 |
20140143165 | CUSTOMIZING A USER-EXPERIENCE BASED ON A JOB-SEEKER SCORE - Techniques are described herein for deriving, for each member of a social network service, a metric representing the job-seeking propensity of the member. Additionally, techniques for classifying each member with a job-seeking status (e.g., active job-seeker, passive job-seeker, or non-job-seeker) are described. A score-generating algorithm will analyze a variety of input data—including member profile data, social graph data, and activity or behavior data—to derive a job-seeker score, representing the job-seeking propensity of a member. Once derived, the metric is used to customize, personalize or otherwise tailor a user-experience. | 05-22-2014 |
20140143323 | USER CHARACTERISTICS-BASED SPONSORED COMPANY POSTINGS - A system may include a network interface, a user interface, and a recommendation engine. The user interface may be configured to receive a company characteristic of a company profile of a company posted to the social network and a company bid from an entity related to company to the social network. The recommendation engine may be configured to determine an aggregate company score for the user based on a relevance of the company characteristic to a user characteristic and the company bid. The network interface may be configured to transmit a message related to the company to the user based, at least in part, on the aggregate company score. | 05-22-2014 |
20140149328 | EVALUATION OF A RECOMMENDER - A machine may implement a recommender that provides recommendations to users. The machine may be configured to present a first version of the recommender configured by various parameters. A user may submit a message to the machine, and the machine may identify a parameter among the various parameters by tokenizing the message and identifying the parameter among the tokens. The machine may then generate a second version of the recommender by modifying the parameter and configuring the second version according to the modified parameter. The machine may then present the first and second versions of the recommender contemporaneously two different portions of the users. By tokenizing a further message received from the users, the machine may evaluate the first and second versions and determine whether the second version is a replacement of the first version. | 05-29-2014 |
20140195549 | SUGGESTED OUT OF NETWORK COMMUNICATION RECIPIENTS - Disclosed in some examples are methods, systems and machine readable medium for recommending an out-of-network communication by determining a set of potential recommended members of a social networking service based upon one or more recommendation criteria. In some examples the recommendation criteria may include: a profile similarity to a previous target of an out-of-network communication, a degree of correspondence between an interest and intent of the sending member, and a likelihood of response. | 07-10-2014 |
20140244612 | TECHNIQUES FOR QUANTIFYING THE INTENT AND INTERESTS OF MEMBERS OF A SOCIAL NETWORKING SERVICE - Techniques are described herein for deriving, for each member of a social networking service, a set of metrics representing a measure of the member's intent and interests. For example, a set of member-intent and member-interest scores are derived by detecting which of several applications and services that a particular user interacts with, when the interactions occur, the frequency of the interactions, the particular type of interactions, the nature of the any particular content (e.g., subject matter, topic, etc.) with which the member is interacting, and so forth. The member-intent and member-interest scores are then made available to a wide-variety of applications and services, for example, for use in personalizing various experiences to best suit the intent and interests of each member. | 08-28-2014 |
20150081576 | GENERATING A SUPPLEMENTAL DESCRIPTION OF AN ENTITY - A statistically overrepresented token in the descriptions of users associated with a target entity may be descriptive of the target entity. This may be true regardless of whether a primary description of the entity includes the overrepresented token. Accordingly, the entity description machine may access multiple descriptions of multiple users associated with the target entity. A portion of the multiple descriptions may each include a token descriptive of the target entity and of a subset of the multiple users. The entity description machine may determine that the token is overrepresented among the tokens within the multiple descriptions and generate a supplemental description of the target entity, where the supplemental description includes the overrepresented token. Once the supplemental description is generated, the entity description machine may use the supplemental description in referencing the target entity. | 03-19-2015 |