Abstract: A method of matching advertisements to users is disclosed. A plurality of attributes of a population of users is identified. A selection is received of an attribute of the plurality of attributes to which a target value is to be broadly matched. A correspondence of an advertisement to a user is determined based on a broad matching of the target value to the attribute. The advertisement is matched to the user based at least in part on the determining of the correspondence.

Abstract: A first user of a presentation machine may be a recruiter that initiates an action in reference to a first user profile. The first user profile may describe a first candidate for a job. The presentation machine may identify the first user profile and determine a similarity score that indicates a degree of similarity between the first user profile and a second user profile that describes a second candidate for the job. The presentation machine may also access a volatility score that indicates a likelihood that the second candidate is receptive to a proposal that the second candidate be employed by an employer. Based on the similarity score and on the volatility score, the presentation machine may determine a rank of the second user profile. Based on the determined rank, the presentation machine may present the second user profile to the first user.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A system may include a network interface and a batch processor coupled to the network interface. The batch processor may be configured to obtain social network profiles corresponding to entities and to obtain a target criterion. The batch processor may be configured to identify at least some of the entities based on a relationship between the social network profiles and the target criterion and to transmit, via the network interface, a data file to a computing device of the at least some of the entities, wherein the data file contains information related, at least in part, to the target criterion.

Abstract: Systems and methods for displaying a user interface object associated with a job candidate's social networking site proximate to a job listing presented by a website are described. When a job candidate selects the user interface object, the systems and methods identify and/or obtain information associated with the job candidate that is located or otherwise stored at the social networking site of which the job candidate is a member. Once the information is identified and/or obtained, the systems and methods provide the information to the website, such as to a job application submission system associated with the website.

Abstract: Systems and methods for displaying job listings via a website are described. In some examples, the systems identify information associated with a viewer of a website, and selectively display job listing information based of the information associated with the viewer of the website, such as information attributed to the viewer within a social network of which the viewer is a member.

Abstract: Systems and methods for displaying a user interface object associated with a job candidate's social networking site proximate to a job listing presented by a website are described. When a job candidate selects the user interface object, the systems and methods identify and/or obtain information associated with the job candidate that is located or otherwise stored at the social networking site of which the job candidate is a member. Once the information is identified and/or obtained, the systems and methods provide the information to the website, such as to a job application submission system associated with the website.

Abstract: 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.

Abstract: 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.

Abstract: 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.