Abstract: A system associates sequentially descending weights with items in a reference list. The system identifies overlapping items from the first position to the second position in both the reference list and a test list. The system determines a weighted overlap for the overlapping items by combining the weights for each overlapping item to create a reference item weight, combining the weights for each item from the first position to the second position in the reference list to create a reference position weight, and determining the ratio between the reference item weight and the reference position weight. The system identifies overlapping items from the first position to the third position in both the reference list and the test list, and then determines a weighted overlap for these overlapping items. The system outputs the average weighted overlap as an efficiency evaluation of the information retrieval system that created the test list.

Abstract: The present disclosure is directed to generating search results, recommendations, and/or the like. In particular, the methods and systems of the present disclosure can: receive data describing one or more aspects of an entity; determine, based at least in part on the aspect(s) of the entity, one or more additional aspects of the entity; determine, based at least in part on the aspect(s) of the entity and the additional aspect(s) of the entity, a metric indicating a reputational prominence of the entity relative to multiple other entities of similar type; generate, based on the metric, one or more search results, recommendations, and/or the like; and communicate, to a computing device (e.g., for display, and/or the like), data indicating the search result(s), recommendation(s), and/or the like.

Abstract: The present disclosure is directed to generating search results, recommendations, and/or the like. In particular, the methods and systems of the present disclosure can: receive data describing one or more aspects of entities of different types; determine, based at least in part on the aspect(s) of the entities, one or more additional aspects of the entities; determine, based at least in part on the additional aspect(s) of the entities, a metric indicating an extent to which the entities correspond to one another; generate, based on the metric, one or more search results, recommendations, and/or the like; and communicate, to a computing device (e.g., for display, and/or the like), data indicating the search result(s), recommendation(s), and/or the like.

Abstract: Techniques for leveraging a social graph to facilitate the delivery of relevant recommendations. 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.

Abstract: 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.

Abstract: 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.

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 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.

Abstract: Systems and methods for de-duplicating electronic job postings are provided. In one embodiment, a method includes obtaining a first set of data indicative of a job posting. The first set of data includes one or more characteristics associated with the job posting. The method includes accessing a second set of data indicative of a job posting cluster. The job posting cluster includes one or more previous job postings. One of the previous job postings is a master job posting that is representative of the previous job postings. The method includes determining whether the job posting is duplicative of the previous job postings based at least in part on the characteristics associated with the job posting and the master job posting. The method includes providing for storage a third set of data indicative of the job posting associated with the job posting cluster or associated with a new job posting cluster.

Abstract: The present disclosure provides systems and methods that improve job posting structure and presentation by, for example, classifying portions of job postings into informative sections. As an example, given a job posting, a computing system implementing aspects of the present disclosure can separate the job posting into multiple portions. After separation into portions, the computing system can classify each portion into the most plausible job-posting-specific section. For example, the computing system can include and implement a machine-learned classification model to classify the portions into the sections. Following classification, the computing system can modify the job posting based on the classification of the portions. In particular, the structure and/or presentation of the job posting can be improved based on the classification of the portions into the sections.

Abstract: Systems and methods for extracting job skills from a job posting are provided. In one embodiment, a computer-implemented method includes obtaining data indicative of a job posting (including textual content associated with a job). The method includes identifying a portion of the textual content that is descriptive of one or more skills associated with the job. The portion of the textual content is in a first format. The method includes converting the portion of the textual content that is descriptive of the one or more skills associated with the job from the first format to a second format. The second format includes one or more text strings. The method includes determining the one or more skills associated with the job based at least in part on one or more of the text strings. The method includes providing an output indicative of the one or more skills associated with the job posting.

Abstract: Systems and methods for autosuggesting related objects to a user are provided. In one embodiment, a method includes receiving data indicative of a user input. The method includes identifying one or more ontologies based, at least in part, on the user input. Each ontology is associated with a category that is related to the user input. Each ontology includes a plurality of object types, each object type including one or more terms. The method includes determining one or more suggested related objects based, at least in part, on the user input and one or more of the plurality of object types. The one or more suggested related objects include one or more of the terms that are related to the user input. The method includes providing data indicative of the suggested related objects for display on a user interface via a display device.

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: Methods, systems, and apparatus, including computer programs encoded on storage devices, for performing a job opportunity search. In one aspect, a system includes a data processing apparatus, and a computer-readable storage device having stored thereon instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations.

Abstract: Methods, apparatus, systems, and computer-readable media are provided for increasing dimensionality of data structures associated with filling positions. In some implementations, a prediction of desired experience for a given position to be filled may be used to increase the dimensionality of a searchable data structure that represents the given position. For example, the predicted desired experience may be incorporated into a searchable field of the data structure. Among other things, increasing the dimensionality of the data structure may facilitate more granular searching of positions and guided creation of new positions to be filled. In some implementations, a predicted desired experience may be used to notify a user posting a new position whether a specified desired experience corresponds to a predicted desired experience.

Abstract: 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.

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.

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.

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: 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.