Abstract: Described herein is a candidate selection technique for an online recommendation system or service. Upon receiving a request to generate recommendations for an end-user, attributes of the end-user are obtained. The end-user attributes are then provided as an input to a trained machine learned model, which generates for each attribute a score indicating the predictive power of the attribute in recommending a relevant content item (e.g., an online job posting). Then, a weighted-OR query is derived from a combination of attributes having scores that exceed a predetermined threshold. The query is expressed, such that, content items satisfying the query include at least “k” attributes specified by the query.

Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.

Abstract: In an example embodiment, machine learning is used to train a machine-learned model that projects each entity, title pair into a single number, called a seniority score, to represent the career progression needed for that position. For example, company A’s “software engineer” and company B’s “senior software engineer” can be represented as two separate numbers, one being p (company A, software engineer) and the other being p (company B, senior software engineer) on the same axis. This allows a comparison to be made about the absolute levels of each title despite their potential different meanings at different entities.

Abstract: Systems and methods for classifying job search queries for improved precision using a machine-trained classifier are provided. In example embodiments, a network system receives a job search query including one or more terms from a device of a user. The network system extracts one or more features from the job search query, whereby the one or more features are derived from the one or more terms. Based on the one or more features, a machine-learned model of the classifier determines whether to use a title field search process or a compound search process. Based on the determining, the network system formats the job search query into a corresponding machine-language format and performs the job search query to derive results. The network system causes presentation of the results on the device of the user.

Abstract: The disclosed embodiments provide a system for performing activity-based inference of title preferences. During operation, the system determines features and labels related to first title preferences for jobs sought by a first set of candidates. Next, the system inputs the features and the labels as training data for a machine learning model. The system then applies the machine learning model to additional features for a second set of candidates to produce predictions of second title preferences for the second set of candidates. Finally, the system stores the predictions in association with the second set of candidates.

Abstract: In some embodiments, a computer system selects a first subset of candidate content items based on their filter scores that are generated based on a partial generalized linear mixed model comprising a baseline model and a user-based model, with the baseline model being a generalized linear model, and the user-based model being a random effects model based on user actions by the target user directed towards reference content items related to the candidate content items. In some embodiments, the computer system then selects a second subset from the first subset based on recommendation scores that are generated based on a full generalized linear mixed model comprising the baseline model, the user-based model, and an item-based model, with the item-based model being a random effects model based on user actions directed towards the candidate online content item by reference users related to the target user.

Abstract: Systems and methods for classifying job search queries for improved precision using a machine-trained classifier are provided. In example embodiments, a network system receives a job search query including one or more terms from a device of a user. The network system extracts one or more features from the job search query, whereby the one or more features are derived from the one or more terms. Based on the one or more features, a machine-learned model of the classifier determines whether to use a title field search process or a compound search process. Based on the determining, the network system formats the job search query into a corresponding machine-language format and performs the job search query to derive results. The network system causes presentation of the results on the device of the user.

Abstract: Methods, systems, and computer programs are presented for search optimization based on relevant-parameter selection. One method includes an operation for training a machine-learning program with information about users of an online service to generate a machine-learning model that calculates parameter preference scores for a plurality of parameters. Further, the method includes operations for detecting a job search for a user, identifying user parameters associated with the user, and calculating, by the machine-learning model, the parameter preference scores for the user parameters. Further, search parameters are determined by selecting a predetermined number of user parameters base on the parameter preference scores. A search of a job-postings database is performed with the search parameters, and the results are presented on a display.

Abstract: In an example embodiment, a first machine learned model is trained to produce output, and a second machine learned model is then trained using training data that has been labeled, at least partially, using the output of the first machine learned model. The first machine learned model is trained to output a measure of how strong a positive signal in the training data really is. Specifically, this measure indicates the level of intention of a user who has engaged in a first user interface action with respect to a piece of content to engage in a subsequent second user interface action with the same piece of content.

Abstract: Methods, systems, and computer programs are presented for search optimization based on relevant-parameter selection. One method includes an operation for training a machine-learning program with information about users of an online service to generate a machine-learning model that calculates parameter preference scores for a plurality of parameters. Further, the method includes operations for detecting a job search for a user, identifying user parameters associated with the user, and calculating, by the machine-learning model, the parameter preference scores for the user parameters. Further, search parameters are determined by selecting a predetermined number of user parameters base on the parameter preference scores. A search of a job-postings database is performed with the search parameters, and the results are presented on a display.

Abstract: In some embodiments, a computer system determines that online postings belong to a cohort based on the postings having an attribute of the cohort, identifies skills from the postings, determines that a user belongs to the cohort based on a determination that a profile of the user includes the attribute(s) of the cohort, determines that one or more of the skills is stored in association with the profile, determines a user confidence score that indicates a relevance level of the skill to the user for each one of the one or more of the skills, determines a cohort confidence score for each one of the one or more of the skills based on how many of the postings include the skill, and displays a recommendation associated based on a combination of the user confidence score and the cohort confidence score for at least a portion of the skills.

Abstract: The disclosed embodiments provide a system for selecting recommendations based on title transition embeddings. During operation, the system obtains a word embedding model of a set of job histories. Next, the system calculates similarities between pairs of the embeddings produced by the word embedding model from attributes associated with titles in the set of job histories. The system then identifies, based on the similarities, job titles with high similarity to a current title of the candidate. Finally, the system outputs the job titles for use in selecting job recommendations for the candidate.

Abstract: In some embodiments, a computer system selects a first subset of candidate content items based on their filter scores that are generated based on a partial generalized linear mixed model comprising a baseline model and a user-based model, with the baseline model being a generalized linear model, and the user-based model being a random effects model based on user actions by the target user directed towards reference content items related to the candidate content items. In some embodiments, the computer system then selects a second subset from the first subset based on recommendation scores that are generated based on a full generalized linear mixed model comprising the baseline model, the user-based model, and an item-based model, with the item-based model being a random effects model based on user actions directed towards the candidate online content item by reference users related to the target user.

Abstract: This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving, from a member of an online networking service, a query for one or more available employment positions; executing the query, at a database of employment positions, to retrieve the one or more available employment positions; filtering results of the query according to one or more facets; generating an electronic user interface to display the filtered results; and allowing the member to adjust the facets using the electronic user interface.

Abstract: The disclosed embodiments provide a system that ranks job recommendations based on title preferences. During operation, the system determines features related to applications for jobs by a candidate, wherein the features include a title preference for the candidate and a similarity between a first set of attribute values for the candidate and a second set of attribute values for a job. Next, the system applies a machine learning model to the features to produce scores representing likelihoods of the candidate applying to the jobs. The system then generates a ranking of the jobs by the scores. Finally, the system outputs, to the candidate, at least a portion of the ranking as a set of recommendations.

Abstract: The disclosed embodiments provide a system for performing activity-based inference of title preferences. During operation, the system determines features and labels related to first title preferences for jobs sought by a first set of candidates. Next, the system inputs the features and the labels as training data for a machine learning model. The system then applies the machine learning model to additional features for a second set of candidates to produce predictions of second title preferences for the second set of candidates. Finally, the system stores the predictions in association with the second set of candidates.

Abstract: The disclosed embodiments provide a system for recommending jobs based on title transition embeddings. During operation, the system obtains a word embedding model of job histories of members of an online network. Next, the system applies the word embedding model to a first set of attributes associated with a title of a candidate to produce a first embedding. The system also applies the word embedding model to a second set of attributes associated with a job title of a job to produce a second embedding. The system then calculates a similarity between the first and second embeddings. Finally, the system outputs the similarity for use in recommending the job to the candidate.

Abstract: This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving a request from a member for available employment positions posted at a social networking service, determining a cohort for the member, retrieving a query that is associated with the cohort for the member, executing the query at a database of employment positions, receiving results of the query, and causing the results of the query to be displayed, using an electronic user interface, to the member.

Abstract: This disclosure relates to systems and methods for recommending relevant positions. A method includes receiving, from a member of an online networking service, a query for one or more available employment positions; executing the query, at a database of employment positions, to retrieve the one or more available employment positions; filtering results of the query according to one or more facets; generating an electronic user interface to display the filtered results; and allowing the member to adjust the facets using the electronic user interface.