Abstract: In an example embodiment, a specialized machine learned model, called a look-alike model, is trained using a machine learning algorithm to predict future job engagement for a user. This look-alike model is then used to create new segments on top of the segments provided by a rules-based approach. Specifically, the look-alike model is designed to take users who have been segmented by a rule-based approach into an “inactive job seeker” categorization (such as those assigned to the resting users and dormant users segments) and calculate a predicted job engagement score for these users. Based on the predicted job engagement score, a user may then be reassigned from one of the inactive job seeker categorizations to one of one or more new job seeker categorizations (such as predicted open job seekers or predicted opportunistic job seekers).

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.

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 an example embodiment, a specialized machine learned model, called a look-alike model, is trained using a machine learning algorithm to predict future job engagement for a user. This look-alike model is then used to create new segments on top of the segments provided by a rules-based approach. Specifically, the look-alike model is designed to take users who have been segmented by a rule-based approach into an “inactive job seeker” categorization (such as those assigned to the resting users and dormant users segments) and calculate a predicted job engagement score for these users. Based on the predicted job engagement score, a user may then be reassigned from one of the inactive job seeker categorizations to one of one or more new job seeker categorizations (such as predicted open job seekers or predicted opportunistic job seekers).

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: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system executes a search query based on a search term and the geographic indicator. In response to determining that a number of the search results is less than a threshold number, the search system determines, based on historical search logs from other users in the first geographic region, a likelihood value indicating a likelihood that the other users in the first geographic region expanded the geographic region of their search queries. The search system compares the likelihood value to a threshold likelihood value, and determines, based on the comparison, that the likelihood value meets or exceeds the threshold likelihood value. The search system then executes an expanded search based on the search term and an expanded geographic indicator that encompasses the first geographic region.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system executes a search query based on a search term and the geographic indicator. In response to determining that a number of the search results is less than a threshold number, the search system determines, based on historical search logs from other users in the first geographic region, a likelihood value indicating a likelihood that the other users in the first geographic region expanded the geographic region of their search queries. The search system compares the likelihood value to a threshold likelihood value, and determines, based on the comparison, that the likelihood value meets or exceeds the threshold likelihood value. The search system then executes an expanded search based on the search term and an expanded geographic indicator that encompasses the first geographic region.

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: The disclosed embodiments provide a system for processing data. During operation, the system obtains labels for entities found in portions of text in a first set of jobs. Next, the system inputs the portions of text and the labels as training data for a machine learning model. The system then applies the machine learning model to a second set of jobs to generate predictions of additional entities in additional portions of text in the second set of jobs. Finally, the system creates, based on the predictions, an index containing mappings of the additional entities to subsets of the second set of jobs in which the additional entities are found.

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 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: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system executes a search query based on a search term and the geographic indicator. In response to determining that a number of the search results is less than a threshold number, the search system determines, based on historical search logs from other users in the first geographic region, a likelihood value indicating a likelihood that the other users in the first geographic region expanded the geographic region of their search queries. The search system compares the likelihood value to a threshold likelihood value, and determines, based on the comparison, that the likelihood value meets or exceeds the threshold likelihood value. The search system then executes an expanded search based on the search term and an expanded geographic indicator that encompasses the first geographic region.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system determines a set of candidate alternate search terms based on historical search logs that include records of previously submitted search terms, corresponding search results that were presented to users, and corresponding search results that were selected by the users. The set of candidate alternate search terms is selected from titles of the corresponding search results that were selected by the users. The search system ranks the set of candidate alternate search terms based on determined probabilities that each of the alternate candidate search terms will be selected if presented to a user, and selects a first candidate alternate search term from the set of candidate alternate search terms based on the ranking. The search system generates an expanded search term based on the first candidate alternate search term.

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: 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 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: In some embodiments, the disclosed subject matter involves techniques for generating type-ahead query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring. Partial query strings may be parsed for literal matching and be processed for spell checks, acronym expansion, and other expansion and rewriting of the partial query to a known possible query suggestion. Possible query suggestions are weighted using global feature metrics. Various weighting, confidence levels and merging based on scoring may be used to rank the suggestions. A machine learning model may be used to assist in assigning scores based on metrics on interaction in the search domain. Other embodiments are described and claimed.

Abstract: In some embodiments, the disclosed subject matter involves techniques for generating personalized query suggestions for a user in a specific subject or application domain that are ranked using confidence levels and contextual scoring. Partial query strings may be parsed for literal matching and be processed for spell checks, acronym expansion, and other expansion and rewriting of the partial query to a known possible query suggestion. Possible query suggestions are weighted using global feature metrics and personalized metrics. Various weighting, confidence levels and merging based on scoring may be used to rank the suggestions. A machine learning model may be used to assist in assigning scores based on metrics on interaction in the search domain. Other embodiments are described and claimed.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system identifies, based on search parameters received from a client device, a target company identified in the search parameters. The search parameters include a search term comprising at least two keywords, a first one of the at least two keyword identifying the target company, and the second one of the at least two keywords identifying an employment position. The search system identifies a second company based on a set of peer scores indicating a probability of employees transitioning between companies. The peer scores are calculated based on historical movement data indicating employee transitions between companies. The search system generates an expanded search term comprising a new keyword identifying the second company and the second keyword identifying the employment position.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for expanding search queries. A search system executes a search query based on a search term and the geographic indicator. In response to determining that a number of the search results is less than a threshold number, the search system determines, based on historical search logs from other users in the first geographic region, a likelihood value indicating a likelihood that the other users in the first geographic region expanded the geographic region of their search queries. The search system compares the likelihood value to a threshold likelihood value, and determines, based on the comparison, that the likelihood value meets or exceeds the threshold likelihood value. The search system then executes an expanded search based on the search term and an expanded geographic indicator that encompasses the first geographic region.