Abstract: Various embodiments described herein provide for systems and methods for using a machine-learning model to rank job search results based on the similarity of the job title of each job search result and a job search query that produces the job search results. According to some embodiments, the machine-learning model comprises a word-embedding machine-learning model that maps a word to a vector.

Abstract: Various embodiments described herein provide for systems and methods for using a machine-learning model to rank job search results based on the similarity of the job title of each job search result and a job search query that produces the job search results. According to some embodiments, the machine-learning model comprises a word-embedding machine-learning model that maps a word to a vector.

Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size. Items are distributed based on similarities identified with matrix factorization. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.

Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size, items are distributed based on similarities identified with matrix factorization. A matrix used in the matrix factorization is customized based on the recommendation type. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.

Abstract: In an example embodiment, a machine learning algorithm is used to train a. deep semantic similarity neural network to output a semantic similarity score between a candidate job search query and a candidate job search result. This semantic similarity score can then be used in a ranking phase to rank job search results in response to a first job search query.

Abstract: In an example embodiment, a machine learning algorithm is used to train a query-based deep semantic similarity neural network to output a query context vector in a vector space that includes both query context vectors and document context vectors. Both the query context vectors and document context vectors are clustered using a clustering algorithm. When an input search query is obtained, the input search query is also passed into the query-based deep semantic similarity neural network and its output document context vector assigned to a first cluster based on the clustering algorithm. Documents within the first cluster are then retrieved in response to the input search query.

Abstract: In an example embodiment, one or more query terms are obtained. For each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term. A confidence score is calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term. In response to a determination that the confidence score transgresses a threshold, the query term is associated with an entity identification corresponding to the standardized entity that most closely matches the query term. One or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification are obtained. The one or more query rewriting rules are executed to rewrite the first query such that the rewritten query, when performed on a data source, returns fewer search results than the first query would have.

Abstract: Various embodiments described herein provide for systems and methods for using a machine-learning model to rank job search results based on the similarity of the job title of each job search result and a job search query that produces the job search results. According to some embodiments, the machine-learning model comprises a word-embedding machine-learning model that maps a word to a vector.

Abstract: In an example embodiment, one or more query terms are obtained. For each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term. A confidence score is calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term. In response to a determination that the confidence score transgresses a threshold, the query term is associated with an entity identification corresponding to the standardized entity that most closely matches the query term. One or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification are obtained. The one or more query rewriting rules are executed to rewrite the first query such that the rewritten query, when performed on a data source, returns fewer search results than the first query would have.

Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size, items are distributed based on similarities identified with matrix factorization. A matrix used in the matrix factorization is customized based on the recommendation type. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.

Abstract: The disclosed subject matter involves identifying clusters and segments of a population of data for use in a recommendation service. Clusters of members or items are formed, where the clusters, or partitions are close to being equal in size. Items are distributed based on similarities identified with matrix factorization. The items are formed into clusters based on the similarities and the clusters are used in training of a generalized linear mixed model treating the clusters as random-level effects. The trained model may be used in the recommendation service. Other embodiments are described and claimed.

Abstract: A trained search system can be configured to retrieve a candidate subset of results, where the trained search system uses data extracted from a machine learning scheme. The machine learning scheme can be trained to identify results that are ranked by a computationally expensive algorithm, such as a ranking algorithm. When a query is received, the trained search system can he used to retrieve results instead of applying the computationally expensive ranking algorithm.

Abstract: Disclosed in some examples are methods, systems, and machine-readable mediums which provide for a personalized expertise searching. When a user of the social networking service enters a search query, the system determines if the user is searching for members who possess a particular skill. If the user is searching for members who possess a particular skill, the search results are post-processed by personalizing the search results using one or more machine-learning models which utilize one or more observed features about the user that enters the query, the skills of the members of the social networking service, and the query itself. In some examples, the system may utilize multiple machine-learning models in multiple passes to fine tune the relevance of the search results and to ensure that the post-processing returns search results in a timely manner.

Abstract: Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method identifies job titles of members in a social network and performs, utilizing a machine-learning program, semantic analysis of the job titles to identify similarity coefficients among the job titles. The machine-learning program utilizes social network data to identify the similarity coefficients. Further, the method includes an operation for receiving a job search query, from a first member, including a query job title, and for expanding the job search query with job titles that are similar to the query job title. The method further includes operations for executing the expanded job search query to generate a plurality of job results, and for causing presentation on a display of one or more of the top job results.

Abstract: Methods, systems, and computer programs are presented for expanding a job search that includes an industry by adding other similar industries. A method accesses, by a social networking server, a plurality of job applications, with each job application being submitted by a member for a job in a company, the member and the job having a respective industry from a plurality of industries. Semantic analysis of the job applications is performed by a machine-learning program to identify similarity coefficients among the plurality of industries. A job search query is received from a first member, the job search query including a query industry, and the job search query is expanded with industries that are similar to the query industry. The social networking server executes the expanded job search query to generate a plurality of job results. Presentation is provided on a display of one or more of the top job results.

Abstract: This disclosure relates to systems and methods for searching names using name clusters. A method includes receiving names, generating a plurality of phonetic cluster identifiers, forming a plurality of name clusters by grouping the names having an equivalent cluster id, removing names from the respective name clusters that differ from a root name by more than either a particular spelling of a phonetic sound or a specific member's reformulation according to a reformulation dictionary, and suggesting one or more names by generating a phonetic cluster id for the received name using the database of phonetic associations and returning names found in the name cluster that matches the phonetic cluster id.

Abstract: A user submits a job search query in an online social networking system. The online social networking system calculates a score based on the similarity between the job search query and the profile of the user. When the score transgresses a threshold, the job search query is enhanced by adding data from the profile of the user to the job search query. The job search query is then used to search for, identify, and display jobs in the online social networking system.

Abstract: This disclosure relates to systems and methods for increasing member engagement at an online social network. In one example, a method includes receiving user input that includes an incomplete sequence of terms, retrieving two or more suggestions to expand the sequence of terms, converting, for each of the suggestions, the sequence of terms to a respective sequence of segments using the suggestion, scoring the suggestions according to a frequency of how the sequence of segments are found in a corpus of segments, and recommending a highest scoring suggestion to complete the sequence of terms.

Abstract: In an example embodiment, one or more query terms are obtained. For each of the one or more query terms, a standardized entity taxonomy is searched to locate a standardized entity that most closely matches the query term. A confidence score is calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term. In response to a determination that the confidence score transgresses a threshold, the query term is associated with an entity identification corresponding to the standardized entity that most closely matches the query term. One or more query rewriting rules corresponding to an entity type of the standardized entity having the entity identification are obtained. The one or more query rewriting rules are executed to rewrite the first query such that the rewritten query, when performed on a data source, returns fewer search results than the first query would have.

Abstract: A system and method for personalized search based on searcher interest may include obtaining a search term from a member of a social network at a user device via the network interface. An initial result may be generated based on the search term, including a first group of content items from a social network and stored in a content database, the content items including member profiles of members of the social network. Each of the content items of the first group may be ranked based on information from an activity database, the activity database storing the information related to the social network, the activities including interactions with search results that include ones of the member profiles. A second group of the content items may be displayed, including at least some of the first group of the content items, based on the rank of the first group of the content items.