Microsoft Technology Licensing, LLC

Linkedin Corporation

Dhruv Arya

Ganesh Venkataraman

Shakti Dhirendraji Sinha

Saurabh Kataria

Aman Grover

Viet Thuc Ha

Liang Zhang

Deepak Agarwal

Ada Cheuk Ying Yu

Benjamin Hoan Le

Swanand Wakankar

Jia Li

Satya Kanduri

Kimberly Faith McManus

Abhimanyu Lad

Yongwoo Noh

Lin Guo

Ankan Saha

Huichao Xue

Kevin Kao

Shahdad Irajpour

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.

Expanding search queries

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.

Search query and job title proximity computation via word embedding

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.

Entity based query filtering

In an example embodiment, one or more query terms are obtained. Then, 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, with the standardized entity taxonomy comprising an entity identification for each of a plurality of different standardized entities. A confidence score is then calculated for the query term-standardized entity pair for the standardized entity that most closely matches the query term, and the query term is tagged with the entity identification corresponding to the standardized entity that most closely matches the query term and the calculated confidence score.

Entity based search retrieval and ranking

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.

Determining similarities among job titles to enhance job searching

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.

Semantic similarity for machine learned job posting result ranking model

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.

Recommending relevant positions

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.

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.

Semantic clustering based retrieval for candidate set expansion

In an example embodiment, usage information is used to calculate one or more individual document historical information-deficient metrics (IDHIDMs) by combining values for the one or more metrics from multiple documents within the document corpus that share an identical combination of entities of the one or more entity types. A search query is segmented into a plurality of segments, wherein at least one of the plurality of segments is tagged as a first entity type and at least one of the plurality of segments is tagged as a second entity type. At least one for a combination of entities matching the tagged segments is used to rank one or more retrieved documents responsive to the query.

Entity-faceted historical click-through-rate

Method and system to generate multiple entity aware typeahead suggestions is provided. The system is configured to use multiple Finite State Transducers (FSTs) to examine an input string submitted by a user via a search box, and to generate one or more typeahead suggestions based on the results of the examination. Different FSTs are constructed with respect to strings identified as associated with different entity types. At least one of the typeahead suggestions includes a portion associated with one entity type and a portion associated with a different entity type.

Multiple entity aware typeahead in searches

In an example, a first hash function is performed on job posting features extracted from a job posting to obtain hashed job posting features. The hashed job posting features are stored in a forward-index corresponding to the job posting in the database. When a job search query is received from a first member of a social networking service, job search query features are extracted from the job search query and a second hash function is performed on the job search query features. The hashed job posting features and the hashed job search query features are fed to a job posting result ranking model trained via a machine learning algorithm to compare the hashed job posting features to the hashed job search query features to generate an application likelihood score indicating a likelihood that the first member will apply for a job corresponding to the job posting.

Hashing query and job posting features for improved machine learning model performance

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.

Personalized job search and recommendations using job seeker behavioral features

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.

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Sunnyvale, CA, United States of America

Dhruv Arya