GB2556406A

GB2556406A - System and method of aggregating and analyzing diverse candidate data at a networked computer system and providing the data through a networked agent

Info

Publication number: GB2556406A
Application number: GB1714823.0A
Authority: GB
Inventors: Renee La Londe Norine; Kitchener Walters Frederick; Lau Brion; Kotha Nagaraju
Original assignee: Italent Corp
Current assignee: Italent Corp
Priority date: 2016-09-16
Filing date: 2017-09-14
Publication date: 2018-05-30
Also published as: GB201714823D0

Abstract

Automated matching of candidates with job listings handled by hiring managers, where a computer obtains a job profile comprising job parameters specifying a position to be filled, a plurality of candidate profiles including candidate traits, compares the two and generates a first match score. If the first match score passes a threshold match score a message is transmitted to the hiring manager and, if the hiring manager responds with an indication of interest, an interview is scheduled. Candidates may be ranked based on the match score. The job profile may be based on a template. Values may be associated with job parameters and candidate traits to generate the score and may be weighted, and different profiles may be compared to adjust the values, such that similar traits or parameters have the same value across all profiles. Salaries associated with jobs may be adjusted based on comparisons with other job profiles. The system may compare successful candidates hired by the same manager or a hiring manager profile to determine traits particularly important to that manager. Multi-media data may be aggregated, e.g. image, audio and video. Hiring needs may be anticipated.

Description

(71) Applicant(s):

Italent Corporation

Suite 20, 27 Devine Street, San Jose, California 95110, United States of America (51) INT CL:

G06Q 10/10 (2012.01) (56) Documents Cited:

None (58) Field of Search:

Other: No search performed: Section 17(5)(b) (72) Inventor(s):

Norine Renee La Londe Frederick Kitchener Walters Brion Lau

Nagaraju Kotha (74) Agent and/or Address for Service:

WP Thompson

138 Fetter Lane, LONDON, EC4A 1BT, United Kingdom

Title of the Invention: System and method of aggregating and analyzing diverse candidate data at a networked computer system and providing the data through a networked agent Abstract Title: Automated matching of candidates with job listings

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Application No. GB1714823.0

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Date :5 March 2018

Intellectual

Property

Office

The following terms are registered trade marks and should be read as such wherever they occur in this document:

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Intellectual Property Office is an operating name of the Patent Office www.gov.uk/ipo

SYSTEM AND METHOD OF AGGREGATING AND ANALYZING DIVERSE CANDIDATE DATA AT A NETWORKED COMPUTER SYSTEM. AND PROVIDING THE DATA THROUGH A NETWORKED AGENT

CROSS-REFERENCE TO RELATED APPLICATIONS [001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 62/395,824, filed September 36, 2016, and U.S. Provisional Patent Application Serial No. 62/395,843, filed September 16, 2016, which are hereby incorporated herein by reference in their entirety,

FIELD OF THE INVENTION [002] The invention relates to a system and method of aggregating and analyzing diverse candidate data at a networked computer system and providing the aggregated and analyzed candidate data through a networked agent.

BACKGROUND OF THE INVENTION [003] Gathering information on and screening candidates is conventionally inefficient and subject to human error or overly subjective decisions, Por example, information relating to candidates may be available from diverse sources of information and wading through several candidates can be daunting and highly subjective. Furthermore, it is difficult to identify those candidates that would be a proper match for a given employer or hiring manager. Moreover, oftentimes hiring needs are not systematically anticipated in order to facilitate efficient search and acquisition of appropriate candidates. These and other problems exist with conventional candidate screening and reviewing systems.

SUMMARY OF THE INVENTION [004] The invention described herein relates to a system and method of anticipating hiring needs and aggregating and analyzing diverse candidate data at a server and providing the aggregated and analyzed candidate data through a networked agent, according to an implementation of the invention. Diverse candidate data, which may include text, multi-media.

(e.g., image, audio, video), and/or other types of data relating to a candidate may be aggregated and analyzed at a networked computer system. The aggregated and analyzed data may be provided to a networked agent. The networked agent may operate at a client device. The networked agent may cause the client device to provide differential displays depending on its target audience, and receive inputs that are provided to the networked computer system. Based on the inputs, the networked computer system may, depending on the target audience that provided the inputs, respond with further information and/or perform further analysis on the aggregated data.

[005] The system and method may be used in various contexts, For example, and without limitation, the system and method may be used for aggregating diverse candidate data, generating computational models that match candidates using the diverse candidate data, generating computational models that anticipates a need for candidates, facilitating communication among networked agents (e.g., mobile applications) operating at different client devices each associated with a particular type of Intended audience (e.g., hiring managers, recruiters, candidates, etc.), and facilitating communication between the networked agents and networked computer system. The networked computer system may operate in conjunction with the networked agents to facilitate the foregoing and other implementations, which will be apparent based on the disclosure herein.

[006] To recommend candidates, the computer system may obtain a job profile comprising a plurality of job parameters that specify a position to be filled. In some instances, the job profile job profile is based on a job template or historical job data captured on the networked computer system, including at least some of the plurality of job parameters.

[007] The computer system may obtain a plurality of candidate profiles. The plurality of candidate profiles may include a first candidate profile comprising a plurality of candidate traits that describe a first candidate. The candidate traits may be obtained based on information from individual candidates who have registered to use the system, from hiring managers, from diverse networked sources (e.g., social sites, third party information sites, etc.), and/or other sources of information relating to candidates.

[008] The computer system may compare a value of at least a first job parameter from the job profile with a value of at least a first candidate trait from the first candidate profile and may generate a first match score based on the comparison. The computer system may determine that the first match score passes a threshold match score. The computer system may cause a message to be transmitted to a mobile application executing at a user device of the hiring manager. The message may indicate that the first candidate has been matched to the job listing responsive to the determination that the first match score passes a threshold match score. The hiring manager may then view the first candidate profile information within the mobile application.

[009] The computer system may receive, from the mobile application, an indication of interest by the hiring manager in the first candidate. The computer system may cause an interview to be scheduled between the hiring manager and the first candidate. For example, to cause an interview to be schedule, the computer system may transmit a message to the hiring manager and/or the first candidate that an interview should be scheduled. The hiring manager and/or the first candidate may arrange such interview, and the computer system may store such scheduling in a system-controlled calendar. Alternatively or additionally, the computer system may access a calendar associated with the hiring manager and/or the first candidate and add an interview calendar item to the calendar.

[010] In some implementations, the system may anticipate hiring needs before a hiring manager makes a job requisition request. For example, the computer system may obtain one or more predictive variables associated with the hiring manager. The one or more predictive variables may include user activity information that indicates a level of activity of the hiring manager with respect to the computer system, hiring behavior and patterns based on candidates previously accepted or declined, feedback information provided by the hiring manager for a team member that was previously a candidate and is an employee that was hired by the hiring manager (in which the feedback information may indicate that the team member may be terminated in which case a job listing for the team member’s replacement may be needed), date information that may indicate seasonal hiring trends, and/or other types of information, Based on this information the system may proactively recommend candidates for the hiring manager to review even if no active job requisition has been opened by the manager yet.

[011] The computer system may determine, prior to receipt of a requisition for a particular job listing, that the particular job listing should be opened based on the one or more predictive variables. The computer system may generate a job listing responsive to the determination that the job listing should be opened. Alternatively or additionally, the computer system may generate a job profile comprising a plurality of job parameters that specify a position to be filled.

[012] The computer system may provide the job listing to the hiring manager. Alternatively or additionally, the computer system may identify one or more candidates (which may he matched as described herein) that may be suitable for the job listing and provide the one or more candidates (i.e., information relating to the candidates such as information from their candidate profiles) to the hiring manager. In this manner, the computer system may anticipate hiring needs of the hiring manager before the hiring manager requisitions to open a job listing.

[013] These and other objects, features, and characteristics of the system and/or method disclosed herein, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description and the appended claims with reference to the accompanying drawings, all of which form a part of this specification, wherein like reference numerals designate corresponding parts in the various figures. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. As used in the specification and in the claims, the singular form of “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise,

BRIEF DESCRIPTION OF THE DRAWINGS [014] FIG, 1 illustrates a system for aggregating and analyzing diverse candidate data at a server and providing the aggregated and analyzed candidate data through a networked agent, according to an implementation of the invention.

[015] FIG. 2 depicts a recruiting (i.e. staffing or corporate recruiting) management backend, according to an implementation of the invention.

[016] FIG, 3 depicts a recruiting (i.e. staffing or corporate recruiting) management client application, according to an implementation of the invention.

[017] FIG. 4 illustrates a flow diagram of a process for matching candidates with job listings, according to an implementation of the invention, [018] FIG. 5 illustrates a flow diagram of a process for anticipating a need to open a position, and proactively recommending a job listing before a requisition for the job listing is received, according to an implementation of the invention, [019] FIG. 6 illustrates a hiring manager user interface, according to an implementation of the invention, [020] FIG. 7 illustrates a user interface for listing team members, according to an implementation of the invention.

[021] FIG. 8 illustrates a user interface for viewing a team member, according to an implementation of the invention.

[022] FIG. 9 illustrates a user interface for viewing open positions, according to an implementation of the invention.

[023] FIG. 10 illustrates a user interface for viewing detailed information for an open position, according to an implementation of the invention.

DETAILED DESCRIPTION OF THE INVENTION [024] The invention described herein relates to a system and method of aggregating and analyzing diverse candidate data at a server and providing the aggregated and analyzed candidate data through a networked agent, according to an implementation of the invention. Diverse candidate data, which may include text, multi-media (e.g,, image, audio, video), and/or other types of data relating to a candidate may be aggregated and analyzed at a. networked computer system. The aggregated and analyzed data may be provided to a networked agent. The networked agent may operate at a client device. The networked agent may cause the client device to provide differential displays depending on its target audience, and receive inputs that are provided to the networked computer system. Based on the inputs, the networked computer system may, depending on the target audience that provided the inputs, respond with further information and/or perform further analysis on the aggregated data, [025] The system and method may be used in various contexts. For example, and without limitation, the system and method may be used for aggregating diverse candidate data, generating computational models that match candidates using the diverse candidate data, generating computational models that anticipates a need for candidates, facilitating communication among networked agents operating at different client devices each associated with a particular type of intended audience, and facilitating communication between the networked agents and networked computer system. The networked computer system may operate in conjunction with the networked agents to facilitate the foregoing and other implementations, which will be apparent based on the disclosure herein.

[026] Exemplary System Architecture [027] FIG. i illustrates a system 100 for aggregating and analyzing diverse candidate data at a server and providing the aggregated and analyzed candidate data through a networked agent. In one implementation, system 300 may include a profile database 301, a job database 103, a template database 105, a feedback database 107, a computer system 110, a client device 140, one or more third party social network sites 150, one or more third party data sources 160, and/or other components, [028] In an implementation, profile database 101 may be configured to store candidate profiles, employer profiles, employee profiles, hiring manager profiles, recruiter profiles, and/or other types of information.

[029] In an implementation, a candidate profile may include various candidate traits that describe a candidate. Candidate traits may include, without limitation, previous work experience (e.g., special consideration may be given to candidates with relevant work experience or who have previously worked at a company offering a position and may potentially return to the company, etc.), expertise or specialized skills, demographic information, images (e.g,, personal photos), link to social media accounts or profiles (e.g. Linkedin), a resume document, feedback from users (existing/former co-workers, existing/former managers), personal or professional references, school transcripts, professional interests (e.g., particular field of interest), personal interests (e.g., hobbies, music preferences, books read/preferred, favorite movies or television shows, sports teams, and extracurricular activities, etc,), salary requirements, volunteer/activist work, awards achieved, inventor on patents and/or other information relating to a candidate. As used herein, a “candidate” refers to a jobseeker, who may he currently seeking a job or who may have been hired on a temporary or permanent bases. A “job listing” refers to a job to be filled by a candidate, either on a temporary or permanent basis.

[030] In an implementation, an employer profile may include information relating to an employer. Such information may include, without limitation, information from or documents relating to financial filings, employer name, size, revenues, industries, locations, competitors, key leaders / executives, current & historical hiring needs and/or patterns, salary information or other relevant information obtained from third party sources (i.e. Giassdoor.com), feedback from users, and/or other information relating to an employer.

[031] In an implementation, a hiring manager profile may include hiring manager traits such as, without limitation, air employer for whom the hiring manager works, previous job listings, current job listings, comments or notes from recruiters with which the hiring manager works, a hiring style (e.g., preference to meet with many (or few) candidates), hiring patterns (e.g. budget amount, timing/seasonalitv, time to close or fill open positions), demographic information, images (e.g., personal photos), social media accounts, previous work experience, skills, feedback from users, professional interests (e.g., particular field of interest), personal interests, and/or other information relating to a hiring manager.

[032] In an implementation, a recruiter profile may include, without limitation, comments or notes from an employer for whom the hiring manager works, previous job listings, accounts recruiter is assigned to, performance metrics (i.e. for sourcing candidates and filling job listings), current job listings, hiring managers with whom the recruiter works, a hiring style (e.g., preference to meet with many (or few) candidates), demographic information, images (e.g., personal photos), social media accounts, previous work experience, skills, feedback from users, professional interests (e.g., particular field of interest), personal interests, and/or other information relating to a recruiter.

[033] It is noted that the candidate profile, hiring manager profile, and/or the recruiter profile may each Include similar types of information. For example, a given trait such as hobbies may be stored in: a candidate profile (in which case the candidate profile stores a candidate’s hobbies), a hiring manager profile (in which case the candidate profile stores a hiring manager’s hobbies), and a recruiter profile (in which ease the recruiter profile stores a recruiter’s hobbies). [034] In an implementation, job database 103 may be configured to store information relating to jobs (also referred to herein as “positions”) that are, were, or wall be available. For example, job database 103 may store job listings and associated job profiles. A job profile for a job listing may include a plurality of job parameters that specify a position to be filled. For instance, the job profile may be created in response to a job requisition from a hiring manager, The job parameters may include, without limitation, one or more skills required, compensation, one or more knowledge requirements, an educational requirement, an experience level (e.g., duration of time in a particular job), a particular field (e.g., vertical Industry), a job description (e.g., responsibilities), and/or other information that describes a job.

[035] In an implementation, template database 105 may be configured to store templates relating to job listings. For example, template database 105 may store job listing templates that ears be customized to create a job listing, [036] In an implementation, feedback database 107 may be configured to store feedback information that represents an assessment of a user of the system or a project (also known as statement of work, initiative, client engagement, etc.). The assessment may be made by another user of the system.

[037] Computer system 110 may be configured as a server (e.g., having one or more server blades, processors, etc.) and/or other device that is programmed to perform the operations described herein. Computer system 110 may include one or more processors 112 (also interchangeably referred to herein as processors 112, processor(s) 112, or processor 112 for convenience), one or more storage devices 114 (which may store a recruiting management backend 116), and/or other components. Processors 112 may be programmed by one or more computer program instructions, For example, processors 112 may be programmed by recruiting management backend 116 and/or other instructions. This computer system architecture may be set up on-premise (on-site) or hosted in a cloud environment where this system is housed at a remote data center via a third party service.

[038] Client device 140 may be configured as a personal computer (e.g., a desktop computer, a laptop computer, etc.), a smartphone, a tablet computing device, and/or other device that is programmed to execute a networked agent (such as recruiting management client application 146). Client device 140 may include one or more physical processors 142 programmed by computer program instructions. For example, processors 142 may be programmed by recruiting management client application 146 and/or other instructions.

[039] FIG. 2 illustrates recruiting management backend 116, according to an implementation of the invention. Recruiting management backend 116 may include instructions that program computer system 110. The instructions of recruiting management backend 116 may include, without limitation, a profile manager 202, a job requisition manager 204, a feedback manager 206, a matching engine 208, a scheduling and hiring engine 210, a communication manager 212, a referral manager 214, a proactive recommendation engine 216, an alert manager 218, an API 220, an app analytics engine 222, a proactive recommendation engine 224, and/or other instructions that program computer system 110 to perform various operations, each of which are described In greater detail herein. As used herein, for convenience, the various instructions will s

be described as performing an operation, when, in fact, the various instructions program the processors 112 (and therefore computer system 110) to perform the operation.

[040] FIG. 3 depicts a recruiting management client application 146, according to an implementation of the invention. Recruiting management client application 146 may include instructions that program client device 140. As used herein, for convenience, the various instructions will be described as performing an operation, when, in fact, the various instructions program the processors 142 (and therefore client device 140) to perform the operation.

[041] In some implementations, recruiting management client application 146 may provide different user interfaces and functions depending on a user accessing the application. For example, candidates may be provided with a candidate-specific set of U/Is and functions, while a hiring manager may be provided with another set of U/Is and functions. In other implementations, a specific recruiting management client application 146 may be made available to specific types of users. For instance, a specific recruiting management client application 146 may be made available to candidates while another recruiting management client application 146 may be made available to hiring managers.

[042] Referring to FIGS. 2 and 3. computer system i 10 and client device 1.40 (as programmed by instructions described below) may communicate with one another via a network 102. In some instances, the various components described herein may communicate with one another via a data service such as, for example, a webservice, a SOAP service, and/or other type of service, which may be hosted by computer system 110, For example, recruiting management client application 146 (which programs client device 140) may communicate with recruiting management backend 115 (which programs computer system 110) via a webservice and appropriate data protocols and formats. It is noted that the various Uls described in the examples that follow may he, but are not necessarily, discreet Uls. For example, one portion of a first UI described may be combined with another portion of a second UI. Various combinations of UI features may be used.

[043] Profile Manager 202 and Profile UI Manager 302 [044] In an implementation, profile manager 202 may generate and update profiles used by the system. For example, profile manager 202 may manage candidate profiles, employer profiles, hiring manager profiles, recruiter profiles, job profiles, and/or other profiles. Profile manager 202 may store and access the various profiles from the profile databases (101, 103, 105, 107) described herein.

[045] In an implementation, profile manager 202 may store, in one or more of the databases described herein, associations between different profiles. This allows the system to maintain relationships between users, jobs, and employers. For example, a recruiter may be associated with a set of employers with which the recruiter works. In this example, a recruiter profile may be stored in association with an employer profile. For instance, a database link may link the recruiter profile with the employer profile. Based on the stored association, profile manager 202 may query one or more of the profile databases to obtain a listing of employers with which a recruiter works.

[048] In another example, a hiring manager may be associated with assigned consultants that are on the hiring manager’s team or who are engaged in a given project that the hiring manager is hiring. Other associations among different profiles may be stored as well.

[047] in an implementation, profile UI manager 302 may generate and provide a profile Ui that includes one or more profile UI display options. As used herein throughout, a “display option” may include a user interface component that is configured to receive a user input. Examples of a display option include, without limitation, a text input, a button input, a checkbox, a radio input, a selectable list option, a selectable link, a file browser, and/or other type of input that can be configured to receive a user input. A display option may be associated with a soft user interface (e.g., a display option that is interacted with via a software input member such as a graphical button depicted on a touchscreen) and/or a hard user interface (e.g., a display option that is interacted with via a hardware input member suds as a physical rocker switch).

[048] in an implementation, profile UI manager 302 may generate profile UIs for display to a user. For example, and without limitation, profile IT manager 302 may generate a team view UI, a candidate view UI. an open job listings view UI, and/or other UIs relating to profile information stored by system 100. Such UIs may be generated on demand based on a request from a user. For example, profile UI manager 302 may generate a team view display option that, when selected, causes the team view Ui to be generated and displayed to the user via a display device. To generate the team view UI, profile UI manager 302 may identify the requesting user (e.g., a hiring manager) and request profiles for candidates that are associated with the requesting user. For instance, profile UI manager 302 may transmit a request, to profile manager 202 that includes identifying information for the requesting user, in response, profile manager 202 may to provide, and profile Ό1 manager 302 may receive, profile information for candidates that are on the requesting user’s team. Other filters may be applied as well (such as particular team identifications if the requesting user has multiple teams).

[049] The team view display may itself include display options that allow the requesting user to navigate different team member profiles. For instance, the display options may cause profile UI manager 302 to display details regarding a selected candidate profile, such as a candidate view display.

[050] The candidate view display may include a portion or all of the information stored in a candidate profile (e.g., photograph, experiences, expertise, location, etc.). The candidate view display is not to be confused with the candidate personalization UI, described below, which allows a user to personalize his profile. Profile UI manager 302 may generate the candidate view display in a manner similar to generating a team view display. The candidate view display may include display option that cause profile UI manager 302 to transmit a communication (e.g,, SMS text message, phone call, etc.) to the candidate, collect feedback on the candidate, and/or perform other functions with respect to the candidate described herein. The open job list view may include a listing of open jobs being handled by a hiring manager and/or organization.

[051] The recruiter view display may provide a recruiter with a status feed showing the most recent RFQs, integration with workflow tracking, view' of candidate profiles, integration with job boards to post out an opportunity, integration with employer referral systems, etc.

[052] The candidate personalization UI may provide display options for displaying candidate profiles, updating their own candidate profiles, provide feedback on the hiring or job, submit referrals, etc. Other types of views may be similarly displayed.

[053] Job Requisition Manager 204 and Job Requisition UI Manager 304 [054] In an implementation, job requisition manager 204 may manage job requisitions and related jobs handled by system 100. For example, job requisition manager 204 may obtain and store (in job database 103) request for quote information, job profile information, statement of work information, and/or other information related to a job managed by system 100. Some or all of the foregoing information may be stored (and obtained from) third party databases as well (not illustrated In the figures).

[055] In some implementations, job requisition manager 204 may obtain and store job templates. A job template is a reusable set of information from which a job listing is created. As it such, a job template may allow hiring managers and others to efficiently create new'job listings without having to repeatedly supply information that is duplicated across different job listings. In some instances, a job template may include variables that may be customized for a given job listing. The variables may be manually or automatically filled. For example, and without limitation, a job template may have a variable for the location of a job. The location may be automatically filled based on a location of a hiring manager, or may be manually filled based on input from the hiring manager. Other types of variables may be similarly automatically filled based on information of which job requisition manager 204 is aware or manually filled based on user input.

[056] In some instances, job requisition manager 204 may provide a job template to be edited by a user, such as a hiring manager. Such edits may include additions to, removal from, and updates to some or all of a job template. Job requisition manager 204 may receive the edits and create a job listing based on the job template and any edits. In this manner, a job listing may be customized based on a job template.

[057] hi some instances, a job template may be created from a pre-existing job listing or a preexisting job template. For instance, job requisition manager 204 may provide a job listing to be edited by a user so that a new job template can be created based on the job listing and edits to the job listing. In another example, job requisition manager 204 may provide a job template to be edited by a user so that a new job template can be created based on the job template and edits to the job template. Furthermore, a first portion of a first job listing or template may be combined with a second portion of a second job listing or template to form a new job template. In this manner, job requisition manager 204 facilitates mixing and matching job listings and/or templates to create new job templates (or new job listings).

[658] Once a job template has been saved, job requisition manager 204 may share the job template with other users. For instance, a hiring manager may request to share a job template with another hiring manager, who may then use the job template for her job listings.

[659] In an implementation, job requisition UI manager 304 may generate a job requisition UI having display options that receive one or more job parameters for a job fisting. The job parameters may include, without limitation, a job title, a salary (or salary range), a location of the job, an employer name, a skills required or preferred, experience required or preferred, an education level required or preferred, a statement of work, and/or other job related description.

A hiring manager or others may use the job requisition UI to input the job parameters. In some instances, the job requisition UJ may include display options for receiving a request for quote, a job template, and/or other information, job requisition UI manager 304 may communicate the inputs received via the job requisition UI to job requisition manager 204 for processing.

[060] In some implementations, the job requisition UI may Include display options for requesting a status of an RFQ. In response to the request, job requisition manager 204 may provide, and job requisition UI manager 304 may display, a status of the RFQ. The status of the RFQ may include work performed by a recruiter in fulfilling the RFQ, including any workflow tasks related to RFQ fulfillment.

[061] In some implementations, the job requisition UI may include display options for escalating an RFQ. For example, if a hiring manager is not satisfied with the work performed to date to service the RFQ, an escalation display option may be selected. Upon selection of the escalation display option, job requisition UI manager 304 may communicate information indicating such selection to job requisition manager 204, which then identifies an appropriate user to contact in order to resolve the request. An appropriate person may Include, for example, a manager of the recruiter handling the RFQ.

[062] Feedback Manager 206 and Feedback UI manager 306 [063] In an implementation, feedback manager 206 may obtain, store (e.g., using feedback database 107), analyze, and provide feedback information relating to a user. The feedback information may relate to user’s feedback of another user. For example, a hiring manager may provide feedback for a candidate or a recruiter. A candidate may provide feedback relating to her experiences with a hiring manager, a recruiter, or an employer generally. Other users may provide other types of feedback as well.

[064] The feedback may be provided through a feedback UI generated by feedback UI manager 306. For example, the feedback UI may include display options for receiving the feedback, which may be in the form of text, audio, video, and/or other format. In some instances, the feedback may be in the form of a star rating system, in which a user provides a number of stars (or other rating unit up to a maximum) indicative of the feedback. The feedback may be categorized such that different categories of feedback exist. In these implementations, an overall feedback score may be generated based on the category feedback. Alternatively or additionally, the feedback may be in the form of spoken words via an audio or video recording made through a microphone or camera device of a user device (e.g., a client device 140). In these instances, the feedback may be stored as an audio, video, or other multi-media file. In some of these instances, the feedback may be analyzed using speech-to-text processors that automatically recognize spoken words and convert them to text. Alternatively or additionally, the multi-media file may be associated with the entity to which the feedback relates so that the multi-media file may be played back by a user who is viewing the entity’s profile or is otherwise accessing feedback information for the entity.

[065] Feedback UI manager 306 may provide the feedback upon demand from a user. For example, a hiring manager may request feedback (e.g., from other hiring managers) relating to a candidate, in another example, a candidate may request feedback (e.g,, from other candidates) regarding a particular hiring manager or employer.

[066] In some implementations, feedback manager 204 may automatically associate feedback for a given entity (e.g., user or employer) with that entity. For example, feedback manager 204 may automatically add feedback for a candidate into the candidate’s profile. Likewise, feedback manager 204 may automatically add feedback for a hiring manager into the hiring manager’s profile. Other feedback for other entities may similarly be added to its profile.

[067] As will be described, the stored feedback information may be analyzed for matching and/or for other purposes.

[068] Matching Engine 208 and Matching Ui Manager 308 [089] in an implementation, matching engine 208 may identify a potential match between a candidate and a job listing and generate a match score for the potential match. A potential match refers to a potential matching of a candidate to fill a job listing requisitioned (which, as used herein, includes being handled) by a hiring manager, [070] The matching engine 208 may generate a match score for the potential match based on a comparison of match variables, A match variable may include, without limitation, information from a candidate profile, information from a hiring manager profile, information from a recruiter profile (e.g., for a recruiter with whom the hiring manager works), information from a job profile, information from an employer profile (for which the job relates), and/or other information used to assess a potential match. In other words, the computer system may use information from a candidate’s profile, a hiring manager’s profile, a job profile, an employer’s profile, etc. (and/or combination of information) to determine whether a candidate is a potential match for a given job specified by a job profile.

[071] For example, matching engine 208 may compare candidate traits from a candidate profile with job parameters from a job profile to determine whether the candidate is a good match for the job listing. Alternatively or additionally, matching engine 208 may compare the candidate traits with hiring manager traits from a hiring manager profile to determine whether the candidate is a good match for the job listing (which is being handled by the hiring manager). Other comparisons of information available to the system may be alternatively or additionally made as well.

[072] Because the match variables may include a wide range of information available from various profiles described herein, in some implementations, a given match variable may be assigned with a coefficient that indicates a level of importance, or weight, of the given match variable. The matching engine 208 may generate a match score based on the match variables and their corresponding coefficients. In this manner, the matching engine 208 may consider multiple variables, each of which may be assigned with varying degrees of importance, when assessing the suitability of a potential match.

[073] The tables below illustrate non-limiting examples of information used by matching engine 208 and how such information may be used by the matching engine to generate a match score for a candidate.

[074] Table 1 below illustrates non-limiting example of a candidate profile, which may be stored as a relational database table, a comma-separated value file, a key=value file, and/or other type of data structure.

Candidate Trait	Value
Education	Bachelor’s Degree
Salary Range	40,000-50,000
Location	San Francisco, Willing to Relocate
Hobbies	Reading, Hiking, Baseball
Previous Work Experience 3	Systems Engineer, TechCo inc.
...	...

J 5

Other Trait Values [Q75j Table 2 below illustrates a non-limiting example of a job profile, which may be stored as a relational database table, a comma-separated value file, a key^value file, and/or other type of data structure.

Job Parameter	Value
Education	Bachelor's Degree
Salary Ranee	45,000-55,000
Location	Los Angeles, CA
Skills	Microsoft Office 365, Salesforce CRM, email marketing
...	...
Other Job Parameters	Other Job Parameter Values

[076] Table 3 below illustrates a non-limiting example of a hiring manager profile, which may be stored as a relational database table, a comma-separated value file, a key=value file, and/or other type of data structure.

Hiring Manager Trait	Value
Education	Bachelor’s Degree
Hobbies	Reading, Hiking, Racketball
Previous Work Experience 1	HR Manager, TeehCo Inc.
Current Role	Director of Human Resources
	...
Other Traits	Other Trait Values

[077] As illustrated in Tables 1-3, information from various profiles may be encoded using tags that are common across different profiles so that comparison of their corresponding values may be made. For example, referring to Table I, a candidate trait of “Education” may indicate that the candidate has achieved a level of education specified in the “Value” column; referring to

Table 2, a job parameter of “Education” may indicate a minimum education level required for a job listing as specified in the “Value” column; referring to Table 3. a Hiring Manager Trait of “Education” may indicate that the hiring manager for a job listing has achieved a level of education specified in the “Value” column.

[078] Table 4 below illustrates a scoring matrix using information from the candidate profile, job profile, and hiring manager profile. Other types of information may be alternatively or additionally used. Furthermore, other types of scoring mechanisms and values may be used, as would be apparent based on the disclosure herein. As one example, although a higher match score relates to a better match, another scoring mechanism in which a lower match score relates to a better match may be used as well. Thus, “passing a threshold match score” merely is intended to mean that a threshold match score has been reached or crossed, rather than “being above” or “greater than.” As such, the various entries in Table 4 (and other Tables) are provided for illustrative purposes.

Match Variable	Candidate Trait	Job Parameter	Sub-score	Coefficient	Weigh t ed Score	Suh-
Education	Bachelor’s Degree	Bachelor’s Degree	1.0	5.0	5.0
Salary Range	40,000- 50,000	45,GOO- 55,000	1.0	5.0	5,0
Location	San Francisco, Willing to Relocate	Los Angeles, CA	0.6	2.0	1,2
...	...	...	...	...	...
Match Variable	Candidate Trait	Hiring Manager Trait	Sub-score	Coefficient	Weighted Score	Sub-
Education	Bachelor’s Degree	Bachelor’s Degree	1.0	0.1	0.1
Hobbies	Reading,	Reading,	0.7	0.2	1.4

Ϊ7

	Hiking, Baseball	Hiking, Racketball
Previous Work Experience 1	Systems Engineer, TechCo inc.	HR Manager, TechCo inc.	1,0	0.1	0.1
	...	...	...	...	...
	Match Score			12.8

[079] In the example illustrated by Table 4, matching engine 208 may compare one or more candidate traits with one or more job parameters for a job profile relating to a position for which a hiring manager is seeking to fill. Matching engine 208 may alternatively or additionally compare one or more candidate traits with one or more hiring manager traits as well. Each comparison represents a match variable, which relates to a category of information being compared, [080] For each match variable, matching engine 208 may generate a sub-score based on the comparison. For each sub-score, matching engine 208 may apply a coefficient (also referred to interchangeably herein as “weight”) to the sub-score to determine a weighted sub-score. Matching engine 208 may cumulate (e.g., add) all the weighted sub-scores to determine the match score. As is illustrated, a coefficient may increase or decrease a contribution of a given match variable to the overall match score. For instance, as illustrated, a higher coefficient will result in a given match variable having more of an impact on a match score than does a lower coefficient.

[081] In some implementations, the coefficient and/or the sub-score may be a negative value. If one of these are a negative value, then this will tend to decrease a match score (if a higher match score, as illustrated in the Tables, represents a better match than a lower match score). [082] To obtain the sub-score, matching engine 208 may normalize values to be equal to 1.0 such that a range from zero to 1 is used. Other values may be used as well. In the illustrated example, a sub-score I may represent a maximum match for the corresponding match variable. For instance, if a candidate’s education level matches with a required education level for a job, matching engine 208 may assign a sub-score of 1. if the education level does not match the

IS required education level, then matching engine 208 may assign a value of less than 1, depending on one or more sub-score assignment rules that specify how to assign a sub-score of less than 1: (including a fraction or zero) for non-matching comparisons.

[083] The foregoing sub-score assignment rules may be pre-specified and/or may be configurable by hiring managers, recruiters, system administrators or others. For example, subscore assignment a rule may state that if an education requirement is a PhD and a candidate has achieved a Bachelor’s degree, then a sub-score of zero should be assigned. On the other hand, the same or different sub-score assignment rule may specify that if education requirement is a PhD and the candidate has a Master’s Degree, then the sub-score of 0.2 should be assigned. Similar rules may guide the assignment of sub-scores for other types of match variables as well. [084] For certain types of match variables, there may be a list of values, which may be stored as an array. For example, a candidate may list a number of hobbies, which may be compared to the hobbies of another user (e.g., a hiring manager’s hobbies), Each of the candidate’s hobbies may be compared with each of the other user’s hobbies such that common interests are identified, The number of common interests may be used to assign a sub-score for that match variable.

[085] Other types of user (e.g,, candidate, hiring manager, recruiter, etc.) traits and job parameters may be compared as well, examples of which are described below.

[085] In an implementation, for example, matching engine 208 may compare candidate traits and hiring manager traits to determine similarities or differences between the candidate and the hiring manager. Such traits may be obtained from candidate and hiring manager profiles, from third party social sites 150, and/or third party data sources 160, It should be noted that information about a user (e.g., a candidate, a hiring manager, a recruiter, etc.) from third party social sites 150 and/or third party data sources 160 may be obtained automatically by the system and included in an appropriate profile (e.g., a candidate profile, a hiring manager profile, a recruiter profile, etc,). Matching engine 208 may use such similarities or differences to modify (e.g., increased or decreased, depending on the scoring mechanism) a match score for the candidate such that the candidate is more or less likely to be matched with a job listing. For instance, if the candidate and the hiring manager attended the same school (or belong to the same alumni group), then a positive correlation may be determined and cause a match score for the candidate such that the candidate is more likely to be matched, [087] In other non-limiting examples, if the candidate and the hiring manager worked for the same company in the past, share the same interests, and/or have other similarities or differences, then a match score for the candidate may be modified accordingly, In some instances, such similarities or differences may be highlighted by hiring 111 manager 310 so that the hiring manager is made aware of such information. For instance, when a hiring manager is reviewing potential candidates, one or more specific U1 elements may be used to call out specific matching interests for the hiring manager, in a summary and/or profile view.

[088] In some implementations, the matching engine 208 may use, without limitation, industry experience when generating a match score for a candidate. For instance, candidates that worked previously at a hiring manager’s company may get special consideration or review as they may understand the culture. If there is no such previous working experience, matching engine 208 may determine whether a candidate has relevant industry experience for a competitor, if not, matching engine 208 may determine whether the candidate has relevant experience in the industry, even if not a direct competitor.

[089] In some implementations, the matching engine 208 may fine-tune match scoring over time based on observed behavior of hiring managers and their hiring decisions or other actions. For example, based on hired candidates, interviewed candidates, rejected candidates, feedback information, and/or other information, the matching engine 208 may adjust coefficients for the match variables.

[090] In some implementations, a hiring manager may change a default value of a coefficient. For example, a first hiring manager may value a first set of match variables over others. A second hiring manager may value the other match variables over the first set of match variables. In some instances, a coefficient may be set such that the corresponding match variable does not contribute at ail to a match score. As such, determination of match scores may be customized for a given hiring manager (or may be determined based on a default set of rules or coefficient values), [091] Such adjustments may be negative adjustments such that a corresponding match variable is not considered at all or is considered less than other match variables. For example, a hiring manager may hire, select, or otherwise provide good feedback (“good” being defined as being a feedback value such as a star count passing a threshold value) for a first candidate for a job listing. The first candidate may have a skill in his profile that indicates he is proficient in Spanish. The hiring manager may also hire, select, or otherwise provide good feedback for (also referred to as simply “like” for convenience) a second candidate for the job listing, but the second candidate does not have a profile that indicates he is proficient in Spanish. After multiple instances of related observations, the matching engine 208 may determine that (at least for the particular job listing), the hiring manager does not care about Spanish proficiency, As such, the matching engine 208 may set a coefficient relating to Spanish proficiency (or language skills generally) to be zero such that language proficiency does not factor heavily (or at all) into a match score.

[092] The matching engine 208 may observe other patterns for machine-learning as well. For instance, the matching engine 208 may observe that the hiring manager tends to like candidates who have graduated from a particular school or has particular work experience. In these instances, the matching engine 208 may adjust the coefficient for match variables relating to education or prior work experience such that one or both of these match variables are considered more so than other match variables. Furthermore, the matching engine 208 may observe particular schools from which the hiring manager likes to hire. In this case, matching engine 208 may store a rule in association with the hiring manager and/or the job listing that a particular school is to be weighted higher than other schools. The rule may cause the coefficient to be adjusted for the school-related match variable (if the school-related match variable indicates that the candidate attended the particular school). The opposite rule may he stored as well, in which a non-liked school is to he weighted lower than other schools if the hiring manager tends to not like candidates from the non-liked school.

[093] Matching engine 208 may make such observations across a plurality of hiring managers to understand who they’ve hired in the past, and who they currently have on their team. Matching engine 208 may identify patterns, such as a given hiring manager’s preference to hire candidates with particular sets of traits (e.g., project management certifications that are local and have an average of 10 years of work experience). Other types of preferences that may be observed include, without limitation, expertise, education, gender (unconscious bias), ethnicity (bias or no bias), age (bias or no bias), geographical radius preference (local, national, telecommute OK, or no preference), etc. In some instances, matching engine 208 may monitor the accuracy of matches by individual hiring manager, by department/org, by company, and on average across the entire statistical base. This allows the system to establish thresholds for inspection and quality assurance.

[094] Monitoring Template and System Use to Generate Recommendations [095] In some implementations, matching engine 208 may monitor use of job templates and, more generally, hiring patterns over time. For instance, matching engine 208 may observe that a hiring manager fills positions (for job listings generated using a particular job template) with candidates having a specific set of one or more candidate traits and pay a certain salary (or salary range). These traits and salary (and/or other candidate information) may be stored in association with the job template, Matching engine 208 may identify candidates having the set of candidate traits, and may provide the identified candidate(s) to the hiring manager, [098] Matching engine 208 may collect job template and/or (more generally) system usage data over time and generate normalized job templates within a range to indicate whether the hiring manager’s job parameters are within a normalized range. For example, matching engine 208 may determine that, for a given job listing, certain job parameters are out of the norm (e.g., based on conventional statistical analyses such as analysis of Standard Deviation for a given job parameter) as compared to similar job listings. Job listings may be considered “similar” if one or more of their job parameters (e.g., job title, geographic location, industry, etc.) are shared in common. In a particular example, matching engine 208 may determine that, for a particular job listing for a position in a given locale (e.g., geographic location) and a given position, a salary provided for the position by the hiring manager is higher (or lower) than the norm. In this example, matching engine 208 may recommend decreasing (or increasing) the salary or salaryrange accordingly for the job listing. Alternatively, matching engine 208 may determine that the salary or salary range is commensurate with similar job listings so that the hiring manager is assured that the salary or salary range is within a normal range of similar job listings.

[097] The foregoing salary example is described by way of illustration and not. limitation. Other job parameters may be similarly analyzed (e.g., whether a particular education requirement is above, below, or substantially equal to the norm, whether a particular number of years of experience is above, below', or substantially equal to the norm, etc.).

[098] The matching engine 208 may store any of the foregoing machine-learned observations in association with a hiring manager generally. In these instances, the machine learned models maybe applied to all job listings for which the hiring manager has requisitioned. Alternatively, the matching engine 208 may store the foregoing machine-learned observations in association with a hiring manager and the particular job listing. In these instances, the machine-learned models may be applied to only the same job listing requisitioned by the hiring manager.

[099] In an implementation, the matching engine 208 may adjust any of the foregoing machinelearned observations based on the performance of resulting matches. For example, the matching engine 208 may monitor a match after the match has been recommended to a hiring manager. If the matched candidate is hired by the hiring manager, the matching engine 208 may determine that the match was successful. After a number of successful matches, the matching engine 208 may identify match variables that are common among the successful hires so as to emphasize those match variables (e.g., via coefficient adjustments) for future matches. On the other hand, if a match was unsuccessful (i.e,, did not result in a hire) the matching engine 208 may identify match variables that are common among the unsuccessful hires to identify those match variables that may have led to an unsuccessful outcome.

[0100] In an implementation, the matching engine 208 may continue to monitor hired candidates to determine whether they eventually performed the job well (e.g., via feedback information, termination indications, etc,). To the extent that no feedback or other outcome information is was provided or is available to the matching engine 208, the models may be left undisturbed. To the extent that feedback or outcome information has been determined, the matching engine 208 may further adjust the coefficients for the identified match variables based on whether a positive or negative outcome resulted. Over time, the matching engine 208 may continually adjust the coefficients based on new matches and subsequent observations, [0101] In an implementation, matching Ufmanager 208 may rank, sort, and provide the potential matches via a Ul. In some instances, each potential match may be displayed alongside their match score, in some instances, match variables contributing to the match score may be presented as well. In this manner, the user may be presented with information that indicates why a potential match was made and the match variables that contributed to such match. The matching Ul manager 208 may integrate with scheduling and hiring UI manager 310 to present possible matches to the user.

[0102] Scheduling and Hiring Engine 210 and Scheduling and Hiring Ul Manager 310 [0103] in an implementation, scheduling and hiring engine 210 may schedule interviews or other meetings between users and facilitate hiring of candidates. For instance, scheduling and hiring engine 210 may schedule a meeting between a hiring manager and a candidate for an interview, a follow-up interview, and/or other meeting. In another example, scheduling and hiring engine

210 may schedule a meeting between a hiring manager and a recruiter. Meetings between other users may be scheduled as well.

[0104] To facilitate scheduling a meeting, scheduling and hiring Ui manager 310 may generate a scheduling UI that provides display options for navigating users with whom to meet, input times to meet (“times” including a date and/or time of day), input location to meet, information indicating a topic for the meeting (e.g., an interview for a particular job listing, which may be identified), and/or other information for scheduling a meeting, [0105] In an implementation, scheduling and hiring UI manager 310 may generate a UI with display options to schedule meetings. The display options may include inputs for a meeting time (e.g., a date and/or time), a meeting location, and/or other meeting information. The display options may include gesture-based display options in which a user may input a gesture (e.g., a swipe left, right, up, down, etc.) to scroll through the candidates. The candidates may include potential matches automatically determined by matching engine 208 and/or candidates selected by a recruiter, [0106] When scheduling information (e.g., identification of a candidate with whom to meet, preferred times of the meeting, etc.) is received, scheduling and hiring engine 210 may cause a message that includes the scheduling information to be transmitted to the recruiter. The recruiter may then use the system (or third party communication) to contact the candidate. Alternatively or additionally, scheduling and hiring engine 2.10 may cause a message that includes the scheduling information to be transmitted to the candidate.

[0107] In either scenario, in implementations where the candidate is contacted using system 100, the candidate may have an option to decline or accept an invitation to meet. In some instances, the candidate may suggest an alternative time to meet, in which case scheduling and hiring engine 210 may communicate the alternative time to the hiring manager (and/or the recruiter).

[0108] Once a time has been agreed upon (whether an alternative time was proposed or not), scheduling and hiring engine 210 may schedule the meeting and, in some implementations, cause a calendar appointment to be made on the hiring manager’s calendar, the recruiter’s calendar, and/or the candidate’s calendar.

[0109] in some implementations, either one of the foregoing users’ calendars may be maintained by the system, for example, recruiting management client application 146 may display calendars for each respective user. Alternatively or additionally, scheduling and hiring UI manager 310 may automatically access a third party calendar application and insert the calendar details into the third party calendar application.

[01103 in some implementations, scheduling and hiring engine 210 may facilitate hiring a candidate. For example, scheduling and hiring IJT manager 310 may generate a hiring UI that provides display options for receiving an input to hire a candidate. Upon receipt of an input indicating that the hiring manager wishes to hire the candidate, scheduling and hiring UI manager 310 may provide such an indication to scheduling and hiring engine 210, which causes a message that indicates such intent to the appropriate recruiter.

[0111] Communication Manager 212 and Communication UI Manager 312 [0112] In ap implementation, communication manager 212 may facilitate communications between users of system 100 through one or more communication channels. The communication channels may include in-app messaging (e.g., via a push alert to recruiting management client application 146), SMS text message, electronic mail, voice, and/or other communication, channel, in some instances, a user’s profile may indicate one or more preferred communication channels through which to receive communications, in these instances, alert manager 218 may provide a communication through a preferred communication channel.

[0113] In some implementations, communication manager 212 may map a user with other users with whom communications are to occur. In this manner, when the user accesses a systemgenerated UI (e.g., a UI described herein), communication manager 212 may identify the user and identify the user’s mapped contacts. For example, communication UI manager 312 may integrate with other UI managers. Communication manager 212 may provide communication UI manager 312 with appropriate contact information for the user, and communication UI manager 312 may integrate a display option corresponding to the mapped contacts. In this manner, the user may be able to easily and efficiently send a communication with one or more of the mapped contacts from the UI. For example, a hiring manager may be mapped to one or more recruiters. Instead of having to lookup the recruiter’s contact Information (or know which recruiter is handling a hiring manager’s requisitions at any given time), the hiring manager may simply use a display option to contact a recruiter (who may be currently handling a requisition).

[0114] In some implementations, communication UI manager 312 may provide a persistent display option that persists across multiple (and possibly all) system-generated UIs. In this manner, a user may have persistent access to one or more mapped contacts on multiple or all system-generated UIs to which the user may access.

[0115] In some implementations, the mapped contacts may be arranged hierarchically, such that different levels of contacts may be mapped to a user. For example, a recruiter may be a lowerlevel mapped contact and a recruiter’s supervisor may be a higher-level mapped contact. To the extent that the lower-level mapped contact was not responsive to a communication from the hiring manager, the hiring manager may escalate the situation by contacting the higher-level mapped contact. Multiple levels of contacts may be used.

[0116] In an implementation, communication manager 212 may facilitate collaboration among multiple users. For example, a hiring manager may wish to share views and transactions with their team (e.g., multiple candidates who have been hired to work on the hiring manager’s project). Similarly, recruiters may wish to interface with their HR, finance, operations, or the software development team. Other types of multi-user interactions may be facilitated as well.

[0117] Referral Manager 214 and Referral III Manager 314 [0118] In an implementation, referral manager 214 may manage a referral of one user by another user. Tor example, a hiring manager may refer a candidate (e.g., if a candidate worked on a temporary job assignment for the hiring manager or is otherwise pursuing another job) to another hiring manager, a hiring manager may refer a recruiter to another hiring manager, and so on. Referral manager 214 may store such referrals in association with both the referring user’s profile and the referred user’s profile. In this manner, such referral information may be used by the matching engine and other system components to identify preferences (e.g., a referring user’s preference for a referred user).

[0119] Referral Uf manager 314 may provide a referral UI that includes display options for receiving referrals. Additionally or alternatively, the referral UI may provide an indication of such referrals for the referred user and/or the referring user.

[0120] Alert Manager 218 and Alert 131 Manager 318 [0121] In an implementation, alert manager 218 may provide system-generated alerts to users. Such alerts may be communicated to a user through communication manager. The alert may relate to various information communication by system 100 to users. The nature of the alert will vary based on the user to which the user is directed. For instance, the alert may relate to an RFQ status update provided to a hiring manager, an identification of a matched candidate provided to a recruiter or hiring manager, an identification of a new RFQ from a hiring manager, a communication from one user to another user, and/or other types of communications described herein.

[0122] in an implementation, alert manager 218 may aggregate feed information and provide an alert in the form of a feed. The feed may relate to information that may be of interest to a recipient user, and may be identified based on the user’s profile. For example, and without limitation, a feed for a hiring manager may relate to industry news, human resources bulletins, news of general interest, and/or other information that may be relevant to a hiring manager. Likewise, a feed for a candidate may relate to interview tips, resume tips, employment trends, new job listings, and/or other information that may be relevant to a candidate, [0123] Alert manager 218 may provide the feed to alert UI manager 318, which may present the feed to the user. For example, the feed may be presented to the user when the user opens a mobile app (e.g., activates recruiting management client application 146), presented to the user on demand (e.g., via a display 1.T provided by alert UI manager 318), and/or at other times. In some instances, alert manager 218 may provide the feed to the user via the communication manager.

[0124] API Manager 220 [0125] In an implementation, computer system 110 may integrate with various third party social media sites 150, third party data sources 160, and/or other third party entities. API manager 220 may provide APIs or integrate with third party APIs to communicate Information between computer system 110 and third parties.

[0126] Examples of API integrations include, without limitation, NETSU1TE, VMS (vendor management systems), LINKEDIN, FACEBOOK, DROPBOX, eSignature services (e.g., DOCUSiGN, CUDASIGN, FIELLOSIGN), real-time messaging platforms (e.g., SLACK, SKYPE for Business), web conferencing (e.g. CISCO WEBEX), etc. One or more of the foregoing integrations may be used by communication manager 212 to provide communication to and/or from users.

[0127] App Analytics Engine 222 [0128] In an implementation, app analytics engine 222 may monitor usage of recruiting management client application 146. Based on the monitoring, app analytics engine 222 may generate usage metrics. The usage metrics may relate to who is using recruiting management client application 146, when it is being used, how often it Is being used, and which features are being used most. The usage metrics may be used to fine-tune features provided by recruiting management client application 146.

[0129] Proactive Recommendation Engine 224 [0130] In an implementation, proactive recommendation engine 224 may proactively recommend candidates and/or job listings to be requisitioned prior to a request to requisition a job listing is received. In this manner, proactive recommendation engine 224 may anticipate hiring needs before a hiring manager requisitions a job or otherwise requests help in filling a job. [0131] in an implementation, proactive recommendation engine 224 may generate a prediction score based on one or more predictive variables. The predictive variable may include, without limitation, information indicating monitored user activity, date, prior job requisitions, news events, and/or other information that may indicate a job listing will be or is required to be opened. If the prediction score passes a threshold value, proactive recommendation engine 224 may recommend a job listing to be opened and, in some instances, one or more candidates for he proactively recommended job listing. Each prediction value may be weighted using a prediction coefficient. Proactive recommendation engine 224 may adjust, based on machine-learning, the prediction value and the prediction coefficient, much like matching engine 208 adjusts the match variables and corresponding coefficients.

[0132] Proactive recommendation engine 224 may obtain user activity information. The user activity information may include, without limitation, usage (by a hiring manager) of recruiting management client application 146, feedback provided by a hiring manager, number of communications with a recruiter, and/or other information that indicates user activity with respect to the app. For example, if a hiring manager activates the recruiting management client application 146 above an average number of times over a particular time period, proactive recommendation engine 224 may accordingly adjust a predictive variable associated with the user activity information. In another example, if a hiring manager provides negative feedback on a team member (e.g., a former candidate provided by the system and current employee hired by the hiring manager), proactive recommendation engine 224 may predict that a new team member may need to be requisitioned (and may cause matching engine 208 to determine potential matches, which may exclude similar candidates - i.e., candidates with one or more candidate traits as the team member who received negative feedback). In still another example, if a hiring manager communicates with a recruiter more than average over a particular time period, proactive recommendation engine 224 may predict that the hiring manager is interested in a new job listing or candidate. Proactive recommendation engine 224 may correlate any of the foregoing user activity information with prior hiring activity and predict that such activity indicates that a job requisition or interest in a new candidate will be forthcoming.

[0133] Proactive recommendation engine 224 may obtain a date and compare the date with prior job requisitions. For example, proactive recommendation engine 224 may determine that a hiring manager performs seasonal hiring around certain months of the year. Proactive recommendation engine 224 may accordingly adjust a predictive variable associated with the date.

[0134] Proactive recommendation engine 224 may obtain news events and compare such news events with prior hiring decisions. For example, proactive recommendation engine 224 may correlate increases in stock price, sales/revenue, and/or other news events with prior job requisitions and may accordingly adjust a predictive variable associated with news events.

[0135] When a job listing is proactively recommended prior to a requisition being received, proactive recommendation engine 224 may match candidates (as described herein) to the proactively recommended job listing. In this manner, a given hiring manager may be recommended with both a proactively determined job listing and corresponding potential candidate matches to fill the proactively determined job listing.

[0136] Client Configuration Manager 330 [0137] In an implementation, client configuration manager 330 may manage and provide display options for changing settings. For example, the display options may enable password changes, provide application feedback or suggestions to the app, change profile settings (e.g., communication channel preferences), and/or other settings changes.

[0138] Document Scan Engine 332 [0139] In an implementation, document scan engine 332 may scan one or more documents. For example, document scan engine 332 may use an onboard camera or other imaging device to generate a photograph or other image of a document for sharing. Document scan engine 332 may provide an electronic document based on the photograph to a user, such as a hiring manager. The electronic document may include the photograph image itself, characters recognized from the image (e.g., via optical character recognition), a reformatted version of the photograph (e.g,, a photograph embedded within or converted to another document format), and/or other information relating to the image. In this manner, a hiring manager or others may input job descriptions, RFQ information, and/or other information into the system based on hard copy documents and other imaged objects without having to type in or otherwise manually enter such information.

[0140]Although illustrated in FIG. 1 as a single component, computer system 110 and client device 140 may each include a plurality of individual components (e.g., computer devices) each programmed with at least some of the functions described herein. In this manner, some components of computer system 110 and/or client device 140 may perform some functions while other components may perform other functions, as would be appreciated. The one or more processors 112 may each include one or more physical processors that are programmed by computer program instructions. The various instructions described herein are exemplary only. Other configurations and numbers of instructions may be used, so long as the processor(s) 112 are programmed to perform the functions described herein.

[0141] Furthermore, it should be appreciated that although the various instructions are illustrated in FIG. 1 as being co-iocated within a single processing unit, in implementations in which processor(s) 512 includes multiple processing units, one or more instructions may be executed remotely from the other instructions, [0142] The description of the functionality provided by the different instructions described herein is for illustrative purposes, and is not intended to be limiting, as arty of instructions may provide more or less functionality than is described. For example, one or more of the instructions may be eliminated, and some or all of its functionality may be provided by other ones of the instructions. As another example, processors) 112 may be programmed by one or more additional instructions that may perform some or all of the functionality attributed herein to one of the instructions.

[0143] The various instructions described herein may be stored in a storage device 114, which may comprise random access memory (RAM), read only memory (ROM), and/or other memory. The storage device may store the computer program instructions (e.g,, the aforementioned instructions) to be executed by processor 132 as well as data that may be manipulated by processor 112. The storage device may comprise Hoppy disks, hard disks, optical disks, tapes, or other storage media for storing computer-executable instructions and/or data.

[0144] The various databases (e.g., databases 301, 103, 105, and 107) described herein may be, include, or interface to, for example, an Oracle™ relational database sold commercially by Oracle Corporation. Other databases, such as Informix™, DB2 (Database 2) or other data storage, including file-based, or query formats, platforms, or resources such as OLAP (On Line Analytical Processing), SQL (Structured Query Language), a SAN (storage area network), Microsoft Access'™ or others may also be used, incorporated, or accessed. The database may comprise one or more such databases that reside in one or more physical devices and in one or more physical locations. The database may store a plurality of types of data and/or files and associated data or file descriptions, administrative information, or any other data.

[0145]The various components illustrated in FIG. I may be coupled to at least one other component via a network, which may include any one or more of, for instance, the Internet, an intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a SAN (Storage Area Network), a MAN (Metropolitan Area Network), a wireless network, a cellular communications network, a Public Switched Telephone Network, and/or other network. In FIG. I, as well as in other drawing Figures, different numbers of entities than those depicted may be used. Furthermore, according to various implementations, the components described herein may be implemented in hardware and/or software that configure hardware.

[0146] FIG. 4 illustrates a flow diagram of a process 400 for matching candidates with job listings, according to an implementation of the invention. The various processing operations and/or data flows depicted in FIG. 4 (and in the other drawing figures) are described in greater detail herein. The described operations may be accomplished using some or all of the system components described in detail above and, in some implementations, various operations may be performed in different sequences and various operations may be omitted. Additional operations may be performed along with some or ah of the operations shown in the depicted flow diagrams. One or more operations may be performed simultaneously. Accordingly, the operations as Illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting.

[0147] In an operation 402, process 400 may include obtaining a job profile comprising a plurality of job parameters that specify a position to be filled. In some instances, the job profilejob profile is based on a job template that includes at least some of the plurality of job parameters.

[0148] in an operation 404, process 400 may include obtaining a plurality of candidate profiles. The plurality of candidate profiles may include a first candidate profile comprising a plurality of candidate traits that describe a first candidate.

[0149] In an operation 406, process 400 may include comparing a value of at least a first job parameter from the job profile with a value of at least a first candidate trait from the first candidate profile.

[0150] In an operation 408, process 400 may include generating a first match score based on the comparison.

[0151] In an operation 410, process 400 may include determining that the first match score passes a threshold match score.

[0152] In an operation 412, process 400 may include causing a message to be transmitted to a mobile application executing at a user device of the hiring manager. The message may indicate that the first candidate has been matched to the job listing responsive to the determination that the first match score passes a threshold match score.

[0153] In an operation 414, process 400 may include receiving, from the mobile application, an indication of interest by the hiring manager in the first candidate.

[0154] In an operation 416, process 400 may include causing an interview to be scheduled between the hiring manager and the first candidate. For example, to cause an interview to be schedule, process 400 may transmit a message to the hiring manager and/or the first candidate that an interview should be scheduled. The hiring manager and/or the first candidate may arrange such interview, and process 400 may store such scheduling in a system-controlled calendar. Alternatively or additionally, process 400 may access a calendar associated with the hiring manager and/or the first candidate and add an interview calendar item to the calendar, [0155]FIG, 5 illustrates a flow diagram of a process 500 for anticipating a need to open a position, and proactively recommending a job listing before a requisition for the job listing is received, according to an implementation of the invention, [0156] In an operation 502, process 500 may include obtaining one or more predictive variables associated with a hiring manager. The one or more predictive variables may include user activity information that indicates a level of activity of the hiring manager with respect to the computer system, feedback information provided by the hiring manager for a team member that was previously a candidate and is an employee that was hired by the hiring manager (in which the feedback information may indicate that the team member may be terminated in which case a job listing for the team member’s replacement may be needed), date information that may indicate seasonal hiring trends, and/or other types of information.

[0157] in an operation 504. process 500 may include determining, prior to receipt of a requisition for a particular job listing, that the particular job listing should be opened based on the one or more predictive variables.

[0158] In an operation 506, process 500 may include generating, by the computer system, a job listing responsive to the determination that the job listing should be opened. Alternatively or additionally, process 500 may generate a job profile comprising a plurality of job parameters that specify a position to be filled.

[0159] In an operation 508, process 500 may include providing the job listing to the hiring manager. Alternatively or additionally, process 500 may include identifying one or more candidates (which may be matched as described herein) that may be suitable for the job listing and provide the one or more candidates (i.e., information relating to the candidates such as information from their candidate profiles) to the hiring manager, [0160] In this manner, process 500 may anticipate hiring needs of the hiring manager before the hiring manager requisitions to open a job listing.

[0161] FIG. 6 illustrates a hiring manager user interface (“UI”) 600, according to an implementation of the invention. Upon verification of credentials from a user identified as a hiring manager, UI 600 may be presented to the user (hiring manager). UI 600 (and Uls 7001000 described below) may include a display option 601 configured to be persistently displayed across different user interfaces. Display option 601 may be configured to cause, upon receipt of an input (e.g., a selection, touch, click, gesture, voice command, etc.), a message to be initiated to a given contact.

[0162] The contact (e.g., to whom the message is to be addressed) may be context-specific. For example, and without limitation, for a hiring manager, the contact may be a recruiter, the recruiter’s superior, or other individual at an entity that operates computer system 110. In another example, the contact may change depending on the particular UI being visited. For example, the contact may be the recruiter if the input relating to display option 601 is received in a first UI, but may be a team member if the input relating to display option 601 is received in a second UI. In this manner, the contact may be changed depending on the context (whether user context, UI context, or other context).

[0163] in some instances, the display option 60 i may be used to contact the recruiter or others without having to know that person’s contact information (e.g., email address, SMS text number, in-app communication, etc.). Upon receipt of an indication that an input relating to the display option 601 is received, for example, computer system 110 may look up the appropriate contact and communication channel to use, and cause a message (whose content may have been input by the user) to be transmitted to the appropriate contact via the communication channel.

[0164JU1 600 may, as illustrated, provide various display options 602 for the hiring manager, including, without limitation, a my team display option, an open positions display option, a candidates display option, a request quote display option, and a contact us display option. The my team display option, when selected, may cause UI 700 to be displayed. The open positions display option, when selected, may cause UI 900 to be displayed. The candidates display option, when selected, may cause a listing of candidates (either ail candidates or matched candidates) to be displayed. The request quote display option, when selected, may cause a job listing requisition form to be displayed. The job listing requisition form may be used to request, a job listing to be opened, which may be based on a job template selected and customized by the hiring manager. The contact us display option, when selected, may cause a message to be initiated to an appropriate individual at an entity that operates computer system 110.

[0165] FIG. 7 illustrates a user interface 700 for listing team members, according to an implementation of the invention. UI 700 may include display options 701 for listing team members 702 and their respective photographs and titles. In some implementations, selection of a team member 702 will cause a UI 800 for the team member to be displayed, [0166] FIG. 8 illustrates a user interface 800 for viewing a team member, according to an implementation of the invention, UI 800 may include a display portion 802 for a photograph of the team member, a display portion 804 for listing the team member’s name and title, a display portion 806 for displaying a feedback indication of the team member (where such feedback may be from one or more hiring managers, one or more recruiters, one or more supervisors of the team member, etc.), a display portion 808 for displaying one or more candidate traits of the team member, and a display portion 810 for taking one or more actions. Such actions may be io release the team member (e.g., terminate an employment), contact the team member (so that the hiring manager need not recall the team member’s contact information), and provide feedback on the team member.

[0167] FIG. 9 illustrates a user interface 900 for viewing open positions, according to an implementation of the invention. HI 900 may include a display portion 902 for displaying a listing of open positions. Such open positions may be all open positions handled by the hiring manager, open positions for an employer of the hiring manager, and/or other open positions to which the hiring manager may have access (including all positions hosted by the system). Selection of a given one of the open positions may cause a corresponding Ul 1000 to be displayed.

[0168] PIG. 10 illustrates a user interface 1000 for viewing detailed information for an open position, according to an implementation of the invention, UI 1000 may include a display option 1002 for displaying a job description, which may include one or more job parameters (e.g., minimum requirements, desired skills/experienee, etc.), and a display option 1004 for taking one or more actions with respect to the open position. For example, the one or more actions may include contacting a recruiter (again so that the hiring manager need not know who to contact or the contact information) and reviewing candidates (which may cause UI 900 to be displayed), [0169] Various user interface components of user interfaces 600-1000 of FIGS. 6-10 may be added, deleted, moved, or otherwise changed so that the configuration, appearance, and/or content of the screenshots may be different than as illustrated in the figures. Accordingly, the graphical user interface objects as illustrated (and described in greater detail below) in the Figures are exemplary by nature and, as such, should not he viewed as limiting.

[0170] It should be noted that the various user interfaces 60(5-1000 may be implemented as a mobile application interface (in which instructions for the interface may be encoded using Hypertext Markup Language (HTML) and/or native mobile operating system interface language, a website rendered by a browser application (in which instructions for the interface may be encoded using HTML or other language), and/or other type of interface. The user interfaces 600-1000 may provide various display options such that, upon receipt of an input by a user relating to a display option, an action occurs locally at a device (e.g,, client device 14(5) that displays the user interface. Such action can include processing the input at the device and/or transmitting the input to a remote device, such as computer system 110, via a network. The remote device may respond with information and/or other interfaces that are displayed by the device.

[0171] Other implementations, uses and advantages of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. The specification should he considered exemplary only, and the scope of the invention is accordingly intended to be limited only by the following claims.

Claims

CLAIMS What is claimed is:

1, A computer-implemented method of matching candidates with job listings handled by hiring managers, the method being implemented on a computer system having one or more processors programmed with computer program instructions that, when executed by the one or more processors, programs the computer system to perform the method, the method comprising:

obtaining, by the computer system, a job profile comprising a plurality of job parameters that specify a position to be filled;

obtaining, bv the computer system, a plurality of candidate profiles, the plurality of candidate profiles including a first candidate profile comprising a plurality of candidate traits that describe a first candidate;

comparing, by the computer system, a value of at least a first job parameter from the job profile with a value of at least a first candidate trait from the first candidate profile;

generating, by the computer system, a first match score based on the comparison; determining, by the computer system, that the first match score passes a threshold match score;

causing, by the computer system, a message to be transmitted to a mobile application executing at a user device of the hiring manager, the message indicating that the first candidate has been matched to the job listing responsive to the determination that the first match score passes a threshold match score;

receiving, by the computer system, from the mobile application, an indication of interest by the hiring manager in the first candidate; and causing, by the computer system, an interview to be scheduled between the hiring manager and the first candidate.

2. The method of claim 1, wherein the job profile is based on a job template that includes at least some of the plurality of job parameters, the method further comprising:

receiving, by the computer system, an indication from the hiring manager to use the job template; and generating, by the computer system, the job profile based on the job template.

3. The method of claim 2, the method further comprising:

obtaining, by the computer system, information indicating a plurality of job templates used by the hiring manager to generate job profiles or a number of uses of a single job template used by the hiring manager to generate the job profiles;

identifying, by the computer system, a particular value of a job parameter that is: common to each of the plurality of job templates used by the hiring manager, or part of the single job template;

determining, by the computer system, that the particular value should be adjusted based on at least one other job profile having at least one job parameter in common with the job profile;

generating, by the computer system, a recommendation to adjust the particular value; and providing, by the computer system, the recommendation to the hiring manager.

4. The method of claim 3, wherein the job parameter that is common to each of the plurality of job templates comprises an offered salary or salary range associated with the job profile, and wherein determining that the particular value should be adjusted comprises determining, by the computer system, that a level of the offered salary or salary range is too high or too low.

5. The method of claim 1, wherein generating the match score comprises: determining, by the computer system, a first sub-score for the first job parameter based on the comparison:

obtaining, by the computer system, a first weight for the first job parameter, wherein the first weight Is indicative of a relative importance of the first job parameter; and applying, by the computer system, the first weight to the first sub-score, wherein the generated match score is based on the weighted first sub-score,

6. The method of claim 1, further comprising:

obtaining, by the computer system, an indication that the first candidate was hired to fill the position;

obtaining, by the computer system, an indication that a second candidate was hired to fill a second position associated with the hiring manager;

identifying, by the computer system, one or more candidate traits that have values that are common to both the first candidate and the second candidate; and adjusting, by the computer system, corresponding weights for the one or more candidate traits based on the identification of the one or more candidate traits, wherein the corresponding weights are used to determine a match score for a given candidate.

7. The method of claim 1, wherein the first job parameter comprises an education requirement and the first candidate trait comprises an education of the candidate, wherein comparing the value of the first job parameter from the job profile with the value of the first candidate trait from the first candidate profile comprises; comparing a level of education specified by the education requirement with a level of education achieved by the first candidate as specified by the first candidate trait, wherein the first match score is generated based on whether or not the level of education achieved by the first candidate meets or passes the level of education specified by the education requirement.

8. The method of claim 1, wherein the first job parameter comprises a salary or salary range associated with the position and the first candidate trait comprises a salary or salary range desired by the candidate, wherein comparing the value of the first job parameter from the job profile with the value of the first candidate trait from the first candidate profile comprises: comparing the salary or salary range associated with the position with the salary or salary range desired by the candidate, wherein the first match score is generated based on whether or not the salary or salary range desired by the candidate is below or within the salary or salary range associated with the position.

9. The method of claim 1, the method further comprising:

obtaining, by the computer system, a hiring manager profile comprising a plurality of hiring manager traits that describe the hiring manager; and comparing, by the computer system, a value of at least a hiring manager trait from the hiring manager profile with the value of the first candidate trait or a value of another candidate trait from the first candidate profile, wherein the first match score is based further on comparing the value of the hiring manager trait from the hiring manager profile with the value of the first candidate trait or the value of another candidate trait from the first candidate profile.

10. The method of claim 1, wherein the plurality of candidate profiles further include a second candidate profile comprising a plurality ofsecond candidate traits that describe a second candidate, the method further comprising:

comparing, by the computer system, the value of the first job parameter from the job profile with a second value of at least a second candidate trait from the second candidate profile;

generating, by the computer system, a second match score based on comparing the value of the first job parameter from the job profile with the second value;

determining, by the computer system, that the second match score passes the threshold match score; and ranking, by the computer system, the first candidate and the second candidate based on the first match score and the second match score, wherein the message includes the ranking of the first candidate with respect to the second candidate.

11. A system of matching candidates with job listings handled by hiring managers, the system comprising:

a computer system comprising one or more processors programmed with computer program instructions that, when executed by the one or more processors, programs the computer system to:

obtain a job profile comprising a plurality of job parameters that specify a position to be filled;

obtain a plurality of candidate profiles, the plurality of candidate profiles including a first candidate profile comprising a plurality of candidate traits that describe a first candidate;

compare a value of at least a first job parameter from the job profile with a value of at least a first candidate trait from the first candidate profile;

generate a first match score based on the comparison;

determine that the first match score passes a threshold match score;

cause a message to be transmitted to a mobile application executing at a user device of the hiring manager, the message indicating that the first candidate has been matched to the job listing responsive to the determination that the first match score passes a threshold match score;

receive from the mobile application, an indication of interest by the hiring manager in the first candidate; and cause an interview to be scheduled between the hiring manager and the first candidate.

12, The system of claim 1I, wherein the job profile is based on a job template that includes at least some of the plurality of job parameters, wherein the computer system is further programmed to:

receive an indication from the hiring manager to use the job template; and generate the job profile based on the job template.

13, The system of claim 12, wherein the computer system is further programmed to: obtain information indicating a plurality of job templates used by the hiring manager to generate job profiles or a number of uses of a single job template used by the hiring manager to generate the job profiles;

identify a particular value of a job parameter that is: common to each of the plurality of job templates used by the hiring manager, or part of the single job template;

determine that the particular value should be adjusted based on at least, one other job profile having at least one job parameter in common with the job profile;

generate a recommendation to adjust the particular value; and provide the recommendation to the hiring manager.

14. The system of claim 13, wherein the job parameter that is common to each of the plurality of job templates comprises an offered salary or salary range associated with the job profile, and wherein to determine that the particular value should be adjusted, the computer system is further programmed to:

determine that a level of the offered salary or salary range is too high or too low.

15. The system of claim 11, wherein to generate the match score the computer system is further programmed to:

determine a first sub-score for the first job parameter based on the comparison; obtain a first weight for the first job parameter, wherein the first weight is indicative of a relative importance of the first job parameter; and apply the first weight to the first sub-score, wherein the generated match score is based on the weighted first sub-score.

16. The system of claim 11 , wherein the computer system is further programmed to: obtain an indication that the first candidate was hired to fill the position;

obtain an indication that a second candidate was hired to fill a second position associated with the hiring manager;

identify one or more candidate traits that have values that are common to both the first candidate and the second candidate; and adjust corresponding weights for the one or more candidate traits based on the identification of the one or more candidate traits, wherein the corresponding weights are used to determine a match score for a given candidate.

17. The system of claim 11, wherein the first job parameter comprises an education requirement and the first candidate trait comprises an education of the candidate, wherein to compare the value of the first job parameter from the job profile with the value of the first candidate trait from the first candidate profile, the computer system is further programmed to:

compare a level of education specified by the education requirement with a level of education achieved by the first candidate as specified by the first candidate trait, wherein the first match score is generated based on whether or not the level of education achieved by the first candidate meets or passes the level of education specified by the education requirement.

18. The system of claim 11, wherein the first job parameter comprises a salary or salaryrange associated with the position and the first candidate trait comprises a salary or salary range desired by the candidate, wherein to compare the value of the first job parameter from the job profile with the value of the first candidate trait from the first candidate profile, the computer system is further programmed to:

compare the salary or salary range associated with the position with the salary or salary range desired by the candidate, wherein the first match score is generated based on whether or not the salary or salary range desired by the candidate is below or within the salary or salary range associated with the position,

19. The system of claim 11, wherein the computer system is further programmed to: obtain a hiring manager profile comprising a plurality of hiring manager traits that describe the hiring manager; and compare a value of at least a hiring manager trait from the hiring manager profile with the value of the first candidate trait or a value of another candidate trait from the first candidate profile, wherein the first match score is based further on comparing the value of the hiring manager trait from the hiring manager profile with the value of the first candidate trait or the value of another candidate trait from the first candidate profile,

20. The system of claim 11, wherein the plurality of candidate profiles further include a second candidate profile comprising a plurality of second candidate traits that describe a second candidate, and wherein the computer system is further programmed to:

compare the value of the first job parameter from the job profile with a second value of at least a second candidate trait from the second candidate profile;

generate a second match score based on comparing the value of the first job parameter from the job profile with the second value;

determine that the second match score passes the threshold match score; and rank the first candidate and the second candidate based on the first match score and the second match score, wherein the message includes the ranking of the first candidate with respect to the second candidate.