DESC:FIELD OF PRESENT DISCLOSURE
[0001] The embodiments of the present disclosure generally relate to a digital platform for matching job providers and job seekers. In particular, the present disclosure relates to systems and methods for matching job providers and job seekers based on a scoring model.

BACKGROUND
[0002] Now-a-days, the Internet is the most preferred medium for job placement. Typically, a profile matching service or aggregator is provided through a website where both job providers and job seekers may register and create their profiles. The profiles may include details such as educational background, work experience, skill sets, and the like. Thereafter, the job seekers and job providers are matched based on the details provided in the respective profiles.
[0003] However, the conventional systems and methods are inflexible, specifically for blue-collared and grey-collared segment, as there is lack of clear definition of skills and competencies, relevance with job/skills, etc., few usages of resumes, incomplete and incomprehensible resumes, demographic barriers (e.g., language), etc. Further, the conventional systems and methods do not consider implicit skills associated with any job profile performed by candidates as well as do not extract indirect competency for a given skill.
[0004] There is, therefore, a need for systems and methods for addressing at least the above-mentioned problems in existing systems.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] An object of the present disclosure is to provide an efficient solution for determining a best match between a user and a service such as, between a job seeker and a job provider.
[0006] Another object of the present disclosure is to provide systems and methods that facilitate in finding a best match for a user based on both implicit and explicit preferences, as well as demographic data, for example, language.
[0007] A yet another object of the present disclosure is to provide systems and methods that facilitate creation of a knowledge graph to identify association between a job seeker and a job provider.

SUMMARY
[0008] Aspects of the present disclosure generally relate to a digital platform for matching job providers and job seekers. In particular, the present disclosure relates to systems and methods for matching job providers and job seekers based on a scoring model.
[0009] In an aspect, the disclosed system includes one or more computing devices configured to receive a set of inputs from one or more users pertaining to a data related to job seekers and job providers and a processing unit communicatively coupled to the one or more computing devices via a network. The processing unit configured to receive the set of inputs from the one or more computing devices and generate a knowledge graph to identify associations between attributes of job seekers and job providers. Further, the processing unit is configured apply a machine learning-based scoring model to the knowledge graph to compute a relevance score for each job seeker and job provider based on one or more compatibility metrics, generate job and talent recommendations for the users based on the computed relevance scores and transmit the job and talent recommendations to the one or more computing devices of the users for display on a user interface.
[0010] The set of inputs may include explicit data provided by users including resumes, skills, job preferences, and job postings, and implicit data inferred from user interactions with the system including job views, applications, and other behavioral data.
[0011] The knowledge graph may further identify demographic and industry-based metadata associated with job seekers and job providers, including location, preferred work mode, industry experience, and job types.
[0012] The machine learning-based scoring model may be configured to compute the relevance score based on multiple compatibility metrics, including a skill gap score, a talent trust score, and an employer trust score, each representing different aspects of job seeker and job provider compatibility. The job and talent recommendations may be ranked according to the computed relevance scores, with higher-ranked recommendations being displayed more prominently on the user interface.
[0013] The processing unit may be configured to apply generative AI to the set of inputs to augment the knowledge graph, including processing video, audio, and textual data to extract attributes such as voice tone, body language, and language proficiency.
[0014] The user interface on the one or more computing devices may allow users to provide feedback on the recommendations received, and the processing unit may use the feedback to refine the machine learning-based scoring model.
[0015] In another aspect, the disclosed method includes receiving, by a plurality of computing devices operated by users, a set of job-related data from job seekers and job providers. In addition, the method includes generating, by a processing unit associated with a system, a knowledge graph to identify associations between attributes of job seekers and job providers and computing, by the processing unit, a relevance score for each job seeker and job provider based on one or more compatibility metrics. Further, the method includes generating, job and talent recommendations for the users based on the computed relevance scores and transmitting, the job and talent recommendations to the one or more computing devices of the users for display on a user interface.
[0016] The method may include updating the knowledge graph in real time based on new or modified job-related data from job seekers and job providers, allowing for continuous refinement of associations between attributes.
[0017] Computing the relevance score may include applying demographic and industry-based metadata to the compatibility metrics, allowing the recommendations to reflect user preferences and industry-specific requirements such as location, preferred work mode, or relevant job skills.

BRIEF DESCRIPTION OF DRAWINGS
[0018] FIG. 1 illustrates an operating environment of a network architecture for implementing a system for determining a best match between a job seeker and a job provider, in accordance with embodiments of the present disclosure.
[0019] FIG. 2 illustrates an example representation of a proposed system, in accordance with embodiments of the present disclosure.
[0020] FIG. 3A illustrates an example block diagram of a proposed system, in accordance with embodiments of the present disclosure.
[0021] FIG. 3B illustrates an example block diagram of a scoring engine of a proposed system, in accordance with embodiments of the present disclosure.
[0022] FIG. 4 illustrates a computer system in which or with which embodiments of the present disclosure may be implemented.
[0023] FIG. 5 illustrates an exemplary flow diagram of a proposed method, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0024] FIG. 1 illustrates an example operating environment or a network architecture 100 in which a system or a server 108 may be implemented for determining a best match for a job seeker and a job provider, in accordance with embodiments of the present disclosure.
[0025] In this embodiment, the network architecture 100 may include one or more computing devices (104-1, 104-2…104-N) operated by one or more users (102-1, 102-2…102-N). A person of ordinary skill in the art will appreciate that the one or more computing devices (104-1, 104-2…104-N) may be collectively referred as computing devices 104 and individually referred as computing device 104. Similarly, a person of ordinary skill in the art will understand that the one or more users (102-1, 102-2…102-N) may be collectively referred as users 102 and individually referred as user 102. Herein, the users 102 may include vendors, job seekers, and job providers.
[0026] In an example embodiment, the computing device 104 may refer to a wireless device and/or a user equipment (UE). It should be understood that the terms “computing device,” “wireless device,” “user device,” and “user equipment (UE)” may be used interchangeably throughout the disclosure.
[0027] A wireless device or the UE 104 may include, but not be limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and/or any other type of computer device with wireless communication capabilities, and the like. In an example embodiment, the computing devices 104 may communicate with the system 108 via a set of executable instructions residing on any operating system. In an example embodiment, the computing devices 104 may include, but are not limited to, any electrical, electronic, electro-mechanical or an equipment or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, wherein the computing device 104 may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, a keyboard, input devices for receiving input from the user 102 such as touch pad, touch enabled screen, electronic pen and the like.
[0028] Referring to FIG. 1, the system 108 may be communicatively coupled to the computing devices 104 via a network 106. In an example embodiment, the system 108 may communicate with the computing devices 104 in a secure manner via the network 106. The network 106 may include, by way of example, but not limited to, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, some combination thereof, or so forth. The network 106 may also include, by way of example, but not limited to, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. In particular, the network 106 may be any network over which the user 102 communicates with the system 108 using their respective computing devices (e.g., computing devices 104).
[0029] Referring to FIG. 1, the system 108 may be implemented by way of a single device or a combination of multiple devices that may be operatively connected or networked together. The system 108 may be implemented in a hardware or a suitable combination of hardware and software. In another example embodiment, the system 108 may be implemented as a cloud computing device or any other device that is network connected. In an example embodiment, the system 108 may implement artificial intelligence (AI) and machine learning (ML) prediction algorithm to determine a best match for a job seeker and a job provider in a timely manner, and thus, reducing the post to hire time and apply to join time.
[0030] Referring to FIG. 1, the computing device 104 may store and execute a client-side application (i.e., an employer side application, a vendor side application, and a talent side application) that presents, to the user 102, one or more user interfaces. The client-side application may interact with a server-side application or the system 108. In an example embodiment, the computing device 104 may display a webpage of a website hosted by the system 108. The present disclosure proposes a system (e.g., 108) that responds accurately and timely in any use case or exemplary scenario where there is a requirement for a user query to be responded through an interface or otherwise.
[0031] In an example embodiment, the user 102 may post a query or initiate a search on the system 108 using the computing device 104. As an example, the user 102 may look for a job opportunity on the system 108 such that the user 102 may submit one or more inputs such as, but not limited to, a resume including educational qualifications, past work experience, skill set, etc., a video of themselves seeking the job opportunity, an audio recorded by themselves, or the like. In such an example, the system 108 may collect one or more parameters from a dynamic database (not shown). The one or more parameters may include, but not limited to, job postings in the public domain, public or open entity skill definition and competencies, or the like. Based on the one or more inputs and the one or more parameters, the system 108 may use AI and ML based algorithms to determine the best match for the user 102 based on their skill set and other metadata. Likewise, the user 102 may be the best match for the job provider. This is explained in more detail throughout the disclosure.
[0032] Referring to FIG. 2, the system 108 may receive a set of inputs including, but not limited to, video resume and profile 202 of users (e.g., 102), public resume and profile or job postings 204, public or open entity on skill definition and competencies 206, and text resume and profile 208 of users 102. In some embodiments, a computing device (e.g., 104) being operated by the user 102 may include a digital platform communicatively coupled with the system 108. In some embodiments, the digital platform may be a mobile application (“app”). The mobile application may be installed on the computing device 104. In some embodiments, the digital platform may be a web application (e.g., a website or a webpage) or a desktop application. In some embodiments, the digital platform may be a cloud application in-premise infrastructure or on third-party cloud provider infrastructure. The digital platform in conjunction with a processing unit of the system 108 may render a graphical user interface on the computing device 104 such that the user 102 of the computing device 104 may communicate with the system 108 via the graphical user interface rendered on the computing device 104. The graphical user interface may be rendered on the computing device 104 under control of the system 108. In some embodiments, the digital platform may be hosted on the system 108. In some embodiments, the user 102 may use the computing device 104 to, but not limited to, view job postings/profiles rendered by the system 108, submit the set of inputs at the system 108, or the like.
[0033] In some embodiments, the system 108 may authenticate the user 102 of the computing device 104 seeking access to the digital platform. In such embodiments, the system 108 may request login information from the user 102, such as user credentials for the digital platform. If the user 102 is successfully authenticated, the system 108 may route the computing device 104 to the digital platform for performing a desired service for the user 102, for example, finding a suitable job, or finding a suitable candidate/talent, or the like. In some embodiments, the user credentials may be associated with user profile information such as, but not limited to, the user’s e-mail address, user preferences, etc., so that each time the user 102 logs into the digital platform, the associated user profile information is available with the digital platform. This user profile information allows the user 102 to enter the information once and use that same information during subsequent logins to the digital platform. By maintaining the user profile information, the system 108 may be able to share pertinent information with the digital platform to speed registration and enable access to services. Further, the system 108 enables association of implicit and explicit user preferences with the user profile. In some embodiments, as discussed herein, the implicit preferences may be arrived from user activities and behavior of the user on the system 108 and beyond.
[0034] In some embodiments, the system 108 may associate the set of inputs with the corresponding user profile information such that the user 102 may not have to submit the same information again and again, however, the user 102 may be able to modify the information, as and when required. In some embodiments, the system 108 may receive one or more search parameters from the user 102 such as, but not limited to, a type of job, a location of the job, skills required for the job, educational qualifications required for the job, compensation offered, expected compensation, benefits, working hours, medical policies, leave policy, termination policy, or the like. It may be appreciated that the user 102 here may be a job seeker or a job provider. In some embodiments, the system 108 may store the one or more search parameters received from the user 102 in a database (not shown in FIG. 2) to provide one or more recommendations to the user 102 related to the one or more search parameters. In some embodiments, the system 108 may associate the user profile information with the one or more search parameters provided by the user 102, so that the system 108 may generate the one or more recommendations based on their submitted search parameters. The system 108 may identify a set of job postings or a set of candidates whose parameters match with the one or more search parameters provided by the user 102. In such a scenario, the system 108 may first locate data related to the job postings or candidates by comparing terms in the one or more search parameters to the data in the database, and then assign a relevance score to the identified set of job postings or the set of candidates based at least on the one or more search parameters and/or the one or more inputs provided by the user 102. Further, the system 108 may sort the one or more recommendations based on the relevance score, and then provide the sorted recommendations to the user 102 on the graphical user interface of the computing device 104. In some embodiments, the system 108 may generate a report for the list of recommendations. Consistent with aspects of the present disclosure, the relevance score may refer to a value that attempts to quantify a likelihood of a job seeker matching a job role and a job provider. This is explained in the ongoing description with reference to FIG. 2.
[0035] Referring to FIG. 2, in some embodiments, the system 108 may enrich and augment 210 the collected set of inputs by performing data sanitation, validation, synchronization, and training (e.g., self-training by the system 108). For example, the system 108 may perform data cleanup and noise removal on the collected set of inputs. In some embodiments, the system 108 may also tag and categorize the collected set of inputs, i.e., resumes and profiles into one or more categories including, but not limited to, based on skill set, based on educational qualifications, based on demographic variables such as, age, gender, location, or the like. In some other embodiments, the system 108 may convert the video resume and profiles into transcript format using appropriate techniques. In some embodiments, the system 108 may extract one or more attributes from the video resume and profiles such as, but not limited to, facial expressions, gestures, volumetric data, body movements, or the like. Further, the system 108 may convert audio resume or profile attributes into transcript format using appropriate techniques for automatic speech recognition. In some embodiments, the system 108 may extract one or more attributes from the audio resume and profiles such as, but not limited to, voice tone features, emotion features, accent features, language features, or the like. By extracting the one or more attributes, the system 108 may record a behavior of the user 102.
[0036] Referring to FIG. 2, it may be understood that the digital platform may include a vendor side application 212, a job seeker or talent side application 214, and an employer side application 216. In some embodiments, the system 108 may gather information from each of these applications (212, 214, 216) in the form of interaction data 218. For example, using the digital platform provided by the system 108, the vendors, the job seekers, and the employers may communicate with each other in the form of job postings, job opportunities, looking for work posts, messages, calls, or the like. Therefore, the system 108 may gather the interaction data 218. In some embodiments, the interaction data 218 may include, but not limited to, messages, job descriptions, call logs, applied jobs, viewed jobs/employers, rejected/ignored jobs, community interactions, time of day, or the like. Further, the system 108 may also determine preferences 220 of the job seekers and the job providers. The preferences 220 may include implicit preferences and explicit preferences. The implicit preferences may be based on, but not be limited to, demographic data of a user 102, actions on the digital platform, current stage in life journey, personal details, aspiration, or the like. The explicit preferences may include, but not be limited to, a type of job, location preferences, work time (e.g., full time, part time, day shift, night shift), work mode (e.g., work from home, work from office), salary, work life balance, preferred kind of job role, preferred companies, travel distance to office, and mobility (i.e., open to relocate). In some embodiments, the system 108 may capture the preferences 220 based on the interaction data 218.
[0037] Referring to FIG. 2, the system 108 may use the enriched and augmented data 210, the interaction data 218, and the preferences 220 to get raw data 222 for the system 108 to process. In some embodiments, the system 108 may use generative AI 224 on the raw data 222. A person of ordinary skill in the art may understand that generative AI may refer to algorithms that may be used to create content, including audio, code, images, text, simulations, and video in response to prompts. In particular, generative AI uses deep learning techniques, such as neural networks, to generate new content such as images, videos, or text.
[0038] In accordance with embodiments of the present disclosure, the system 108 uses generative AI 224 to generate a knowledge graph 226 corresponding to the collected set of inputs, the preferences 220, and the interaction data 218 gathered from each of the vendor application 212, the job seeker application 214, and the employer application 216. A knowledge graph may refer to a network of real-world entities, i.e., objects, events, situations, concepts, etc., and illustrate a relationship between them. For example, in the current scenario, the knowledge graph 226 may depict a relationship between a set of job seekers and a set of job providers based on the raw data 222. In an example embodiment, the knowledge graph 226 may capture an association between various attributes, role, employers/organizations, sill, experience, location, age, qualification, form of work, etc.
[0039] Referring to FIG. 2, the system 108 may apply a custom ML model 228 on the knowledge graph 226 to determine a set of scores 230. The set of scores 230 may include, but not be limited to, skill gap score, talent trust score, and employer trust score. In some embodiments, the system 108 may determine the skill gap score to quantify a gap between skills required for a job role and skills possessed by the job seeker. The less the skill gap score, the better may be the match between the job seeker and the job provider. In some embodiments, skill gap may have multiple attributes such as skill level (e.g., new, novice, beginner, intermediate, expert, elite) and skill specific elements (e.g., customer problem understanding, sale process understanding, product expertise, business acumen, prospecting, etc.). Further, in some embodiments, the talent trust score may quantify a fit of the user 102, in case the user 102 is a candidate or a job seeker, with respect to a job provider or a job posting by a job provider. Furthermore, the employer trust score may quantify a fit of the user 102, in case the user 102 is a job provider or an employer, with respect to a job seeker. It may be noted that this is not an exhaustive list of set of scores 230 that may be determined by the system 108, and the set of scores 230 may include other relevant scores within the scope of the ongoing disclosure such as, but not limited to, talent motivation level, talent commitment score, or the like. In some embodiments, the system 108 may determine the set of scores 230 based on applying custom ML models 228 on the generated knowledge graph 226. In some embodiments, the system 108 may dynamically update the set of scores 230 based on an update in user profile information and/or job postings, etc.
[0040] In some embodiments, the raw data 222 may be applied to a hybrid model 236. Further, the system 108 may apply demography-based metadata 232 and industry-based metadata 234 to the hybrid model 236. In some embodiments, demography may be related to conduciveness of a role for age, gender, ethnicity, etc., and some sills may be acquired as part of social nature, local practices, family practices, etc. which may enable certain demography to perform better than others. This information may not be typically available as part of a resume or an understanding of the job seeker themselves. In some embodiments, when working in specific industries, candidates may develop certain innate characteristics and attributes. For example, logistics, attention to detail, timeliness, customer orientation, efficiency, or the like. As an example, working in the hospitality industry may bring in customer empathy, relationship building, communication, etc. These may refer to industry-based metadata 234. In some embodiments, the demography-based metadata 232 and the industry-based metadata 234 may be based on the data stored and collected by the system 108 continuously or at pre-defined time intervals. The hybrid model 236 may refer to a content or collaborative or a recommendation model. Based on the raw data 222 which may include the collected set of inputs from the user 102, the gathered interaction data 218, and the preferences 220 (both implicit and explicit), the demography-based metadata 232, and the industry-based metadata 234, the system 108 may apply the hybrid model 236 to generate one or more recommendations. In some embodiments, the one or more recommendations may include talent recommendations 238 and job recommendations 240. For example, the system 108 may generate the talent recommendations 238 for the job providers or employers, and the job recommendations 240 for the job seekers or candidates.
[0041] Referring to the FIG. 2, the set of scores 230, the talent recommendations 238, and the job recommendations 240 may be applied to a deep learning-based ML model 242. In particular, the deep learning-based ML model may analyze the set of scores 230, the talent recommendations 238, and the job recommendations 240 to determine a relevance score for each of the users 102 (i.e., both job providers and job seekers). The relevance score may indicate a suitable fit or match of a user 102 corresponding to a job posting/profile. For example, for the job seeker, the system 108 may determine relevance score for a set of job postings and display top recommendations. Similarly, for the job provider, the system 108 may determine relevance score for a set of candidates and display top recommendations. In some embodiments, the system 108 may apply a cosine similarity function to identify the recommendations. For example, the cosine similarity function may measure a similarity between two vectors (e.g., skills in this case) in the considered space.
[0042] FIG. 3A illustrates an example block diagram 300A of a proposed system, in accordance with embodiments of the present disclosure.
[0043] Referring to FIG. 2, the functionalities of the system 108 may be incorporated in its entirety or at least partially in a server (not shown), without departure from the scope of the disclosure. The server may be implemented as a cloud server which may execute operations through web applications, cloud applications, hypertext transfer protocol (HTTP) requests, repository operations, file transfer, and the like. Other examples of the server may include, but are not limited to, a database server, a file server, a web server, a media server, an application server, a mainframe server, a cloud server, or other types of servers. In one or more embodiments, the server may be implemented as a plurality of distributed cloud-based resources by use of several technologies that are well known to those skilled in the art.
[0044] In some embodiments, the system 108 may include a processor(s) 302, a memory 304, an interface(s) 306, processing engine(s) 308, and a database 310. In some embodiments, the system 108 may be communicatively coupled with one or more external entities such as, but not limited to, a computing device (e.g., 104) and one or more public platforms (e.g., job posting platforms) via a communication network (e.g., 106).
[0045] In some embodiments, the processor(s) 302 may include suitable logic, circuitry, and interfaces that may be configured to execute program instructions associated with different operations to be executed by the system 108. For example, some of the operations may include, but are not limited to, authenticating a user 102 of the computing device 104, accessing a digital platform, submitting resumes or job postings, rendering the data on a graphical user interface of the computing device 104, updating the database 310 with information provided by the user 102, and the like. Several other steps and/or sub-steps may be performed by the processor(s) 302 within the scope of the current disclosure.
[0046] In some embodiments, the processor(s) 302 may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Examples of implementations of the processor(s) 302 may be a Graphics Processing Unit (GPU), a Reduced Instruction Set Computing (RISC) processor, an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, a microcontroller, a central processing unit (CPU), and/or a combination thereof.
[0047] Among other capabilities, the processor(s) 302 may be configured to fetch and execute computer-readable instructions stored in the memory 304 of the system 108. The memory 304 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 304 may comprise any non-transitory storage device including, for example, volatile memory such as Random-Access Memory (RAM), or non-volatile memory such as Electrically Erasable Programmable Read-only Memory (EPROM), flash memory, and the like.
[0048] In some embodiments, the interface(s) 306 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as input/output (I/O) devices, storage devices, and the like. The interface(s) 306 may facilitate communication for the system 108. The interface(s) 306 may also provide a communication pathway for one or more components of the system 108. Examples of such components include, but are not limited to, the processing engine(s) 308 and the database 310. In some embodiments, the processing engine(s) 308 may include a data acquisition engine 312, an AI engine 314, a scoring engine 316, and other engine(s) 318 as suitable. In some embodiments, the database 310 may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the system 108 such as, but not limited to, user login information, user submitted inputs, resumes or profiles, job postings, industry trends, search parameters, or the like.
[0049] In some embodiments, the data acquisition engine 310 may be configured to collect a set of inputs from the user 102 such as, but not limited to, interaction data, resume or profiles, skill definition and competencies, or the like. The AI engine 314 may be configured to apply AI and ML based algorithms and models to perform one or more operations such as, but not limited to, generating a knowledge graph, determining a set of scores, generating one or more recommendations, or the like. Further, the scoring engine 316 may be configured to determine and assign a set of scores (e.g., 230). There may be other relevant engine(s) 318 within the scope of the ongoing disclosure.
[0050] In some embodiments, the interface(s) 306 may include suitable logic, circuitry, and interfaces that may be configured to facilitate a communication between the system 108, the computing device 104, and the digital platform (i.e., vendor application 212, job seeker application 214, and employer application 216) via the communication network 106. In some embodiments, the interface(s) 306 may be implemented by use of various known technologies to support wired or wireless communication of the system 108 with the communication network 106. The interface(s) 306 may include, for example, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, or a local buffer circuitry.
[0051] Referring to FIG. 3B, in some embodiments, the scoring engine 316 may refer to an AI engine used for determining and/or calculating scores associated with each candidate and/or employer and/or job posting. In an example embodiment, the scoring engine 316 may implement a scoring model. The scoring model may compute a set of scores (e.g., 230), as depicted in FIG. 3B.
[0052] In some embodiments, the scoring engine 316 may determine a skill gap score 316-1, a talent trust score 316-2, an employer trust score 316-3, and an overall relevance score 316-4. It may be appreciated that the scoring engine 316 may determine other relevant scores within the scope of the current disclosure.
[0053] In some embodiments, the scoring engine 316 may determine the set of scores, as depicted in FIG. 3B, based on applying custom ML models on a knowledge graph (e.g., 226). The knowledge graph 226 may indicate associations between various attributes of job seekers and job providers. Accordingly, the skill gap score 316-1 may quantify a gap between skills desired by a job provider and possessed by a job seeker. The talent trust score 316-2 may quantify a suitability fit of a candidate with respect to a job provider. The employer trust score 316-3 may quantity a suitability fit of a job provider with respect to a candidate. Finally, the relevance score 316-4 may be an overall score assigned to a candidate and/or an employer based on the set of scores, talent recommendations (e.g., 238) generated by the system 108, and job recommendations (e.g., 240) generated by the system 108. It may be appreciated that the scoring engine 316 may calculate the above-described score(s), and save this information in a database (e.g., 310).
[0054] Additionally, or alternatively, in accordance with embodiments of the present disclosure, the scoring engine 316 may calculate a feedback score by implementing a feedback scoring model. The feedback score may be based on feedback obtained on a recommendation from a user 102 of the computing device 104. In some embodiments, the scoring engine 316 may collect data from interview performance, offer to accept data on job performance, stickiness to job, earning growth, or the like, which may act as a feedback.
[0055] Referring to FIG. 4, the computer system 400 may include an external storage device 410, a bus 420, a main memory 430, a read-only memory 440, a mass storage device 450, communication port(s) 460, and a processor 470. The communication port(s) 460 may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) 460 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system 400 connects. The main memory 430 may be random access memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory 440 may be any static storage device(s) including, but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor 470. The mass storage device 450 may be any current or future mass storage solution, which may be used to store information and/or instructions. The bus 420 communicatively couples the processor 470 with the other memory, storage, and communication blocks. The bus 420 can be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), universal serial bus (USB), or the like, for connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 470 to the computer system 400. Optionally, operator and administrative interfaces, e.g., a display, keyboard, and a cursor control device, may also be coupled to the bus 420 to support direct operator interaction with the computer system 400. Other operator and administrative interfaces may be provided through network connections connected through the communication port(s) 460.
[0056] FIG. 5 illustrates an exemplary flow diagram of a proposed method, in accordance with an embodiment of the present disclosure. The method 500 may be implemented using the components of the system 108 as given in FIG. 1.
[0057] In an embodiment, the method 500 can include a step 502 of receiving, by a plurality of computing devices operated by users, a set of job-related data from job seekers and job providers. In addition, the method 500 can include a step 504 of generating, by a processing unit associated with a system, a knowledge graph to identify associations between attributes of job seekers and job providers. Further, at step 506, the method 100 may include computing, by the processing unit, a relevance score for each job seeker and job provider based on one or more compatibility metrics.
[0058] At step 508, the method 500 can include computing, by the processing unit, a relevance score for each job seeker and job provider based on one or more compatibility metrics. Further, the method 500 can include at step 510, transmitting the job and talent recommendations to the one or more computing devices of the users for display on a user interface.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0059] The present disclosure provides an efficient solution to determining a best match between a user and a service such as, between a job seeker and a job provider.
[0060] The present disclosure facilitates in finding a best match for a user based on both implicit and explicit preferences, as well as demographic data.
[0061] The present disclosure facilitates creation of a knowledge graph to identify association between the job seeker and the job provider and reduce the post to hire time for job providers and the apply to join time for job seekers.
,CLAIMS:1. A system (108) for matching job providers and job seekers, said system (108) comprising:
one or more computing devices (104) configured to receive a set of inputs from one or more users (102) pertaining to a data related to job seekers and job providers;
a processing unit communicatively coupled to the one or more computing devices (104) via a network (106), the processing unit configured to:
receive the set of inputs from the one or more computing devices (104);
generate a knowledge graph to identify associations between attributes of job seekers and job providers;
apply a machine learning-based scoring model to the knowledge graph to compute a relevance score for each job seeker and job provider based on one or more compatibility metrics;
generate job and talent recommendations for the users (102) based on the computed relevance scores; and
transmit the job and talent recommendations to the one or more computing devices (104) of the users (102) for display on a user interface.
2. The system (108) as claimed in claim 1, wherein the set of inputs comprises explicit data provided by users (102) including resumes, skills, job preferences, and job postings, and implicit data inferred from user interactions with the system (108) including job views, applications, and other behavioral data.
3. The system (108) as claimed in claim 1, wherein the knowledge graph further identifies demographic and industry-based metadata associated with job seekers and job providers, including location, preferred work mode, industry experience, and job type.
4. The system (108) as claimed in claim 1, wherein the machine learning-based scoring model is configured to compute the relevance score based on multiple compatibility metrics, including a skill gap score, a talent trust score, and an employer trust score, each representing different aspects of job seeker and job provider compatibility.
5. The system (108) as claimed in claim 1, wherein the job and talent recommendations are ranked according to the computed relevance scores, with higher-ranked recommendations being displayed more prominently on the user interface.
6. The system (108) as claimed in claim 1, wherein the processing unit is configured to apply generative AI to the set of inputs to augment the knowledge graph, including processing video, audio, and textual data to extract attributes such as voice tone, body language, and language proficiency.
7. The system (108) as claimed in claim 1, wherein the user interface on the one or more computing devices (104) allows users (102) to provide feedback on the recommendations received, and the processing unit uses the feedback to refine the machine learning-based scoring model.
8. A method (500) for matching job providers and job seekers, said method (100) comprising:
receiving (502), by a plurality of computing devices operated by users, a set of job-related data from job seekers and job providers;
generating (504), by a processing unit associated with a system, a knowledge graph to identify associations between attributes of job seekers and job providers;
computing (506), by the processing unit, a relevance score for each job seeker and job provider based on one or more compatibility metrics;
generating (508), job and talent recommendations for the users based on the computed relevance scores; and
transmitting (510), the job and talent recommendations to the one or more computing devices of the users for display on a user interface.
9. The method (500) as claimed in claim 1, wherein the method (500) comprises updating the knowledge graph in real time based on new or modified job-related data from job seekers and job providers, allowing for continuous refinement of associations between attributes.
10. The method (500) as claimed in claim 1, wherein computing the relevance score includes applying demographic and industry-based metadata to the compatibility metrics, allowing the recommendations to reflect user preferences and industry-specific requirements such as location, preferred work mode, or relevant job skills.