CN117057762A

CN117057762A - Position information processing method, device, equipment and storage medium based on AI

Info

Publication number: CN117057762A
Application number: CN202311118528.4A
Authority: CN
Inventors: 宁博; 董晓盼
Original assignee: Beijing 58 Information Technology Co Ltd
Current assignee: Beijing 58 Information Technology Co Ltd
Priority date: 2023-08-31
Filing date: 2023-08-31
Publication date: 2023-11-14

Abstract

The embodiment of the application provides an AI-based post information processing method, an AI-based post information processing device, AI-based post information processing equipment and a storage medium. In the embodiment of the application, the AI service module is provided for assisting in mining the implicit recruitment demand information of the recruitment user, and generating the target post information of the recruitment user by combining the explicit recruitment demand information of the recruitment user, so that the time cost for generating the post information is reduced, and the efficiency of generating the post information is improved. Further, the AI service module can also combine the post information and the attribute information of the recruitment consultant to screen the appropriate recruitment consultant for the post information, and the recruitment consultant provides subsequent recruitment service for the post information.

Description

Position information processing method, device, equipment and storage medium based on AI

Technical Field

The present application relates to the field of cloud computing technologies, and in particular, to an AI-based post information processing method, apparatus, device, and storage medium.

Background

Currently, when recruiting, recruiters need to prepare recruitment post information, and the work content, the work requirement and the like of the post are described through the recruitment post information so as to be displayed to job seekers at job seekers. However, preparing the post information often requires a significant amount of time and effort from the recruiter, and the results are often unsatisfactory for the recruiter, resulting in less efficient generation of the post information.

Disclosure of Invention

Aspects of the application provide an AI-based post information processing method, an AI-based post information processing device, AI-based post information processing equipment and a storage medium, which are used for improving the generation efficiency of post information.

The embodiment of the application provides an AI-based post information processing method, which is suitable for recruiting terminals configured with AI service modules, wherein the AI service modules are at least used for executing the post information processing method, and the post information processing method comprises the following steps: determining explicit recruitment demand information submitted by a recruiter corresponding to a recruitment end in response to a post mining operation on an AI interaction page provided by the recruitment end; acquiring target recruitment record information of the recruitment user in the historical recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users; inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of a recruitment user; generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and distributing the target post information; screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and combining the attribute information of the recruitment consultant; at least one target recruiter is assigned to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

The embodiment of the application also provides an AI-based post information processing device, which comprises: an AI service module; the AI service module is used for responding to the position mining operation on the AI interaction page provided by the recruitment terminal and determining the explicit recruitment demand information submitted by the recruitment user corresponding to the recruitment terminal; acquiring target recruitment record information of the recruitment user in the historical recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users; inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of a recruitment user; generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and distributing the target post information; screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and combining the attribute information of the recruitment consultant; at least one target recruiter is assigned to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

The embodiment of the application also provides an AI-based post information processing device, which comprises: a memory and a processor; the memory is used for storing a computer program corresponding to the AI service module; and the processor is coupled with the memory and used for executing the computer program to realize each step in the AI-based post information processing method provided by the embodiment of the application.

The embodiment of the application also provides a computer readable storage medium storing a computer program, which when executed by a processor, causes the processor to implement the steps in the AI-based post information processing method provided by the embodiment of the application.

In the embodiment of the application, the AI service module is provided for assisting in mining the implicit recruitment demand information of the recruitment user, and generating the target post information of the recruitment user by combining the explicit recruitment demand information of the recruitment user, so that the time cost for generating the post information is reduced, and the efficiency of generating the post information is improved. Further, the AI service module can also combine the post information and the attribute information of the recruitment consultant to screen the appropriate recruitment consultant for the post information, and the recruitment consultant provides subsequent recruitment service for the post information.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

fig. 1a is a schematic structural diagram of an AI-based recruitment service system provided in accordance with an exemplary embodiment of the present application;

FIG. 1b is a schematic flow chart of an AI-based post information processing method according to an exemplary embodiment of the application;

FIG. 2a is a schematic diagram of an AI interaction page according to an exemplary embodiment of the application;

FIG. 2b is a schematic diagram of another AI interaction page provided in accordance with an exemplary embodiment of the application;

FIG. 3 is a schematic diagram of an AI-based post information processing apparatus according to an exemplary embodiment of the present application;

fig. 4 is a schematic structural diagram of an AI-based post information processing apparatus according to an exemplary embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region, and provide corresponding operation entries for the user to select authorization or rejection. The various models (including but not limited to language models or large models) to which the present application relates are compliant with relevant legal and standard regulations.

Aiming at the technical problems, in the embodiment of the application, the AI service module is provided to assist in mining the implicit recruitment demand information of the recruitment user, and the target position information of the recruitment user is generated by combining the explicit recruitment demand information of the recruitment user, so that the time cost for generating the position information is reduced, and the efficiency of generating the position information is improved. Further, the AI service module can also combine the post information and the attribute information of the recruitment consultant to screen the appropriate recruitment consultant for the post information, and the recruitment consultant provides subsequent recruitment service for the post information.

A solution provided by the embodiments of the present application is described in detail below with reference to the accompanying drawings.

Fig. 1a is a schematic structural diagram of an AI-based recruitment service system according to an exemplary embodiment of the present application. As shown in fig. 1a, the system comprises: recruiter 101, job seeker 102, and artificial intelligence (Artificial Intelligence, AI) service module 103 corresponding to the recruiter. The AI service module is configured to provide recruitment interaction service for a recruitment terminal, where the recruitment interaction service refers to interaction service related to recruitment, such as chat service or short message service. The job hunting end and recruitment end can relate to the field of home administration, the field of freight drivers, the field of renting houses, the field of selling houses by sellers, the field of moving houses and the like.

In the present embodiment, the deployment implementation of each end side is not limited. The recruiter 101 and the job seeker 102 may be deployed on a terminal device respectively. For example, a recruitment application (APPlication, APP) corresponding to the recruitment terminal 101 may be deployed on the terminal device to implement deployment of the recruitment terminal 101 on the terminal device, and correspondingly, a job application APP corresponding to the job application terminal 102 may be deployed on the terminal device to implement deployment of the job application terminal 102 on the terminal device. Alternatively, the recruitment APP and the job-seeking APP may be the same APP.

In addition, the terminal devices that deploy the recruiter 101 and the job seeker 102 can communicate through the server device. The AI service module 103 may be independently deployed on a service device, may be independently deployed on a terminal device corresponding to the recruitment end 101, or may be distributed and deployed on a terminal device corresponding to the recruitment end 101 and a service device, which is not limited.

In this embodiment, the AI service module is mainly configured to provide post information processing services, such as post information mining, for recruitment terminals.

The embodiment of the application provides a post information processing method based on AI besides the system embodiment, and the process of the post information processing method based on AI provided by the embodiment of the application is explained below.

Fig. 1b is a flowchart of an AI-based post information processing method according to an exemplary embodiment of the present application, where the method is applicable to recruitment terminals configured with an AI service module, and the AI service module is at least configured to execute the post information processing method, as shown in fig. 1b, and the method includes:

101b, determining explicit recruitment demand information submitted by a recruiter corresponding to a recruitment end in response to post mining operation on an AI interaction page provided by the recruitment end;

102b, acquiring target recruitment record information of the recruitment user in the history recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users;

103b, inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of a recruiter;

104b, generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and distributing the target post information;

105b, screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and combining the attribute information of the recruitment consultant;

106b assigning at least one target recruiter to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

In this embodiment, the recruitment terminal is provided with an AI interaction page, and the recruiter may interact with the AI service module on the AI interaction page. As shown in fig. 2a and 2b, which illustrate an example in which the AI service module is an AI helper, a schematic diagram of recruiter interaction with the AI helper is shown.

In this embodiment, the recruiter may initiate a post mining operation on the AI interaction page, and the AI service module may determine explicit job demand information submitted by the recruiter in response to the post mining operation. Typically, the explicit recruitment requirement information is structured and low cost to describe, and may be obtained from a recruitment or described by a job seeker.

The manner in which the post mining operation is responded to is not limited. For example, an input box is provided on the AI interaction page, through which the recruiter inputs a job mining instruction to initiate a job mining operation, e.g., help me generate or modify job information, and the AI service module may identify the job mining instruction input by the recruiter, determine explicit recruitment demand information submitted by the recruiter. For example, recruiter inputs "can not help me generate a post message" are illustrated, but are not limited thereto. For another example, the AI service module has a voice recognition function, a voice input control is provided on the AI interaction page, the recruiter can input a voice command based on the voice input control to initiate a post mining operation, and the AI service module recognizes the input voice command to determine explicit recruitment demand information submitted by the recruiter.

In this embodiment, the recruiter may generate target recruitment record information during the historical recruitment process, where the target recruitment record information at least includes: in the historical recruitment process, target communication information of recruiting users and job seekers and target search information of the recruiters are obtained, wherein the target search information of the recruiters comprises at least one of the following: target search information for historic posts, target search information for job-seeking users, and the like. For example, in a historical recruitment process, the target communication information for the recruiter and the job seeker includes at least one of: interaction information, call information, short messages or mails and the like of recruitment users and job-seeking users on the interaction page; the search information for the historic post may be: "Chinese chef", "City name+Chinese chef" or "City name+county/district name+Chinese chef"; the target search information for the job-seeking user may be: "Zhang San+chef".

The recruiter has identification information such as a nickname, account number, identification number (Identity document, ID), or business name of the recruiter, among others. The AI service module maintains the corresponding relation between the identification information of the recruitment users and the target recruitment record information of the recruitment users in the historical recruitment process in advance, and based on the corresponding relation, the AI service module can acquire the target recruitment record information of the recruitment users in the historical recruitment process according to the identification information of the recruitment users.

In this embodiment, the target recruitment record information may be input to the AI language model for data mining, so as to obtain implicit recruitment requirement information of the recruiter. The AI language model is not limited thereto. From the perspective of the technology employed by the AI language model, the types of AI language models include, but are not limited to: a supervised learning model, a semi-supervised learning model, an unsupervised learning model, a reinforcement learning model, a convolutional neural network (Convolutional Neural Networks, CNN) model, a recurrent neural network (Recurrent Neural Networks, RNN) model, a generative model, or an anomaly detection model, etc. From the function implemented by the AI language model, the AI language model includes, but is not limited to: natural language understanding models, image or video understanding models, chat models, intelligent customer service models, AI search engines, multimodal picture generation models, and the like. Further, the AI language model is implemented as: a large language model (Large Language Model, LLM), a generic language generation model (General Language Model, GLM), a language dialogue model based on GLM architecture (ChatGLM-6B), or a variant of the above. LLM is a deep learning model trained using large amounts of text data, where natural language text can be generated or meaning of language text can be understood. The large language model can process various natural language tasks, such as text classification, question-answering, dialogue and the like, and is an important path to artificial intelligence.

The data mining is mainly a process of extracting implicit recruitment demand information from target recruitment record information by using various algorithms through an AI language model. The implicit recruitment requirement information has the characteristics of unstructured, high description cost, long tail effect (differentiated, small quantity of requirements) and the like, for example, the implicit recruitment requirement information can be recruitment preference information, specifically, fine-granularity work place information accurate to county/district or street, relevant benefit treatments or special requirements on working time/place and the like, and the implicit recruitment requirement information needs to be confirmed, clarified or screened by recruiter and recruiter in repeated communication.

In this embodiment, the target post information may be generated according to the explicit recruitment requirement information and the implicit recruitment requirement information, so that richer information is reflected in the target post information, which is beneficial to improving recruitment efficiency. All explicit recruitment demand information and all implicit recruitment demand information can be embodied in the target post information, and part of the explicit recruitment demand information or part of the implicit recruitment demand information can be embodied without limitation.

In this embodiment, after the target post information is generated, the target post information may be issued, for example, to a job-seeking platform or a recruitment platform, so that a job-requiring user may deliver a resume for the target post information, or the recruitment end may obtain resume information adapted to the target post information from a resume information base.

In this embodiment, a recruitment consultant information base is maintained, and the recruitment consultant information base includes a recruitment consultant, and is mainly used for providing recruitment service for a recruitment terminal, assisting the recruitment terminal in screening resume information, and providing resume information meeting recruitment requirements for the recruitment terminal. For example, the job seeker corresponding to the resume information performs at least one round of interaction to ask questions to the job seeker and replies questions to the job seeker, and the resume information meeting the requirements is determined based on the at least one round of interaction.

Wherein the recruiter has attribute information, the attribute information of the recruiter including, but not limited to: basic information of the recruiter, conversion rate of the recruiter, post type labels served by the recruiter, loading threshold of the recruiter, and the like. Wherein the basic information of the recruitment advisor may include, but is not limited to: name, employee number bound, job site, job time, etc. The conversion rate of the recruitment consultant refers to the probability of selecting resume information meeting the recruitment requirement from the candidate resume information. The post type tag refers to the post type served by the recruiter, for example, the post type tag may be "chef," "nurse," "driver," or "jiasao," etc. The loading threshold of the recruiter is a threshold of the amount of target post information that the recruiter is assigned to in a unit time (e.g., one day or one month) or a threshold of the amount of resume information corresponding to the target post information that the recruiter is assigned to in a unit time. For example, the loading threshold of the recruiter may be 2, 5, or 10 posts information, which may correspond to the same recruiter or may correspond to different recruiters, and the 2, 5, or 10 posts information may belong to the same post type tag. For another example, the loading threshold of the recruiter can be 50, 100, or 150 resume messages corresponding to the same post type label post information, which can correspond to the same recruiter or to different recruiters.

The target post information also has attribute information, such as recruitment requirement information, heat information, account level information of the recruiter, service fees provided by the recruiter, and the like. The heat information can embody the social attention of the target post information. The account level information of the recruiting user is not limited, and may be determined according to the activity level, the online time period, etc. of the recruiting user, for example, level 1, level 2, level 4, etc., and may be differentiated by a general account and a member (VIP) account, where the member account may be a paid user. The recruitment user provides a service fee indicating the fee for purchasing the recruitment service for the target post information.

In this embodiment, at least one target recruiter matching the target post information may be screened from the recruitment advisor information library according to the attribute information of the target post information in combination with the attribute information of the recruiter; at least one target recruiter is assigned to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information. It should be noted that, the allocation of at least one target recruiter to the target post information may be: and establishing a binding relationship between at least one target recruitment consultant and target post information, and sending the target post information to the target recruitment consultant, so that the target recruitment consultant can provide recruitment service for a recruiter based on the target post information.

In the embodiment of the application, the AI service module is provided for assisting in mining the implicit recruitment demand information of the recruitment user, and generating the target post information of the recruitment user by combining the explicit recruitment demand information of the recruitment user, so that the time cost for generating the post information is reduced, and the efficiency of generating the post information is improved. Further, the AI service module can also combine the post information and the attribute information of each recruitment consultant to screen the appropriate recruitment consultant for the post information, and the recruitment consultant provides subsequent recruitment service for the post information.

In an alternative embodiment, the AI service module of the recruiter may obtain N candidate resume information for the target post information, where N is a positive integer. For example, after the target post information is released, resume information delivered for the target post information is received, and the delivered N resume information is used as N candidate resume information. For example, N resume information adapted to the target post information is obtained from the resume information base as N candidate resume information, which is not limited. The AI service module can also transmit the N candidate resume information to a terminal device of at least one target recruitment advisor for the at least one target recruitment advisor to select target resume information from the N candidate resume information. The number of the target resume information is less than or equal to the number of the candidate resume information, for example, the target resume information may be one or more, for example, 5 or 10, etc. In fig. 1b, a terminal device 104 of at least one target recruiter is schematically illustrated. For example, the at least one target recruitment consultant performs multiple rounds of interactions with job seekers corresponding to the N candidate resume information through the terminal device. The manner in which the multi-round interaction is performed is not limited. For example, the method can be performed through a micro chat, a telephone or a third party instant messaging tool, etc., wherein the micro chat can be implemented by installing an application program (APP) on a terminal device corresponding to a target recruitment consultant, and the application program (APP) is also adapted to the job seeker, so that the target recruitment consultant and the job seeker can perform multiple interactions based on the APP.

The target resume information can be selected from N candidate resume information through information of multiple rounds of interaction. For example, the matching degree between each candidate resume information and the target post information is determined through information of multiple rounds of interaction, the candidate resume information with the set number of the candidate resume information which is ranked in front is ranked according to the matching degree from top to bottom, or the candidate resume information with the matching degree exceeding a set matching degree threshold (for example, 90% or 95%) is used as the target resume information. The target recruitment consultant can continuously interact with the job seeker corresponding to the target resume information to communicate the reserved interview information and provide the reserved interview information to the recruitment terminal, and further, the target recruitment consultant can also provide the target resume information to the recruitment terminal. The recruitment terminal receives reservation interview information reported by terminal equipment of at least one target recruitment consultant, and interviews the job seeker corresponding to the target resume information based on the reservation interview information.

Optionally, the embodiment for acquiring N candidate resume information for the target post information includes: acquiring M initial resume information delivered for the recruitment terminal; and carrying out initial screening from the M initial resume information based on a preconfigured resume screening rule and a filtering strategy set based on a history outbound record to obtain N candidate resume information, wherein M is a positive integer, and M is more than or equal to N.

In this embodiment, the target post information may include various contents such as post name, post responsibility, job title, and the like. Optionally, the M initial resume information may be obtained according to at least one of the contents included in the target post information, for example, a part of resume information may be fished out of massive data according to a post name, and the initial resume information may also be obtained according to a server push manner, where the server push manner of the resume information may be batch push or full push. Optionally, the recruiter can issue target post information through the recruitment end and generate a corresponding post link, the link page contains a resume delivery control and a mailbox address of the recruiter, the job seeker can click the link to check the post information and deliver the resume through the resume delivery control of the page, or send the resume to a mailbox left by the recruiter, and gather resume information obtained by the two channels and push the resume information to the AI service module.

When the initial resume information delivered to the recruitment end is acquired, at least one of the contents of the target post information can be used for fuzzy matching, for example, conventional fuzzy matching can be used for matching based on the similarity between characters, and fuzzy matching can also be performed by using an AI technology, for example, a deep learning model, an NLP (natural language processing) technology, a supervised learning and clustering method and the like can be used. For example, when training a resume screening model, posts in the near or similar industries can be marked, for example, "takeout" and "courier" belong to the distribution work together, further, the marked training data is used for training the model, then the trained model is used, resume information is input, and a matching result is obtained.

Further, based on a pre-configured resume screening rule and a filtering strategy set based on a history outbound record, initial screening is conducted from M pieces of initial resume information, and N pieces of candidate resume information are obtained. The filtering of the resume information can adopt keyword matching or semantic searching, and the resume information can be evaluated by using a pre-trained model. The resume screening rule may be screening according to education background, working experience, skill matching, geographic location, foreign language capability, and the like, and may also be screening according to a time dimension of resume information, for example, a latest update time, update frequency, and the like of resume information.

In an alternative embodiment, at least one piece of initial resume information which is delivered for the target post information and accords with resume screening rules is screened out from the M pieces of initial resume information according to job seeking posts, delivery time, working places and/or job categories corresponding to the M pieces of initial resume information, and further, the at least one piece of initial resume information is filtered according to a filtering strategy so as to obtain N pieces of candidate resume information; the filtering strategy is set according to the history outbound records, and comprises the following steps: at least one of resume information that the frequency of internal and external calls reaches a certain threshold value within a preset time period, resume information that a target post has been reserved, resume information that a blacklist or a recommendation failure record exists, resume information that a predicted external call or a common external call has occurred, and resume information that a communication record exists within a preset time.

In an optional embodiment, the AI service module corresponding to the recruiting end may obtain at least one attribute information of the heat information of the target post information, the account level information of the recruiting user, and the service fee provided by the recruiting user, where the detailed description of the attribute information of the target post information may refer to the foregoing embodiment, and will not be repeated herein. The embodiment of acquiring at least one attribute information of the target post information is not limited. For example, for account level information of a recruiter, the recruiter maintains virtual account information of the recruiter, the virtual account information including account level information, and the target recruiter may obtain the account level information of the recruiter from the recruiter via the terminal device. For another example, for the service fee provided by the recruiter, the AI service module provides a recruitment service purchase service for the recruitment terminal, and the recruitment interaction services with different service levels correspond to different service fees, for example, the service fee with higher price corresponds to the recruitment interaction service with higher service level, and the service level is high, which can be reflected in the aspects of a large number of target recruiters, high conversion rate of the target recruiters, and the like. For another example, aiming at the heat degree information of the target post information, acquiring the domain heat degree to which the target post information belongs and the user access heat degree corresponding to the target post information; the determination method of the domain heat is not limited, for example, the domain to which the target post information belongs is determined, statistics of user access heat is performed on each post information in the domain, and weighted average is performed on the user access heat of each post information to obtain the domain heat; and carrying out weighted summation on the domain heat and the user access heat to obtain heat information of the target post information.

And determining the conversion rate range of the recruitment consultant required by the target post information according to the at least one attribute information. For example, determining a conversion rate range of the recruitment advisor required by the target post information according to the heat information of the target post information; or determining the conversion rate range of the recruitment consultant required by the target post information according to the account grade information of the recruitment user; or determining the conversion rate range of the recruitment consultant required by the target post information according to the service cost provided by the recruitment user; or, the conversion rate range of the recruitment consultant required by the target post information can be determined according to the heat information of the target post information and the account grade information of the recruitment user; or determining the conversion rate range of the recruitment consultant required by the target post information according to the account grade information of the recruitment user and the service cost provided by the recruitment user; or determining the conversion rate range of the recruitment consultant required by the target post information according to the heat information of the target post information, the account grade information of the recruiter and the service cost provided by the recruiter. The conversion rate can be 50% -60%, 70% -82% or 88% -96% and the like. And acquiring the recruitment consultant with the conversion rate within the conversion rate range of the recruitment consultant required by the target post information from the recruitment consultant information base, wherein the post type label served by the recruitment consultant is matched with the post type information, and the recruitment consultant is used as a candidate recruitment consultant. At least one target recruiter is selected from the candidate recruiters based on the current load information of the candidate recruiters. For example, a candidate recruiter having current load information below a set load threshold is selected as the target recruiter, e.g., where the current load information of the candidate recruiter may be 10 resume information and the load threshold is 50, the candidate recruiter may be the target recruiter. For another example, in the case where the number of post information that can be processed by the load information recruiter and resume information corresponding to the post information is measured, the current load information of the candidate recruiter may be that 10 resume information needs to be processed by the assigned post A1, that 20 resume information needs to be processed by the assigned post A2, that 10 resume information of the post A1 is processed by the load threshold, that 15 resume information of the post A2 is processed, and that the current load information of the candidate recruiter is higher than the load threshold, and that the candidate recruiter cannot be considered as the target recruiter.

Optionally, the heat information of the target post information, the account level information of the recruiter, and the service fee provided by the recruiter have a positive correlation with the conversion rate range of the target recruiter required by the target post information. For example, as the heat information of the target post information increases, the higher the conversion range of the target recruiter is required, e.g., the heat information is X1< X2< X3, the required conversion ranges may be 50% -60%, 60% -70%, and 70% -80%, respectively, where the conversion ranges may include a left endpoint value or a right endpoint value. Based on this, a conversion rate range corresponding to the recruitment user's account level information can be determined as a first candidate conversion rate range according to a first positive correlation existing between the recruitment user's account level information and the conversion rate range; determining a conversion rate range corresponding to the heat information of the target post information as a second candidate conversion rate range according to a second positive correlation relationship existing between the heat information of the target post information and the conversion rate range; determining a conversion rate range corresponding to the service expense provided by the recruiter as a third candidate conversion rate range according to a third positive correlation relationship existing between the service expense provided by the recruiter and the conversion rate range; and determining the conversion rate range of the recruiter required by the target post information according to at least one of the first candidate conversion rate range, the second candidate conversion rate range and the third candidate conversion rate range. For example, the conversion rate range with the highest right endpoint value among the first, second, and third candidate conversion rate ranges is selected as the conversion rate range of the recruiter required for the target job information. For another example, the conversion range with the lowest left endpoint value among the first, second, and third candidate conversion ranges is selected as the conversion range of the recruiter for which the target post information is desired. For another example, the left endpoint value and the right endpoint value in the first candidate conversion rate range, the second candidate conversion rate range, and the third candidate conversion rate range are averaged, respectively, to obtain a new left endpoint value and a new right endpoint value, and the conversion rate ranges corresponding to the new left endpoint value and the new right endpoint value are used as the conversion rate ranges of recruiters required by the target post information.

In an alternative embodiment, the AI service module of the recruiting end sends the N candidate resume information to the terminal device of the at least one target recruitment advisor, which may specifically be: and under the condition that the target recruitment consultants are at least two, acquiring current load information of the at least two target recruitment consultants, and distributing N candidate resume information to terminal equipment of the at least two target recruitment consultants by taking load balancing as a target. For example, the target recruitment consultants are 2, the current load information of the first target recruitment consultant is 5 resume information, the current load information of the second target recruitment consultant is 10 resume information, and the candidate resume information corresponding to the target post information is 7, and then the load balancing is used as a target, one candidate resume information is sent to the terminal equipment of the first target recruitment consultant, and 6 candidate resume information is sent to the terminal equipment of the second target recruitment consultant, so that the load balancing is realized.

In an alternative embodiment, the multiple rounds of interaction information may also be used as target recruitment record information; inputting the target recruitment record information into an AI language model for data mining to obtain new implicit recruitment demand information; and continuously updating, supplementing and enriching the target post information according to the new implicit recruitment demand information so as to improve the recruitment efficiency. For the implementation of data mining, reference may be made to the foregoing embodiments, and details are not repeated herein.

In an alternative embodiment, the implementation of determining the explicit recruitment requirement information submitted by the recruiter corresponding to the recruiter in response to the post mining operation on the AI interaction page provided by the recruiter is not limited. For example, the recruiter can input explicit recruitment demand information on the AI interaction page. Accordingly, the AI service module may obtain the explicit recruitment requirement information submitted by the recruiter corresponding to the recruiter in response to the input operation on the AI interaction page provided by the recruiter, which is specifically referred to the foregoing and will not be described herein. For another example, post mining operations on an AI interaction page provided by the recruitment end are responded, and post information published by the recruitment end is obtained; explicit recruitment demand information for the recruiter is determined from the published post information. For example, explicit recruitment demand information for the recruiter is extracted or identified from the published post information.

In an alternative embodiment, according to the identification information of the recruiter, a history communication record and a history search record of the recruiter in the history recruitment process can be obtained; the history communication records comprise history communication records of recruitment users and each job-seeking user, and information related to the job-seeking user is selected from the history communication records to serve as target communication information according to at least one of attribute information of the job-seeking user, account type information of the job-seeking user and content type adaptively set with target post information; wherein, attribute information of job seeker can include, but is not limited to: nickname, job site, salary range, job time, etc. of job seeker, for example, setting attribute information of job seeker is selected from history communication record as target communication information. The account type information of the job-seeking user may be an account level of the job-seeking user, which may embody creditworthiness, use duration, etc. of the job-seeking user, or the account type of the job-seeking user may be a member or a non-member, which is not limited. For example, a set job-seeking user with a higher reputation than a set reputation threshold may be determined, the reputation threshold may be 80%, 95%, 98%, or the like, and a historical communication record of the recruiter with the set job-seeking user may be used as the target communication information, or a historical communication record of the recruiter with a job-seeking user whose account type is a member may be selected as the target communication information. The set content type adapted to the target post information may be post type, salary level, working city, welfare treatment, etc. in the target post information, and the information related to the set content type adapted to the target post information in the history communication record is used as the target communication information. Information related to at least one of a history post and a job seeker is selected from the history search record as target search information. The information related to the historical position may be information related to the historical position of the recruiter, wherein the information related to the job seeker may be attribute information of the job seeker, as described above in detail.

In an alternative embodiment, a large amount of labeling data is required in the construction process of the traditional machine model, the data of a single post or resume is sparse, the typical long tail effect is achieved, most job seekers and recruiters only have a small amount of dialogue data, and the model training of the traditional machine learning is not supported sufficiently, so that the accuracy of the traditional machine model is lower. The AI language model has powerful language understanding and zero-sample processing capacity, and can complete the functions of data mining, information extraction, text matching and dialogue simulation through designing prompt (prompt) without training. Therefore, the data mining can be performed in an AI language model by adopting a Prompt Learning (Prompt Learning) mode, so that the data mining capability of the AI language model can be better realized by adding a Prompt template to the input of the AI language model without significantly changing the structure and parameters of the AI language model. The prompt template is mainly used for guiding the AI language model to output the content of the set type.

Based on the above, the target recruitment record information can be input into the AI language model, and different paths are adopted for analyzing the target communication information and the target search information.

The target communication information is analyzed by adopting a first prompt template trained in advance aiming at the target communication information, so that first interaction information and second interaction information respectively corresponding to the job seeker and the recruiter are obtained. Wherein, first suggestion template includes at least: the alert information associated with the job seeking user and the recruiter may be, for example, identification information, voiceprint information, or other alert information. The information form of the first interaction information and the second interaction information can be graphic information, question-answer pair information, a moving picture and link information carried by the card, or can be voice information or voice interaction control and the like. Extracting target interaction information matched with target post information from the first interaction information and the second interaction information; analyzing at least one question information of the job seeker from the target interaction information, and at least one answer information obtained by replying to the at least one question by the recruiter so as to obtain at least one first question-answer pair.

And analyzing the target search information by adopting a second prompting template which is generated in advance aiming at the target search information to obtain at least one search keyword which is matched with the target post information. The second prompting template at least comprises prompting information related to recruitment, such as working time, working skill, personal experience and the like of the job seeker. For example, the search keyword may be "skill A1", "personal item experience A2", or "accept business trip"; at least one second question-answer pair is generated according to the search keyword, for example, the search keyword is: "skill A1", the second question-answer pair may be: what skills are mastered? [ skill A1 ]; the search keywords are: "personal item experience A2", the second question-answer pair may be: what work experience? [ personal project experience A2 ]; the search keywords are: "accept business trip", the second question-answer pair may be: is it accepted that irregular business trips? [ accept business trip ].

And generating implicit recruitment demand information of the recruiter according to the at least one first question-answer pair and the at least one second question-answer pair. For example, all of the first question-answer pairs and all of the second question-answer pairs are directly used as implicit recruitment demand information for the recruiter. For another example, a portion of the first question-answer pair and a portion of the second question-answer pair are used as implicit recruitment demand information for the recruiter.

In an alternative embodiment, the implicit recruitment demand information and the explicit recruitment demand information are expressed in a question-answer pair mode, and the question-answer pair for expressing the implicit recruitment demand information and the explicit recruitment demand information is added on the basis of the explicit recruitment demand information so as to obtain the target post information. The question-answer pair expressing the implicit recruitment requirement information can be realized as the first question-answer pair and the second question-answer pair; the explicit recruitment requirement information can be divided into two parts, one part is expressed in the form of an information item and description information thereof, for example, part of the description information and the category thereof in the explicit recruitment requirement information are embodied in target post information, for example, the description information is chefs, a market requires 5 years of roulette, and the information item-description information is realized as follows: post: a chef; work site: market A; the working experience requires: rue dish for 5 years. The other part is expressed in terms of question-answer pairs, for example, [ expected working time? [ 10 am to 9 pm ]. The question-answer pairs in the target post information are shown in fig. 2a and 2b, respectively, the question-answer pairs corresponding to the post information are shown in fig. 2a, and the question-answer pairs corresponding to the post preferences (recruitment preferences) are shown in fig. 2 b. The question-answer pairs are not distinguished in fig. 2a and 2b from belonging to either explicit recruitment demand information or implicit recruitment demand information. It should be noted that, in fig. 2a and 2b, the AI interaction page includes two areas, one is an interaction area, and the other is a display area, where a recruiter interacts with a recruiter, and where target resume information is displayed and editing of question-answer pairs is supported, where fig. 2a and 2b are merely exemplary and not limiting.

In an alternative embodiment, whether to activate the AI service module may be preconfigured on the recruiter. For example, an AI service configuration page is provided on the recruiting side, and a service activation operation may be initiated on the AI service configuration page on either side of the recruiting side, to activate an AI service module that provides post mining services for the recruiting side. For example, the AI service configuration page includes: the control is activated and the service activation operation may be implemented as a click operation for the activated control.

In an alternative embodiment, the AI service module may be purchased at the recruiter in advance, such that the AI service module may provide post mining services to the recruiter. An AI service selection page may be provided at the recruiting end, the AI service page includes an AI service to be selected, a selection operation for the AI service to be selected may be initiated on the AI service selection page at any one of the recruiting ends, and the AI service module may determine, in response to the selection operation on the AI service selection page, to activate an AI service module corresponding to the selection operation for the recruiting end.

The detailed implementation and the beneficial effects of each step in the method shown in fig. 1b provided in the embodiment of the present application have been described in detail in the foregoing embodiments, and will not be described in detail herein.

It should be noted that, the execution subjects of each step of the method provided in the above embodiment may be the same device, or the method may also be executed by different devices. For example, the execution subject of steps 101B to 103B may be device B; for another example, the execution subject of steps 101B and 102B may be device B, and the execution subject of step 103B may be device B; etc.

In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that the operations may be performed out of the order in which they appear herein or performed in parallel, the sequence numbers of the operations such as 101b, 102b, etc. are merely used to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

Fig. 3 is a schematic structural diagram of an AI-based post information processing apparatus according to an exemplary embodiment of the present application, and as shown in fig. 3, the apparatus includes: AI service module 30.

The AI service module 30 is configured to determine, in response to a post mining operation on an AI interaction page provided by a recruiter, explicit recruitment demand information submitted by a recruiter corresponding to the recruiter; acquiring target recruitment record information of the recruitment user in the historical recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users; inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of a recruitment user; generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and distributing the target post information; screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and combining the attribute information of the recruitment consultant; at least one target recruiter is assigned to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

In an alternative embodiment, the AI service module is further for: acquiring N candidate resume information aiming at target post information; transmitting the N candidate resume information to terminal equipment of at least one target recruitment consultant so that the at least one target recruitment consultant can select target resume information from the N candidate resume information, wherein N is a positive integer; and receiving reservation interview information reported by terminal equipment of at least one target recruitment consultant, wherein the reservation interview information is determined by multiple rounds of interaction between the at least one target recruitment consultant and a job hunting terminal corresponding to the target resume information through the terminal equipment.

In an alternative embodiment, the AI service module is specifically configured to: acquiring at least one attribute information of heat information of target post information, account grade information of recruitment user and service cost provided by recruitment user; determining a conversion rate range of the recruitment consultant required by the target post information according to at least one attribute information; acquiring a recruitment consultant with conversion rate within the conversion rate range of the recruitment consultant required by the target post information from a recruitment consultant information base, wherein the post type label served by the recruitment consultant is used as a candidate recruitment consultant by adapting to the post type information; selecting at least one target recruiter from the candidate recruiters based on current load information of the candidate recruiters, the load information comprising: assigned post information and/or resume information assigned on the post information.

Optionally, the AI service module is specifically configured to: acquiring the domain heat of the target post information and the user access heat corresponding to the target post information; and carrying out weighted summation on the domain heat and the user access heat to obtain heat information of the target post information.

Optionally, the AI service module is specifically configured to: determining a conversion rate range corresponding to the account level information of the recruitment user as a first candidate conversion rate range according to a first positive correlation existing between the account level information of the recruitment user and the conversion rate range; determining a conversion rate range corresponding to the heat information of the target post information as a second candidate conversion rate range according to a second positive correlation relationship existing between the heat information of the target post information and the conversion rate range; determining a conversion rate range corresponding to the service expense provided by the recruiter as a third candidate conversion rate range according to a third positive correlation relationship existing between the service expense provided by the recruiter and the conversion rate range; and determining the conversion rate range of the recruiter required by the target post information according to at least one of the first candidate conversion rate range, the second candidate conversion rate range and the third candidate conversion rate range.

In an alternative embodiment, the AI service module is specifically configured to: and under the condition that the target recruitment consultants are at least two, acquiring current load information of the at least two target recruitment consultants, and distributing N candidate resume information to terminal equipment of the at least two target recruitment consultants by taking load balancing as a target.

In an alternative embodiment, the AI service module is specifically configured to: acquiring M initial resume information delivered for the recruitment terminal; and carrying out initial screening from the M initial resume information based on a preconfigured resume screening rule and a filtering strategy set based on a history outbound record to obtain N candidate resume information, wherein M is a positive integer, and M is more than or equal to N.

The detailed implementation and the beneficial effects of each step in the apparatus shown in fig. 3 provided in the embodiment of the present application have been described in detail in the foregoing embodiments, and will not be described in detail herein.

Fig. 4 is a schematic structural diagram of an AI-based post information processing apparatus according to an exemplary embodiment of the present application, as shown in fig. 4, the apparatus includes: a memory 44 and a processor 45.

Memory 44 is used to store a computer program corresponding to the AI service module and may be configured to store various other data to support operations on the AI-based post information processing device. Examples of such data include instructions or the like for any application or method operating on the AI-based post information processing device.

A processor 45 coupled to the memory 44 for executing the computer program in the memory 44 for: determining explicit recruitment demand information submitted by a recruiter corresponding to a recruitment end in response to a post mining operation on an AI interaction page provided by the recruitment end; acquiring target recruitment record information of the recruitment user in the historical recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users; inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of a recruitment user; generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and distributing the target post information; screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and combining the attribute information of the recruitment consultant; at least one target recruiter is assigned to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

In an alternative embodiment, processor 45 is further configured to: acquiring N candidate resume information aiming at target post information; transmitting the N candidate resume information to terminal equipment of at least one target recruitment consultant so that the at least one target recruitment consultant can select target resume information from the N candidate resume information, wherein N is a positive integer; and receiving reservation interview information reported by terminal equipment of at least one target recruitment consultant, wherein the reservation interview information is determined by multiple rounds of interaction between the at least one target recruitment consultant and a job hunting terminal corresponding to the target resume information through the terminal equipment.

In an alternative embodiment, the processor 45 is configured to, in combination with the recruiter attribute information, filter at least one target recruiter from the recruiter information base that matches the target post information based on the target post information attribute information, specifically: acquiring at least one attribute information of heat information of target post information, account grade information of recruitment user and service cost provided by recruitment user; determining a conversion rate range of the recruitment consultant required by the target post information according to at least one attribute information; acquiring a recruitment consultant with conversion rate within the conversion rate range of the recruitment consultant required by the target post information from a recruitment consultant information base, wherein the post type label served by the recruitment consultant is used as a candidate recruitment consultant by adapting to the post type information; selecting at least one target recruiter from the candidate recruiters based on current load information of the candidate recruiters, the load information comprising: assigned post information and/or resume information assigned on the post information.

Optionally, the processor 45 is specifically configured to, when acquiring the heat information of the target post information: acquiring the domain heat of the target post information and the user access heat corresponding to the target post information; and carrying out weighted summation on the domain heat and the user access heat to obtain heat information of the target post information.

Optionally, the processor 45 is configured to, in determining the recruiter conversion range required for the target job information based on the at least one attribute information, specifically: determining a conversion rate range corresponding to the account level information of the recruitment user as a first candidate conversion rate range according to a first positive correlation existing between the account level information of the recruitment user and the conversion rate range; determining a conversion rate range corresponding to the heat information of the target post information as a second candidate conversion rate range according to a second positive correlation relationship existing between the heat information of the target post information and the conversion rate range; determining a conversion rate range corresponding to the service expense provided by the recruiter as a third candidate conversion rate range according to a third positive correlation relationship existing between the service expense provided by the recruiter and the conversion rate range; and determining the conversion rate range of the recruiter required by the target post information according to at least one of the first candidate conversion rate range, the second candidate conversion rate range and the third candidate conversion rate range.

In an alternative embodiment, the processor 45 is configured to, when transmitting the N candidate resume information to the terminal device of the at least one target recruiter: and under the condition that the target recruitment consultants are at least two, acquiring current load information of the at least two target recruitment consultants, and distributing N candidate resume information to terminal equipment of the at least two target recruitment consultants by taking load balancing as a target.

In an alternative embodiment, processor 45, when acquiring N candidate resume information for the target post information, is specifically configured to: acquiring M initial resume information delivered for the recruitment terminal; and carrying out initial screening from the M initial resume information based on a preconfigured resume screening rule and a filtering strategy set based on a history outbound record to obtain N candidate resume information, wherein M is a positive integer, and M is more than or equal to N.

The detailed implementation and the beneficial effects of the steps in the apparatus shown in fig. 4 provided in the embodiment of the present application have been described in detail in the foregoing embodiments, and will not be described in detail herein.

Further, as shown in fig. 4, the AI-based post information processing apparatus further includes: communication component 46, display 47, power supply component 48, audio component 49, and other components. Only a part of the components are schematically shown in fig. 4, which does not mean that the AI-based post information processing apparatus includes only the components shown in fig. 4. In addition, the components within the dashed box in fig. 4 are optional components, and not necessarily optional components, and may depend on the product form of the AI-based post information processing apparatus. The AI-based post information processing device of this embodiment may be implemented as a terminal device such as a desktop computer, a notebook computer, a smart phone, or an IOT device.

Detailed implementations and advantageous effects of the AI-based post information processing apparatus provided by the embodiments of the present application have been described in the foregoing embodiments, and will not be described in detail herein.

Accordingly, the embodiment of the present application further provides a computer readable storage medium storing a computer program, where the computer program when executed is capable of implementing the steps executable by the session state synchronization device in the method embodiment shown in fig. 1 b.

The Memory may be implemented by any type or combination of volatile or non-volatile Memory devices, such as Static Random-Access Memory (SRAM), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read Only Memory, EEPROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.

The communication component is configured to facilitate wired or wireless communication between the device in which the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as a mobile communication network of WiFi,2G, 3G, 4G/LTE, 5G, etc., or a combination thereof. In one exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component further includes a near field communication (Near Field Communication, NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on radio frequency identification (Radio Frequency Identification, RFID) technology, infrared data association (Infrared Data Association, irDA) technology, ultra Wideband (UWB) technology, blueTooth (BT) technology, and other technologies.

The display includes a screen, which may include a liquid crystal display (Liquid Crystal Display, LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or sliding action, but also the duration and pressure associated with the touch or sliding operation.

The power supply component provides power for various components of equipment where the power supply component is located. The power components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the devices in which the power components are located.

The audio component described above may be configured to output and/or input an audio signal. For example, the audio component includes a Microphone (MIC) configured to receive external audio signals when the device in which the audio component is located is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may be further stored in a memory or transmitted via a communication component. In some embodiments, the audio assembly further comprises a speaker for outputting audio signals.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, magnetic disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (Central Processing Unit, CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random access memory (Random Access Memory, RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase-change memory (Phase-change Random Access Memory, PRAM), static Random Access Memory (SRAM), dynamic random access memory (Dynamic Random Access Memory, DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital versatile disks (Digital Video Disc, DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by the computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. An AI-based post information processing method, which is suitable for recruiting terminals configured with an AI service module, wherein the AI service module is at least used for executing the post information processing method, and the post information processing method comprises:

determining explicit recruitment demand information submitted by a recruiter corresponding to the recruitment end in response to a post mining operation on an AI interaction page provided by the recruitment end;

acquiring target recruitment record information of the recruitment user in the history recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users;

inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of the recruitment user;

Generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and issuing the target post information; and

screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and combining the attribute information of the recruitment consultant;

assigning the at least one target recruiter to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

2. The method as recited in claim 1, further comprising:

acquiring N candidate resume information aiming at the target post information;

transmitting the N candidate resume information to terminal equipment of at least one target recruitment consultant so that the at least one target recruitment consultant can select target resume information from the N candidate resume information, wherein N is a positive integer;

and receiving reservation interview information reported by terminal equipment of at least one target recruitment consultant, wherein the reservation interview information is determined by the at least one target recruitment consultant through multi-round interaction between the terminal equipment and a job seeker corresponding to the target resume information.

3. The method of claim 1 or 2, wherein screening at least one target recruiter from a library of recruiter information for a match with the target post information based on the attribute information of the target post information in combination with the attribute information of the recruiter comprises:

acquiring at least one attribute information of the heat information of the target post information, the account grade information of the recruitment user and the service cost provided by the recruitment user;

determining a conversion rate range of the recruitment consultant required by the target post information according to the at least one attribute information;

acquiring the conversion rate from a recruitment consultant information base, wherein the conversion rate is in the conversion rate range of the recruitment consultant required by the target post information, and the recruitment consultant with the post type label served by the recruitment consultant and the post type information matched serves as a candidate recruitment consultant;

selecting at least one target recruiter from the candidate recruiters according to current load information of the candidate recruiters, wherein the load information comprises: assigned post information and/or resume information assigned on the post information.

4. The method of claim 3, wherein obtaining the heat information of the target post information comprises:

Acquiring the domain heat to which the target post information belongs and the user access heat corresponding to the target post information;

and carrying out weighted summation on the domain heat and the user access heat to obtain heat information of the target post information.

5. The method of claim 3, wherein determining the recruiter conversion range required for the target post information based on the at least one attribute information comprises:

determining a conversion rate range corresponding to the account level information of the recruitment user as a first candidate conversion rate range according to a first positive correlation existing between the account level information of the recruitment user and the conversion rate range;

determining a conversion rate range corresponding to the heat information of the target post information as a second candidate conversion rate range according to a second positive correlation relationship existing between the heat information of the target post information and the conversion rate range;

determining a conversion rate range corresponding to the service expense provided by the recruitment user as a third candidate conversion rate range according to a third positive correlation relationship existing between the service expense provided by the recruitment user and the conversion rate range;

and determining the conversion rate range of the recruiter required by the target post information according to at least one of the first candidate conversion rate range, the second candidate conversion rate range and the third candidate conversion rate range.

6. The method of claim 2, wherein transmitting the N candidate resume information to the terminal device of at least one target recruitment advisor comprises:

and under the condition that the target recruitment consultants are at least two, acquiring current load information of the at least two target recruitment consultants, taking load balancing as a target, and distributing the N candidate resume information to terminal equipment of the at least two target recruitment consultants.

7. The method of claim 2, wherein obtaining N candidate resume information for the target post information comprises:

acquiring M initial resume information delivered for the recruitment terminal;

and carrying out initial screening from the M initial resume information based on a preconfigured resume screening rule and a filtering strategy set based on a history outbound record to obtain N candidate resume information, wherein M is a positive integer, and M is more than or equal to N.

8. An AI-based post information processing apparatus, the apparatus comprising: an AI service module;

the AI service module is used for responding to the position mining operation on the AI interaction page provided by the recruitment terminal and determining the explicit recruitment demand information submitted by the recruitment user corresponding to the recruitment terminal; acquiring target recruitment record information of the recruitment user in the history recruitment process according to the identification information of the recruitment user, wherein the target recruitment record information at least comprises: target communication information with job-seeking users in the history recruitment process and target search information aiming at historic posts and/or job-seeking users; inputting the target recruitment record information into an AI language model for data mining to obtain implicit recruitment demand information of the recruitment user; generating target post information according to the explicit recruitment demand information and the implicit recruitment demand information, and issuing the target post information; screening at least one target recruitment consultant matched with the target post information from a recruitment consultant information base according to the attribute information of the target post information and the attribute information of the recruitment consultant; assigning the at least one target recruiter to the target post information for the at least one target recruiter to provide subsequent recruitment services for the target post information.

9. An AI-based post information processing apparatus, comprising: a memory and a processor; the memory is used for storing a computer program corresponding to the AI service module; the processor, coupled to the memory, for executing the computer program to implement the steps in the method of any of claims 1-7.

10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1-7.