Multi-platform recruitment tweet generation method and system and storage medium

Abstract

The invention relates to the technical field of enterprise recruitment, and discloses a recruitment tweet generation method and system for multiple platforms and a storage medium, and the method comprises the steps: collecting feedback data to obtain a generation strategy, obtaining update data to obtain update content, evaluating to obtain an update strategy, obtaining a first recruitment tweet, and updating the first recruitment tweet. The system and the storage medium both correspond to the method. According to the method, the related recruitment information is comprehensively and dynamically mastered, and the tweet content is optimized according to the response and the demand of the applicant based on the determined generation strategy; the recruitment tweet is ensured to reflect the latest demand of the enterprise by combining the generated update content; evaluating the feedback data and the generation strategy to obtain an updating strategy, and accurately screening out a platform needing to update the tweet; and updating the tweet based on the generation strategy and the update content, realizing recruitment tweet dynamic optimization on multiple platforms, improving the recruitment efficiency and effect, helping enterprises attract talents meeting requirements, improving the recruitment quality, reducing the recruitment cost, and enhancing the competitiveness of the enterprises in the talent market.

Description

Technical Field

[0001] The present application relates to the field of enterprise recruitment technology, and specifically to a method, system, and storage medium for generating recruitment tweets for multiple platforms. Background Art

[0002] With the widespread adoption of the internet, leveraging multiple platforms for recruitment has become an important way for companies to acquire talent. Posting recruitment tweets across multiple platforms can expand the reach of recruitment information and reach a wider range of potential job seekers. However, the current generation of recruitment tweets across multiple platforms presents numerous problems, specifically:

[0003] 1. Content generation lacks relevance and consistency. Existing methods struggle to generate personalized tweets that appeal to job seekers, tailored to the user characteristics of different platforms and corporate needs. Furthermore, it's difficult to ensure consistency in key messaging and style across multiple platforms, impacting the company's image.

[0004] 2. Regarding platform adaptation, the adaptation dimension is limited and lacks dynamic adjustment. Existing technologies select delivery platforms and time periods based solely on partial data, failing to consider differences in platform formats and styles. Tweets are also unable to be optimized in a timely manner based on platform changes. Furthermore, cost control is neglected, and delivery optimization is not integrated with platform charging models, which can easily lead to increased recruitment costs for companies.

[0005] Chinese patent number CN118733892B discloses a method and system for intelligently recommending recruitment copy placement strategies. This invention primarily focuses on recruitment copy placement strategies, but does not delve into the generation of recruitment tweet content or consider content consistency. Regarding multi-platform adaptation, it only considers user behavior data, without fully considering platform format and style requirements, and lacks dynamic adaptation adjustments. Furthermore, it fails to consider recruitment costs and a comprehensive tweet update mechanism, making it difficult to meet companies' needs for efficient and low-cost recruitment. Summary of the Invention

[0006] The purpose of this application is to provide a method, system and storage medium for generating recruitment tweets for multiple platforms to solve the technical problems raised in the above background technology.

[0007] To achieve the above objectives, this application discloses the following technical solutions:

[0008] In a first aspect, the present application discloses a method for generating recruitment tweets for multiple platforms, the method comprising the following steps:

[0009] S1: Collect feedback data from various platforms regarding the initial recruitment tweet, and derive a generation strategy based on the feedback data; wherein the feedback data is used to characterize applicants' responses to the content of the recruitment tweet and their new requirements, and the generation strategy is used to generate the recruitment tweet;

[0010] S2: Obtaining updated data of the recruiting company and obtaining updated content based on the updated data; wherein the updated data is used to represent changes in the recruiting company's employment needs, and the updated content is used to generate recruitment tweets;

[0011] S3: Evaluate the feedback data and the corresponding generation strategy to obtain an update strategy, where the evaluation is based on the generation frequency of the feedback data and the quality of the generation strategy; wherein the update strategy is used to screen platforms for updating recruitment tweets;

[0012] S4: Based on the generation strategy and the update content, generate a first recruitment tweet for the initial recruitment tweet of the platform that meets the update strategy and update the first recruitment tweet.

[0013] Preferably, the feedback data collected from various platforms regarding the initial recruitment tweets in S1 includes:

[0014] At least collecting the number of clicks, reading time, number of shares, and comment content of the applicants on the initial recruitment tweet on various platforms as the first response feedback to the initial recruitment tweet;

[0015] Based on a preset online consultation feedback portal, collecting information that the applicants wish to know but is not covered in the initial recruitment tweet as a second response feedback to the initial recruitment tweet;

[0016] Using the platform's own data analysis tools, we collected detailed information on applicant responses and new demand feedback from different time periods, regions, and user groups on the platform as the third response feedback;

[0017] The first response feedback, the second response feedback, and the third response feedback are packaged and defined as the feedback data.

[0018] Preferably, obtaining a generation strategy based on the feedback data in S1 includes:

[0019] Analyzing the applicant's attention to different recruitment contents in the first response feedback, and determining the content to be highlighted based on the attention;

[0020] Analyze the new demand feedback from the applicant in the second response feedback and the third response feedback, and determine the content that needs to be supplemented and improved in the recruitment tweet based on the new feedback demand;

[0021] The generation strategy is defined as packaging the determined content that needs to be highlighted and the determined content that needs to be supplemented in the recruitment tweets.

[0022] Preferably, obtaining updated data of recruiting companies in S2 includes:

[0023] Obtaining job data related to positions in the human resources of recruiting companies;

[0024] Obtain data on changes in the recruitment company's employment needs during its own development;

[0025] The position data and the change data are packaged and defined as the update data.

[0026] Preferably, obtaining updated content based on the updated data in S2 includes:

[0027] Determining detailed information of a new position based on the position data;

[0028] Determining talent requirement information and corresponding key information for attracting talent based on the change data;

[0029] The updated content is defined as the detailed information of the newly added positions, the determined talent requirement information, and the corresponding key information for attracting talents are packaged.

[0030] Preferably, S3 includes:

[0031] Counting the generation frequency of the feedback data, and defining a generation frequency threshold that satisfies a preset value as a first update constraint condition;

[0032] The actual push effect index of recruitment tweets on each platform is calculated based on the recruitment conversion rate and the match between applicants and positions. The threshold of the actual push effect index that meets the preset threshold is defined as the second update constraint condition;

[0033] The first update constraint condition and the second update constraint condition are defined as the update policy.

[0034] Preferably, in S4, the following is included:

[0035] parsing the updated content, and adjusting the word count, layout, and frequency of occurrence of the updated content in the initial recruitment tweet based on the generation strategy, and defining the adjusted initial recruitment tweet as the first recruitment tweet;

[0036] A platform that meets the update strategy is screened, and the initial recruitment tweet of the platform is updated to the first recruitment tweet.

[0037] Preferably, in S4, the method further includes:

[0038] Obtaining the recruitment tweet charging categories and preset recruitment budget of the platform that meets the update strategy; wherein the recruitment tweet charging categories include at least word count charging rules, page layout charging rules, and push frequency charging rules;

[0039] Based on a comparison between the initial recruitment tweet and the first recruitment tweet, combined with the recruitment tweet charging rules and the preset recruitment budget, the recruitment cost of updating to the first recruitment tweet on the platform is calculated. When the recruitment cost does not meet the recruitment budget, a new recruitment tweet is generated to obtain a third recruitment tweet and updated. When generating the third recruitment tweet, a restrictive adjustment is made to the generated recruitment tweet based on the recruitment tweet charging category, and the restrictive adjustment includes at least an adjustment to the word count, layout, and push frequency of the recruitment tweet.

[0040] In a second aspect, the present application discloses a system for generating recruitment tweets for multiple platforms. The system is applicable to the above-described method for generating recruitment tweets for multiple platforms. The system includes a generation strategy module, an update content module, an update strategy module, and a recruitment tweet generation and update module. The generation strategy module, the update content module, and the update strategy module are all in communication with the recruitment tweet generation and update module.

[0041] The generation strategy module is configured to collect feedback data from various platforms regarding the initial recruitment tweet and derive a generation strategy based on the feedback data; wherein the feedback data is used to characterize applicants' responses to the content of the recruitment tweet and their new requirements, and the generation strategy is used to generate the recruitment tweet;

[0042] The update content module is configured to: obtain updated data of the recruiting company and obtain updated content based on the updated data; wherein the updated data is used to represent changes in the recruitment company's employment needs, and the updated content is used to generate recruitment tweets;

[0043] The update strategy module is configured to: evaluate the feedback data and the corresponding generation strategy to obtain an update strategy, wherein the evaluation is performed based on the generation frequency of the feedback data and the quality of the generation strategy; wherein the update strategy is used to screen platforms for updating recruitment tweets;

[0044] The recruitment tweet generation and update module is configured to: based on the generation strategy and the update content, generate a first recruitment tweet for the initial recruitment tweet of the platform that meets the update strategy and obtain the first recruitment tweet and update it.

[0045] In a third aspect, the present application discloses a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned method for generating recruitment tweets for multiple platforms is implemented.

[0046] Beneficial effects: The recruitment tweet generation method, system and storage medium for multiple platforms of the present application utilize the feedback data of each platform on the initial recruitment tweets and the updated data of the recruiting companies to achieve comprehensive and dynamic grasp of recruitment-related information, determine the generation strategy based on the analysis of the feedback data, and optimize the tweet content according to the responses and needs of the applicants to make it more attractive and targeted; generate updated content in combination with the updated data of the changes in the company's employment needs to ensure that the recruitment tweets reflect the latest needs of the company in a timely manner; evaluate the feedback data and generation strategy to obtain the update strategy, accurately screen out the platforms that need to update the tweets, and avoid waste of resources; finally, update the tweets based on the generation strategy and the updated content, realize dynamic optimization of recruitment tweets on multiple platforms, improve recruitment efficiency and effectiveness, help companies more efficiently attract talents that meet their needs, improve recruitment quality, reduce recruitment costs, and enhance the competitiveness of companies in the talent market. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 A flowchart of a method for generating recruitment tweets for multiple platforms provided in an embodiment of the present application;

[0049] Figure 2 This is a structural block diagram of a recruitment tweet generation system for multiple platforms provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following is a clear and complete description of the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] In this document, the term "comprising" is intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0052] The first aspect of this embodiment discloses Figure 1 A method for generating recruitment tweets for multiple platforms is shown, and the method includes the following steps:

[0053] S1: Collect feedback data from various platforms regarding the initial recruitment tweets and develop a generation strategy based on this feedback data. The feedback data is used to characterize applicants' responses to the content in the recruitment tweets and their new requirements, and the generation strategy is used to generate the recruitment tweets.

[0054] S2: Obtain updated data from the recruiting company and generate updated content based on the updated data; the updated data is used to represent changes in the recruiting company's employment needs, and the updated content is used to generate recruitment tweets;

[0055] S3: Evaluate the feedback data and the corresponding generation strategy to obtain an update strategy. This evaluation is based on the frequency of feedback data generation and the quality of the generation strategy. The update strategy is used to select platforms that update recruitment tweets.

[0056] S4: Based on the generation strategy and the update content, the initial recruitment tweets of the platform that meet the update strategy are generated to obtain the first recruitment tweet and update it.

[0057] Through the above, this embodiment utilizes the feedback data collected from various platforms on the initial recruitment tweets and obtains the updated data of the recruiting companies to achieve comprehensive and dynamic grasp of recruitment-related information, determine the generation strategy based on the analysis of feedback data, and optimize the tweet content according to the responses and needs of applicants to make it more attractive and targeted; generate updated content in combination with the updated data of changes in the company's employment needs to ensure that recruitment tweets reflect the latest needs of the company in a timely manner; evaluate the feedback data and generation strategy to obtain the update strategy, accurately screen out the platforms that need to update tweets, and avoid waste of resources; finally, update tweets based on the generation strategy and updated content, realize dynamic optimization of recruitment tweets on multiple platforms, improve recruitment efficiency and effectiveness, help companies more efficiently attract talents that meet their needs, improve recruitment quality, reduce recruitment costs, and enhance the competitiveness of companies in the talent market.

[0058] Specifically, the feedback data collected from various platforms regarding the initial recruitment tweets in S1 includes:

[0059] At least collect the number of clicks, reading time, number of shares, and comments of candidates on the initial recruitment tweets on various platforms as the first response feedback to the initial recruitment tweets;

[0060] Based on the preset online consultation feedback portal, the information that candidates want to know but is not covered in the initial recruitment tweet is collected as the second response feedback to the initial recruitment tweet;

[0061] Using the platform's own data analysis tools, we collected detailed information on applicant responses and new demand feedback from different time periods, regions, and user groups on the platform as the third response feedback;

[0062] The first response feedback, the second response feedback, and the third response feedback are packaged and defined as feedback data.

[0063] Through the above, this embodiment uses the data collected from applicants on various platforms regarding the number of clicks, reading time, number of shares, and comment content on the initial recruitment tweets, as well as the expected information collected by the preset consultation portal using existing interactive technology and the segmented data collected by the platform's own tools, to achieve multi-dimensional and refined acquisition of feedback data. Based on this, it comprehensively reflects the applicants' focus, interest, and information needs for recruitment tweets, providing a detailed basis for subsequent generation strategies. The generation strategy determined based on these data can accurately highlight the content that applicants pay close attention to, supplement and improve the information they expect to know, make recruitment tweets more in line with applicants' needs, improve the attractiveness and effectiveness of tweets, and thus enhance the dissemination effect of recruitment information on various platforms, increase the interaction between companies and potential job seekers, and improve recruitment efficiency.

[0064] Specifically, the generation strategy based on the feedback data in S1 includes:

[0065] Analyze the applicants' attention to different recruitment contents in the first response feedback, and determine the content to be highlighted based on this attention;

[0066] Analyze the new requirements provided by the applicants in the second and third responses, and determine the content that needs to be supplemented or improved in the recruitment tweet based on the new requirements;

[0067] The content that needs to be highlighted and the content that needs to be supplemented in the recruitment tweets are packaged and defined as the generation strategy.

[0068] Based on the above, this embodiment uses analysis of first-response feedback to determine which content should be highlighted, and combines this with second and third-response feedback to determine which content needs to be supplemented and improved, thus achieving targeted optimization of recruitment tweet content. By focusing on applicants' interest in different recruitment content, the tweet can highlight key and attractive parts, quickly capturing applicants' attention. Supplementing content based on new demand feedback can fill information gaps and meet applicants' information needs. The resulting integrated generation strategy ensures that recruitment tweets are rich in content, highlight key points, and highly align with applicants' expectations, enhancing the tweets' appeal and persuasiveness to potential job seekers, improving the effectiveness of recruitment information dissemination, and laying the foundation for companies to attract more suitable talent.

[0069] Specifically, the updated data of recruiting companies in S2 includes:

[0070] Obtaining job data related to positions in the human resources of recruiting companies;

[0071] Obtain data on changes in the recruitment company's employment needs during its own development;

[0072] The position data and change data are packaged and defined as update data.

[0073] Through the above, this embodiment uses the position data obtained from the human resources of the recruiting enterprise and the data on changes in employment needs during its own development to fully grasp the dynamic changes in the enterprise's recruitment needs. Position data can clarify the specific information of the recruitment position, which includes at least salary and work system. The data on changes in employment needs reflects the new needs of the enterprise due to development. The new needs include at least skill requirements and educational requirements. The combination of the two provides support for generating accurate and timely updated content. The updated content generated based on these data can accurately reflect the company's latest recruitment position details, talent requirements and key information for attracting talents, so that recruitment tweets are closely synchronized with the actual needs of the enterprise, improve the accuracy and effectiveness of recruitment tweets, help enterprises more accurately attract talents that meet the job requirements, and improve the accuracy and efficiency of recruitment.

[0074] Specifically, obtaining updated content based on the updated data in S2 includes:

[0075] Determine detailed information of new positions based on position data;

[0076] Determine talent requirements and corresponding key information for attracting talent based on change data;

[0077] The detailed information of the newly added positions, the determined talent requirements and the corresponding key information for attracting talents are packaged and defined as update content.

[0078] Through the above, this embodiment uses job data to determine new job information, and combines it with change data to determine talent requirements and key information for attracting talent, thereby effectively updating the content of recruitment tweets. New job information can promptly display the company's job vacancies to job seekers, talent requirements information allows job seekers to clearly understand whether they are a good match for the position, and key information for attracting talent highlights the company's advantages and enhances its appeal to job seekers. Integrating this information into updated content makes recruitment tweets more timely and attractive, better meeting the recruitment needs of companies and the job seekers' job expectations, promoting efficient matching between companies and job seekers, improving recruitment results, helping companies quickly find suitable talent, and promoting corporate development.

[0079] Specifically, in S3, it includes:

[0080] Counting the generation frequency of feedback data, and defining the generation frequency threshold that meets a preset threshold as the first update constraint condition;

[0081] The actual push effect index of recruitment tweets on each platform is calculated based on the recruitment conversion rate and the match between applicants and positions. The threshold of the actual push effect index that meets the preset threshold is defined as the second update constraint condition;

[0082] The first update constraint and the second update constraint are defined as an update policy.

[0083] Based on the above, this embodiment uses statistical feedback data generation frequency and calculation of the actual push effect index of recruitment tweets to achieve scientific and reasonable evaluation and screening of update platforms. By determining the update strategy based on the generation frequency threshold and the actual push effect index threshold, it is possible to accurately determine which platforms need to update recruitment tweets. A high generation frequency indicates that the platform has active feedback and is worthy of attention; a good push effect index means that the platform has good recruitment results and should be optimized. By updating tweets on the platforms selected in this way, resources can be concentrated on optimizing recruitment tweets on key platforms, improving the utilization efficiency of recruitment resources, and ensuring that recruitment tweets maintain good dissemination effects and recruitment efficiency on key platforms.

[0084] Specifically, S4 includes:

[0085] Parse the updated content and, based on the generation strategy, adjust the word count, layout, and frequency of the updated content in the initial recruitment tweet. Define the adjusted initial recruitment tweet as the first recruitment tweet.

[0086] The platform that meets the update strategy is selected, and the initial recruitment tweet of the platform is updated to the first recruitment tweet.

[0087] Based on the above, this embodiment optimizes and precisely delivers recruitment tweets by parsing updated content and adjusting the word count, layout, and frequency of initial recruitment tweets based on a generation strategy. Adjusting tweets according to the generation strategy can make them more consistent with the characteristics of each platform and the needs of applicants. Reasonable word count adjustments ensure that information is concise and clear, suitable for reading habits on different platforms; optimizing the layout can enhance the visual effect of tweets and increase their appeal; adjusting the frequency of appearance can push tweets to the target audience at the right time. Screening platforms that meet the update strategy and updating tweets can ensure that recruitment tweets are presented in the best condition on key platforms, increasing the exposure and appeal of recruitment information, and improving recruitment results.

[0088] Specifically, S4 also includes:

[0089] Obtain the recruitment tweet charging categories and preset recruitment budgets for platforms that meet the update strategy; the recruitment tweet charging categories include at least word count charging rules, page layout charging rules, and push frequency charging rules;

[0090] Based on a comparison between the initial recruitment tweet and the first recruitment tweet, combined with the recruitment tweet charging rules and the preset recruitment budget, the recruitment cost of updating to the first recruitment tweet on the platform is calculated. When the recruitment cost does not meet the recruitment budget, a new recruitment tweet is generated to obtain a third recruitment tweet and updated. When the third recruitment tweet is generated, a restrictive adjustment is made to the generated recruitment tweet based on the recruitment tweet charging category, and the restrictive adjustment includes at least an adjustment to the word count, layout, and push frequency of the recruitment tweet.

[0091] As a preferred implementation of this embodiment, linkage adjustment is used when generating the third recruitment tweet, and the linkage adjustment is:

[0092] Obtain a preset recruitment budget B, a word count price a, a page layout price b, and a push frequency price c. The first recruitment tweet has a word count of W2, a page layout of P2, and a push frequency of F2. Obtain adjustment weights ω1, ω2, and ω3, which correspond to the importance of adjusting word count, page layout, and push frequency, respectively, based on common sense known to those skilled in the art. In this embodiment, ω1 + ω2 + ω3 = 1.

[0093] Calculate the comprehensive adjustment factor k:

[0094]

[0095] Adjusted word count for the third recruitment tweet Layout Push frequency in, Indicates rounding.

[0096] It should be noted that if k is a positive number, it means that corresponding resources need to be added on the existing basis (for example, increasing the number of words can improve the richness of the content, etc., but in actual calculations, adjustments will be made by rounding down due to charging rules); if k is a negative number, it means that resources need to be reduced to meet the budget.

[0097] In a simple example, a company publishes a recruitment tweet on a recruitment platform, and the preset recruitment budget B = 5000 (unit: yuan). The charging rules of the platform are: word count charging unit price a = 2 (unit: yuan / word), page charge unit price b = 500 (unit: yuan / unit page), push frequency charge unit price c = 300 (unit: yuan / time). The number of words in the first recruitment tweet is W2 = 1500, the page occupied is P2 = 3, and the push frequency is F2 = 5. In order to balance various factors, the adjustment weights ω1 = 0.4 (focusing on word count adjustment), ω2 = 0.3 (moderate page adjustment weight), and ω3 = 0.3 (the push frequency adjustment weight is the same as the page). First, the cost of the current first recruitment tweet is calculated to be 6000 yuan. Since 6000>5000, adjustment is required. Then calculate the comprehensive adjustment factor k≈-3.8. Then calculate the various parameters of the adjusted third recruitment tweet: word count Layout Push frequency The adjusted recruitment tweet cost is 4,396 yuan, which meets the preset recruitment budget.

[0098] Based on the above, this embodiment effectively controls recruitment costs by obtaining the platform's fee categories and pre-set recruitment budget, and then calculating recruitment costs based on a comparison of the initial and first recruitment tweets. When costs exceed the budget, a third recruitment tweet is regenerated and adjusted based on the fee categories, such as word count, layout, and push frequency, to optimize recruitment tweets while staying within budget. This prevents companies from incurring excessive costs by blindly posting recruitment tweets, ensures recruitment activities are conducted within an economically reasonable range, and improves the efficiency of a company's recruitment resources. This allows companies to control costs while still attracting suitable talent through optimized recruitment tweets, maximizing recruitment effectiveness.

[0099] The second aspect of this embodiment discloses Figure 2 A system for generating recruitment tweets for multiple platforms is shown, which is applicable to the above-mentioned method for generating recruitment tweets for multiple platforms. The system includes a generation strategy module, an update content module, an update strategy module, and a recruitment tweet generation and update module; wherein the generation strategy module, the update content module, and the update strategy module are all in communication with the recruitment tweet generation and update module;

[0100] The generation strategy module is configured to collect feedback data from various platforms regarding the initial recruitment tweet and derive a generation strategy based on the feedback data. The feedback data is used to characterize applicants' responses to the content in the recruitment tweet and their new requirements, and the generation strategy is used to generate the recruitment tweet.

[0101] The update content module is configured to: obtain updated data of the recruiting company and obtain updated content based on the updated data; wherein the updated data is used to represent changes in the recruitment company's employment needs, and the updated content is used to generate recruitment tweets;

[0102] The update strategy module is configured to evaluate feedback data and the corresponding generation strategy to obtain an update strategy, where the evaluation is based on the frequency of feedback data generation and the quality of the generation strategy; wherein the update strategy is used to select platforms that update recruitment tweets;

[0103] The recruitment tweet generation and update module is configured to: based on the generation strategy and update content, generate recruitment tweets for the initial recruitment tweets of the platform that meet the update strategy to obtain the first recruitment tweet and update it.

[0104] It should be noted that the recruitment tweet generation system for multiple platforms of this embodiment corresponds to the aforementioned recruitment tweet generation method for multiple platforms. Therefore, the contents not specifically described in the recruitment tweet generation system for multiple platforms of this embodiment may include but are not limited to functional definitions, working principles, and technical effects, etc., which can all be referred to in the aforementioned recruitment tweet generation method for multiple platforms, and this text will not elaborate on them here.

[0105] A third aspect of this embodiment discloses a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned method for generating recruitment tweets for multiple platforms is implemented.

[0106] Similarly, it should be noted that the storage medium of this embodiment corresponds to the aforementioned method for generating recruitment tweets for multiple platforms. Therefore, the content not specifically described in the storage medium of this embodiment may be, but is not limited to, functional definitions, working principles, and technical effects, etc., which can all be referred to in the aforementioned method for generating recruitment tweets for multiple platforms, and this text will not elaborate on them here.

[0107] In summary, the recruitment tweet generation method, system and storage medium for multiple platforms of this embodiment utilize the feedback data of each platform on the initial recruitment tweets and the updated data of the recruiting companies to achieve comprehensive and dynamic grasp of recruitment-related information, determine the generation strategy based on the analysis of the feedback data, and optimize the tweet content according to the responses and needs of the applicants to make it more attractive and targeted; generate updated content based on the updated data of the changes in the company's employment needs to ensure that the recruitment tweets reflect the latest needs of the company in a timely manner; evaluate the feedback data and generation strategy to obtain the update strategy, accurately screen out the platforms that need to update the tweets, and avoid waste of resources; finally, update the tweets based on the generation strategy and updated content, realize dynamic optimization of recruitment tweets on multiple platforms, improve recruitment efficiency and effectiveness, help companies more efficiently attract talents that meet their needs, improve recruitment quality, reduce recruitment costs, and enhance the competitiveness of companies in the talent market.

[0108] In the embodiments provided herein, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiments can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication media include any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0109] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for generating recruitment tweets for multiple platforms, characterized in that: The method comprises the following steps: S1: Collect feedback data from various platforms regarding the initial recruitment tweet, and derive a generation strategy based on the feedback data; wherein the feedback data is used to characterize applicants' responses to the content of the recruitment tweet and their new requirements, and the generation strategy is used to generate the recruitment tweet; S2: Obtaining updated data of the recruiting company and obtaining updated content based on the updated data; wherein the updated data is used to represent changes in the recruiting company's employment needs, and the updated content is used to generate recruitment tweets; S3: Evaluate the feedback data and the corresponding generation strategy to obtain an update strategy, where the evaluation is based on the generation frequency of the feedback data and the quality of the generation strategy; wherein the update strategy is used to screen platforms for updating recruitment tweets; S4: Based on the generation strategy and the update content, generate a first recruitment tweet for the initial recruitment tweet of the platform that meets the update strategy and update the first recruitment tweet.

2. The method for generating recruitment tweets for multiple platforms according to claim 1, characterized in that: In S1, we collected feedback data from various platforms on the initial recruitment tweets, including: At least collecting the number of clicks, reading time, number of shares, and comment content of the applicants on the initial recruitment tweet on various platforms as the first response feedback to the initial recruitment tweet; Based on a preset online consultation feedback portal, collecting information that the applicants wish to know but is not covered in the initial recruitment tweet as a second response feedback to the initial recruitment tweet; Using the platform's own data analysis tools, we collected detailed information on applicant responses and new demand feedback from different time periods, regions, and user groups on the platform as the third response feedback; The first response feedback, the second response feedback, and the third response feedback are packaged and defined as the feedback data.

3. The method for generating recruitment tweets for multiple platforms according to claim 2, characterized in that: The generation strategy based on the feedback data in S1 includes: Analyzing the applicant's attention to different recruitment contents in the first response feedback, and determining the content to be highlighted based on the attention; Analyze the new demand feedback from the applicant in the second response feedback and the third response feedback, and determine the content that needs to be supplemented and improved in the recruitment tweet based on the new feedback demand; The generation strategy is defined as packaging the determined content that needs to be highlighted and the determined content that needs to be supplemented in the recruitment tweets.

4. The method for generating recruitment tweets for multiple platforms according to claim 1, characterized in that: Get updated data of recruiting companies in S2, including: Obtaining job data related to positions in the human resources of recruiting companies; Obtain data on changes in the recruitment company's employment needs during its own development; The position data and the change data are packaged and defined as the update data.

5. The method for generating recruitment tweets for multiple platforms according to claim 4, characterized in that: The updated content obtained in S2 based on the updated data includes: Determining detailed information of a new position based on the position data; Determining talent requirement information and corresponding key information for attracting talent based on the change data; The updated content is defined as the detailed information of the newly added positions, the determined talent requirement information, and the corresponding key information for attracting talents are packaged.

6. The method for generating recruitment tweets for multiple platforms according to claim 1, characterized in that: In S3, this includes: Counting the generation frequency of the feedback data, and defining a generation frequency threshold that satisfies a preset value as a first update constraint condition; The actual push effect index of recruitment tweets on each platform is calculated based on the recruitment conversion rate and the match between applicants and positions. The threshold of the actual push effect index that meets the preset threshold is defined as the second update constraint condition; The first update constraint condition and the second update constraint condition are defined as the update policy.

7. The method for generating recruitment tweets for multiple platforms according to claim 1, characterized in that: In S4, it includes: parsing the updated content, and adjusting the word count, layout, and frequency of occurrence of the updated content in the initial recruitment tweet based on the generation strategy, and defining the adjusted initial recruitment tweet as the first recruitment tweet; A platform that meets the update strategy is screened, and the initial recruitment tweet of the platform is updated to the first recruitment tweet.

8. The method for generating recruitment tweets for multiple platforms according to claim 1, characterized in that: In S4, it also includes: Obtaining the recruitment tweet charging categories and preset recruitment budget of the platform that meets the update strategy; wherein the recruitment tweet charging categories include at least word count charging rules, page layout charging rules, and push frequency charging rules; Based on a comparison between the initial recruitment tweet and the first recruitment tweet, combined with the recruitment tweet charging rules and the preset recruitment budget, the recruitment cost of updating to the first recruitment tweet on the platform is calculated. When the recruitment cost does not meet the recruitment budget, a new recruitment tweet is generated to obtain a third recruitment tweet and updated. When generating the third recruitment tweet, a restrictive adjustment is made to the generated recruitment tweet based on the recruitment tweet charging category, and the restrictive adjustment includes at least an adjustment to the word count, layout, and push frequency of the recruitment tweet.

9. A recruitment tweet generation system for multiple platforms, the system being applicable to the recruitment tweet generation method for multiple platforms as described in any one of claims 1 to 8, characterized in that: The system includes a generation strategy module, an update content module, an update strategy module, and a recruitment tweet generation and update module; wherein the generation strategy module, the update content module, and the update strategy module are all in communication with the recruitment tweet generation and update module; The generation strategy module is configured to collect feedback data from various platforms regarding the initial recruitment tweet and derive a generation strategy based on the feedback data; wherein the feedback data is used to characterize applicants' responses to the content of the recruitment tweet and their new requirements, and the generation strategy is used to generate the recruitment tweet; The update content module is configured to: obtain updated data of the recruiting company and obtain updated content based on the updated data; wherein the updated data is used to represent changes in the recruitment company's employment needs, and the updated content is used to generate recruitment tweets; The update strategy module is configured to: evaluate the feedback data and the corresponding generation strategy to obtain an update strategy, wherein the evaluation is performed based on the generation frequency of the feedback data and the quality of the generation strategy; wherein the update strategy is used to screen platforms for updating recruitment tweets; The recruitment tweet generation and update module is configured to: based on the generation strategy and the update content, generate a first recruitment tweet for the initial recruitment tweet of the platform that meets the update strategy and obtain the first recruitment tweet and update it.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating recruitment tweets for multiple platforms according to any one of claims 1 to 8 is implemented.

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