Internet-based human resource information sharing system and method

Abstract

The invention provides a human resource information sharing system and method based on the Internet, and the method comprises the steps: dividing all human resource information into a plurality of human resource information classification clusters, and determining a credible recommendation index of each piece of human resource information based on a behavior aggregation feature corresponding to each human resource information classification cluster; screening out a plurality of pieces of transferable human resource information based on each credible recommendation index; determining attenuation fluctuation of each piece of transferable human resource information in a sharing process through the feedback frequency of each piece of transferable human resource information in a specified time; and determining a sharing confidence coefficient when sharing each piece of transferable human resource information through all attenuation fluctuations, screening out all transferable human resource information based on the sharing confidence coefficient, and uploading the transferable human resource information to a human resource information sharing platform. By adopting the scheme of the invention, the credible sharing of the human resource information can be realized in the Internet, so that the credible range of the human resource sharing information is expanded.

Description

Technical Field

[0001] The present application relates to the technical field of information sharing, and more specifically, to a human resources information sharing system and method based on the Internet. Background Art

[0002] Information sharing refers to the process of transferring, exchanging and using data or information between individuals, organizations or systems. Information sharing can be applied in different fields, such as medicine, finance, education and government. The application of information sharing not only improves the efficiency of information circulation, but also promotes collaboration and innovation in all walks of life.

[0003] Internet-based human resource information sharing is a way of using Internet technology to transmit, communicate and manage human resource information. This sharing method can improve the efficiency and transparency of organizations and individuals in human resource management. Internet-based human resource information sharing has not only changed the traditional human resource management method, but also promoted the optimal allocation of human resources across organizations and industries. However, in the existing technology, human resource sharing information is easy to be forged or exaggerated. The existing human resource information sharing platform on the Internet often directly shares human resource sharing information, and the human resource information sharing platform mixes a large amount of credible and uncredible human resource information. The uncredible human resource information occupies the storage and processing resources of the human resource information sharing platform, and also wastes the user's attention and time, making it increasingly difficult for users to screen useful human resource information. At the same time, it also reduces the user's trust in the human resource information sharing platform. Therefore, how to achieve credible sharing of human resource information on the Internet, thereby increasing the credible scope of human resource sharing information, has become a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an Internet-based human resources information sharing system and method, which can realize the trusted sharing of human resources information on the Internet, thereby increasing the trusted scope of human resources shared information.

[0005] In a first aspect, the present application provides a method for sharing human resources information based on the Internet, comprising the following steps:

[0006] Receive all human resources information to be shared on the Internet;

[0007] Divide all human resource information into multiple human resource information classification clusters, extract behavioral aggregation features of each human resource information classification cluster, and determine the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavioral aggregation features corresponding to each human resource information classification cluster;

[0008] Filter out multiple transferable human resource information from all human resource information based on various credible recommendation indexes;

[0009] Acquire the feedback frequency of each transferable human resources information within a specified time, and then determine the attenuation fluctuation of each transferable human resources information in the sharing process according to the feedback frequency of each transferable human resources information within the specified time;

[0010] Determine the sharing confidence of each transferable human resource information when sharing it through all the attenuation fluctuations;

[0011] Based on the sharing confidence level, all transferable human resource information is screened out and uploaded to the human resource information sharing platform, thereby completing the trusted sharing of human resource information on the Internet.

[0012] In some embodiments, dividing all human resources information into a plurality of human resources information classification clusters specifically includes:

[0013] Determine the weight of each piece of human resources information;

[0014] Preset weight level;

[0015] All human resource information is divided into different human resource information classification clusters according to each emphasis value and the emphasis level, thereby obtaining a plurality of human resource information classification clusters.

[0016] In some embodiments, selecting multiple transferable human resources information from all human resources information based on various trustworthy recommendation indexes specifically includes:

[0017] Determine the delivery factor for each piece of HR information;

[0018] Determine the information transferability of each piece of human resource information through various transfer factors and various credible recommendation indexes;

[0019] All human resource information whose information transferability exceeds a preset transfer threshold is regarded as transferable human resource information, thereby obtaining a plurality of transferable human resource information.

[0020] In some embodiments, determining the attenuation fluctuation of each transferable human resources information in the sharing process through the feedback frequency of each transferable human resources information within a specified time specifically includes:

[0021] Get the recommendation factor for each deliverable human resource information;

[0022] Determine the amount of shared impact for each deliverable piece of HR information;

[0023] Determining the active state of each transferable human resources information when being shared according to the feedback frequency and the corresponding sharing influence of each transferable human resources information within a specified time;

[0024] The attenuation fluctuation of each piece of transferable human resources information during the sharing process is determined according to the active state and the corresponding trustworthy recommendation index corresponding to each piece of transferable human resources information.

[0025] In some embodiments, determining the attenuation fluctuation of each transferable human resources information during the sharing process according to the activity status and the corresponding trustworthy recommendation index corresponding to each transferable human resources information specifically includes:

[0026] Selecting a piece of deliverable human resources information as selected deliverable human resources information;

[0027] Determine the trust efficacy value of the selected transferable human resource information by the active state of the selected transferable human resource information when it is shared;

[0028] Determining the attenuation fluctuation of the selected transferable human resource information during the sharing process according to the trust recommendation index corresponding to the selected transferable human resource information and the trust effectiveness value;

[0029] Continue to determine the decay fluctuations of the remaining transferable human resource information during the sharing process.

[0030] In some embodiments, determining the trustworthy recommendation index of each piece of human resources information in each human resources information classification cluster based on the behavior aggregation feature corresponding to each human resources information classification cluster specifically includes:

[0031] Selecting a human resource information classification cluster as the selected human resource information classification cluster, and then obtaining the behavior aggregation features corresponding to the selected human resource information classification cluster;

[0032] Determining the importance coefficient of each piece of human resource information in the selected human resource information classification cluster based on the behavior aggregation feature;

[0033] Determine the trustworthy recommendation index of each piece of human resource information in the selected human resource information classification cluster through various important coefficients and preset adjustment parameters;

[0034] Continue to determine the trustworthy recommendation index of each piece of human resource information in the remaining human resource information classification clusters.

[0035] In some embodiments, the human resources information sharing platform may be a recruitment website or an enterprise human resources service center.

[0036] In a second aspect, the present application provides an Internet-based human resources information sharing system, comprising:

[0037] A receiving module, used for receiving all human resources information to be shared on the Internet;

[0038] A processing module, used to divide all human resource information into multiple human resource information classification clusters, and then extract the behavior aggregation features of each human resource information classification cluster, and determine the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavior aggregation features corresponding to each human resource information classification cluster;

[0039] The processing module is further used to filter out multiple transferable human resource information from all human resource information based on each credible recommendation index;

[0040] The processing module is further used to obtain the feedback frequency of each transferable human resources information within a specified time, and then determine the attenuation fluctuation of each transferable human resources information in the sharing process through the feedback frequency of each transferable human resources information within the specified time;

[0041] The processing module is further used to determine the sharing confidence when sharing each transferable human resource information through all attenuation fluctuations;

[0042] The execution module is used to screen out all transferable human resource information based on the sharing confidence and upload them to the human resource information sharing platform to complete the trusted sharing of human resource information on the Internet.

[0043] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned Internet-based human resources information sharing method.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the above-mentioned Internet-based human resources information sharing method when executed.

[0045] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0046] In the present application, all human resource information to be shared on the Internet is received; all human resource information is divided into multiple human resource information classification clusters, and then the behavioral aggregation characteristics of each human resource information classification cluster are extracted, and the trustworthy recommendation index of each human resource information in each human resource information classification cluster is determined based on the behavioral aggregation characteristics corresponding to each human resource information classification cluster; multiple transferable human resource information are screened out from all human resource information based on each trustworthy recommendation index; the feedback frequency of each transferable human resource information within a specified time is obtained, and then the attenuation fluctuation of each transferable human resource information in the sharing process is determined through the feedback frequency of each transferable human resource information within the specified time; the sharing confidence of each transferable human resource information when sharing is determined through all the attenuation fluctuations; all transferable human resource information is screened out based on the sharing confidence and uploaded to the human resource information sharing platform, so as to complete the trusted sharing of human resource information on the Internet.

[0047] It can be seen that in this application, firstly, all human resource information is divided into a plurality of human resource information classification clusters, so that different categories of human resource sharing information can be processed more targetedly to avoid information overload and resource waste; secondly, the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster is determined based on the behavioral aggregation characteristics corresponding to each human resource information classification cluster, and the most trustworthy and valuable transferable human resource information to be shared is screened out through the trustworthy recommendation index to ensure that the human resource information can be effectively transferred on the Internet; then, the transferable human resource information is determined through the activity level of the transferable human resource information in the sharing network (i.e., the active state when sharing). The decay trend of the activity of human resource information during the sharing process over time (i.e., decay fluctuation) can evaluate whether the shared human resource information is still reliable after a long time or multiple sharing; finally, the sharing confidence of each transferable human resource information when sharing is determined through all decay fluctuations, and then all transferable human resource information with a sharing confidence greater than or equal to the preset sharing judgment threshold is uploaded to the human resource information sharing platform, thereby realizing the trusted sharing of human resource information on the Internet, and effectively expanding the sharing scope of credible human resource information; in summary, the scheme can realize the trusted sharing of human resource information on the Internet, thereby increasing the trusted scope of shared human resource information. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 is an exemplary flow chart of an Internet-based human resources information sharing method according to some embodiments of the present application;

[0050] Figure 2 is an exemplary flow chart of determining a credible recommendation index according to some embodiments of the present application;

[0051] Figure 3 is an exemplary flow chart of determining transferable human resources information according to some embodiments of the present application;

[0052] Figure 4 is a schematic diagram of the structure of an Internet-based human resources information sharing system according to some embodiments of the present application;

[0053] Figure 5 It is a structural diagram of a computer device for implementing an Internet-based human resources information sharing method according to some embodiments of the present application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] refer to Figure 1 , which is an exemplary flow chart of a human resources information sharing method based on the Internet according to some embodiments of the present application. The human resources information sharing method 100 based on the Internet mainly includes the following steps:

[0056] In step 101, all human resource information to be shared on the Internet is received.

[0057] In specific implementation, the human resource information that needs to be shared can be collected from the Internet through web crawlers, API interfaces, social media mining, etc., so as to obtain all the human resource information on the Internet. Other methods can also be used in other embodiments, which are not specifically limited here.

[0058] It should be noted that the human resources information in this application includes various information data related to employees, positions, skills and related processes in the organization, such as basic employee information, position information, and skills and ability information, which will not be repeated here.

[0059] In step 102, all human resource information is divided into multiple human resource information classification clusters, and then the behavior aggregation features of each human resource information classification cluster are extracted, and the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster is determined based on the behavior aggregation features corresponding to each human resource information classification cluster.

[0060] In some embodiments, dividing all human resources information into multiple human resources information classification clusters can be achieved by using the following steps:

[0061] Determine the weight of each piece of human resources information;

[0062] Preset weight level;

[0063] All human resource information is divided into different human resource information classification clusters according to each emphasis value and the emphasis level, thereby obtaining a plurality of human resource information classification clusters.

[0064] It should be noted that the bias value reflects the degree of bias of human resources information in a specific dimension. The larger the bias value, the greater the degree of bias of human resources information in the specific dimension. Conversely, the smaller the bias value, the smaller the degree of bias of human resources information in the specific dimension. In specific implementation, the bias value of each piece of human resources information can be determined in the following manner, namely: the importance of each piece of human resources information can be scored based on the information category, importance, historical usage frequency, user feedback and other characteristics of each piece of human resources information through an existing evaluation algorithm, and then the corresponding scoring values ​​are used as the bias values ​​of each piece of human resources information. Among them, the evaluation algorithm, for example, cross-validation, genetic algorithm and ensemble learning, etc., can also be determined by other methods in other embodiments, which are not limited here.

[0065] In addition, it should be noted that the bias value can help the system distinguish which information is more important or has a higher priority among a large amount of human resource information, so as to give priority to processing or sharing such human resource information when resources are limited.

[0066] It should be noted that the emphasis level in the present application represents a predetermined emphasis interval range, and the emphasis level is used to divide each piece of human resources information into different categories of human resources information classification clusters according to its emphasis value; as a preferred embodiment, the emphasis level can be preset through an empirical classification method based on the experience of experts in related fields and historical experimental data, and other methods can also be used for determination in other embodiments, which is not limited here.

[0067] In specific implementation, the following method can be used to divide all human resource information into different human resource information classification clusters according to each emphasis value and the emphasis level, namely: all human resource information can be divided based on the emphasis value of each human resource information by using an existing clustering algorithm (such as K-means clustering or hierarchical clustering). For example, human resource information with the same emphasis level as the emphasis value is divided into the same human resource information classification cluster, thereby obtaining multiple human resource information classification clusters. In other embodiments, other methods can also be used for division, which are not limited here.

[0068] It should be noted that the human resource information classification cluster in the present application represents an information set consisting of human resource information in different importance intervals. In addition, the determination of the human resource information classification cluster can effectively allocate and utilize resources. For example, human resource information in a human resource information classification cluster with a higher bias value will be given priority for sharing, while human resource information in a human resource information classification cluster with a low bias value can be postponed or selectively shared, thereby improving the calculation accuracy of the subsequent determination of the trusted recommendation index, so as to improve the decision-making ability for sharing human resource information. I will not go into details here.

[0069] In some embodiments, the extraction of behavioral aggregation features of each human resources information classification cluster can be achieved in the following manner, namely: a human resources information classification cluster is selected as the selected human resources information classification cluster, and the behavioral features of each piece of human resources information in the selected human resources information classification cluster can be extracted through a machine learning algorithm. The behavioral features are, for example, the amount of user feedback, usage frequency, number of shares, and user satisfaction for each piece of human resources information, and then all the behavioral features extracted from the selected human resources information classification cluster are used to form the behavioral aggregation features of the selected human resources information classification cluster, and the behavioral aggregation features of the remaining human resources information classification clusters are continuously extracted. In other embodiments, other methods can also be used to implement this, which is not limited here. It should be noted that the behavioral aggregation features in the present application represent a set of multiple behavioral features corresponding to the human resources information classification cluster, and the behavioral aggregation features are used to evaluate the performance of the human resources information in the human resources information classification cluster, which will not be repeated here.

[0070] In some embodiments, reference Figure 2As shown, this figure is an exemplary flow chart of determining a credible recommendation index in some embodiments of the present application. In this embodiment, determining the credible recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavioral aggregation features corresponding to each human resource information classification cluster can be implemented by the following steps:

[0071] First, in step 1021, a human resource information classification cluster is selected as a selected human resource information classification cluster, and then the behavior aggregation feature corresponding to the selected human resource information classification cluster is obtained;

[0072] Next, in step 1022, the importance coefficient of each piece of human resource information in the selected human resource information classification cluster is determined based on the behavior aggregation feature;

[0073] Then, in step 1023, the trustworthy recommendation index of each piece of human resource information in the selected human resource information classification cluster is determined by using each important coefficient and the preset adjustment parameter;

[0074] Finally, in step 1024, the trustworthy recommendation index of each piece of human resource information in the remaining human resource information classification clusters is further determined.

[0075] It should be noted that the importance coefficient in the present application reflects the importance of human resources information in the human resources information classification cluster to which it belongs. The larger the importance coefficient is, the greater the importance of the human resources information in the human resources information classification cluster to which it belongs. Conversely, the larger the importance coefficient is, the smaller the importance of the human resources information in the human resources information classification cluster to which it belongs. In specific implementation, the importance coefficient of each piece of human resources information in the selected human resources information classification cluster determined by the behavior aggregation feature can be implemented in the following manner, namely: first, a piece of human resources information in the selected human resources information classification cluster is selected as the selected human resources information, and then, the score of the selected human resources information on each behavior feature corresponding to the behavior aggregation feature can be evaluated by an evaluation algorithm. Secondly, all scores obtained by evaluating the selected human resources information are averaged and weighted summed, and then, the obtained results are normalized. Finally, the normalized result is used as the importance coefficient of the selected human resources information, and the importance coefficients of the remaining human resources information in the selected human resources information classification cluster are further determined. In other embodiments, other methods can also be used for determination, which is not limited here.

[0076] In specific implementation, determining the credible recommendation index of each piece of human resource information in the selected human resource information classification cluster through various important coefficients and preset adjustment parameters can be achieved in the following manner, namely: first, preset adjustment parameters based on historical experimental data and actual application needs through regression analysis or machine learning model, and the adjustment parameters are used to determine the influence weight of the adjustment important coefficient on the credible recommendation index, and then adjust the adjustment parameters to the optimal value through repeated testing and optimization, so that the subsequent determination of the credible recommendation index is more in line with the actual application needs, thereby, the product value of the important coefficient of each piece of human resource information in the selected human resource information classification cluster and the adjustment parameter is used as the credible recommendation index of each piece of human resource information in the selected human resource information classification cluster. In other embodiments, other methods can also be used for determination, which is not limited here.

[0077] It should be noted that the trusted recommendation index in this application reflects the degree to which human resources information can be recommended. The larger the trusted recommendation index, the more recommended the human resources information is. Conversely, the smaller the trusted recommendation index, the less recommended the human resources information is. Therefore, by determining the trusted recommendation index of a piece of human resources information, it can help to more efficiently manage and distribute human resources shared information and ensure that users obtain high-quality and reliable human resources shared information. It will not be elaborated here.

[0078] In step 103, a plurality of transferable human resource information is screened out from all human resource information based on various trustworthy recommendation indexes.

[0079] In some embodiments, reference Figure 3 As shown in FIG. 1 , this figure is an exemplary flow chart of determining transferable human resource information in some embodiments of the present application. In this embodiment, filtering out multiple transferable human resource information from all human resource information based on various credible recommendation indexes can be implemented by the following steps:

[0080] First, in step 1031, the transfer factor of each piece of human resource information is determined;

[0081] Secondly, in step 1032, the information transferability of each piece of human resource information is determined through each transfer factor and each trustworthy recommendation index;

[0082] Finally, in step 1033, all human resource information whose information transferability exceeds a preset transfer threshold is regarded as transferable human resource information, thereby obtaining a plurality of transferable human resource information.

[0083] It should be noted that the transmission factor in the present application reflects the transmission capability of human resources information. Therefore, the transmission priority of human resources information can be understood through the transmission factor to improve the accuracy and efficiency of human resources information transmission and reduce unnecessary information transmission burden. In specific implementation, the transmission factor of each piece of human resources information can be determined in the following manner, namely: a multivariable function can be used to evaluate the transmission capability of each piece of human resources information according to the characteristics, sharing environment, and user demand factors of each piece of human resources information, and the obtained result is used as the transmission factor of each piece of human resources information. For example, the transmission factor of important and urgent human resources information should be larger, while the transmission factor of non-critical or sensitive information should be smaller. Other methods can also be used for determination in other embodiments, which are not limited here.

[0084] It should be noted that the information transferability reflects the comprehensive transferability of human resources information in the shared Internet. The larger the information transferability is, the stronger the comprehensive transferability of human resources information in the shared Internet is. On the contrary, the smaller the information transferability is, the weaker the comprehensive transferability of human resources information in the shared Internet is. As a preferred embodiment, the information transferability of each piece of human resources information can be determined by each transfer factor and each trusted recommendation index in the following manner, namely, the trusted recommendation index of each piece of human resources information can be multiplied by its corresponding transfer factor, and the product result can be used as the information transferability of each piece of human resources information. In other embodiments, other methods can also be used for determination, which is not limited here.

[0085] It should be noted that the transmission threshold in the present application is a judgment threshold for screening transmittable human resource information among all human resource information. As a preferred embodiment, the transmission threshold can be set through regression analysis or machine learning model based on historical experimental data and actual application needs, so that the transmission threshold can effectively filter out human resource information that does not meet the conditions. In other embodiments, other methods can also be used for presetting, which is not limited here.

[0086] It should be noted that the transmittable human resources information in this application refers to the human resources information that can be transmitted on the Internet. In addition, by screening out the transmittable human resources information from all human resources information, the human resources information that does not meet the conditions on the Internet can be effectively filtered out to ensure that the human resources information can be effectively transmitted on the Internet. It will not be repeated here.

[0087] In step 104, the feedback frequency of each transferable human resources information within a specified time is obtained, and then the attenuation fluctuation of each transferable human resources information in the sharing process is determined according to the feedback frequency of each transferable human resources information within the specified time.

[0088] In specific implementation, obtaining the feedback frequency of each transferable human resources information within a specified time can be achieved in the following manner, namely: the number of times the user evaluates each transferable human resources information within a specified time can be obtained from the log records of the online platform, and the obtained number is used as the feedback frequency of each transferable human resources information within a specified time, wherein the specified time may be within the past 24 hours, and in other embodiments may also be other times, which are not specifically limited here; in addition, it should be noted that the feedback frequency in this application represents the number of times the user evaluates the transferable human resources information, which will not be repeated here.

[0089] In some embodiments, determining the attenuation fluctuation of each transferable human resources information during the sharing process by the feedback frequency of each transferable human resources information within a specified time can be achieved by using the following steps:

[0090] Get the recommendation factor for each deliverable human resource information;

[0091] Determine the amount of shared impact for each deliverable piece of HR information;

[0092] Determining the active state of each transferable human resources information when being shared according to the feedback frequency and the corresponding sharing influence of each transferable human resources information within a specified time;

[0093] The attenuation fluctuation of each piece of transferable human resources information during the sharing process is determined according to the active state and the corresponding trustworthy recommendation index corresponding to each piece of transferable human resources information.

[0094] It should be noted that the shared influence reflects the shared value of the transferable human resources information. The larger the shared influence, the greater the shared value of the transferable human resources information, and the smaller the shared influence, the smaller the shared value of the transferable human resources information. In specific implementation, the shared influence of each transferable human resources information can be determined in the following manner, namely: selecting a piece of transferable human resources information as the selected transferable human resources information, and using the comprehensive scoring method in the prior art to conduct a comprehensive evaluation of the credibility, importance, source and other influencing factors of each evaluation content of the selected transferable human resources information based on the historical evaluation records, evaluation sources, evaluation types, and background of the evaluator of the selected transferable human resources information, and then using the standard deviation of all the comprehensive evaluation results obtained as the shared influence of the selected transferable human resources information, and continuing to determine the shared influence of the remaining transferable human resources information. In other embodiments, other methods can also be used for determination, which is not limited here.

[0095] In specific implementation, determining the active state of each piece of transferable human resources information when shared through the feedback frequency of each piece of transferable human resources information within a specified time and the corresponding sharing influence can be implemented in the following manner, namely: selecting one piece of transferable human resources information as the selected transferable human resources information, obtaining the logarithm of the sum of the feedback frequency of the selected transferable human resources information within a specified time and the value 1, and finally taking the product value of the logarithm obtained by multiplying the corresponding sharing influence as the active state of the selected transferable human resources information when shared, and continuing to determine the active state of the remaining transferable human resources information when shared, so as to obtain the active state of each piece of transferable human resources information when shared. In other embodiments, other methods can also be used for determination, which are not limited here.

[0096] It should be noted that the active status in the present application reflects the activity level of the transferable human resources information in the shared network. The larger the value corresponding to the active status, the higher the activity level of the transferable human resources information in the shared network. Conversely, the smaller the value corresponding to the active status, the lower the activity level of the transferable human resources information in the shared network. Therefore, the active status can be used to understand the dissemination potential of human resources sharing information and user participation, and then understand the frequency of use and attention level of the transferable human resources information, which will not be elaborated here.

[0097] In some embodiments, determining the attenuation fluctuation of each transferable human resources information during the sharing process according to the activity status and the corresponding trustworthy recommendation index corresponding to each transferable human resources information can be implemented by the following steps:

[0098] Selecting a piece of deliverable human resources information as selected deliverable human resources information;

[0099] Determine the trust efficacy value of the selected transferable human resource information by the active state of the selected transferable human resource information when it is shared;

[0100] Determining the attenuation fluctuation of the selected transferable human resource information during the sharing process according to the trust recommendation index corresponding to the selected transferable human resource information and the trust effectiveness value;

[0101] Continue to determine the decay fluctuations of the remaining transferable human resource information during the sharing process.

[0102] It should be noted that the trust effectiveness value in the present application reflects the effectiveness of the transferable human resources information in the sharing process after comprehensively considering the trust factors; in specific implementation, determining the trust effectiveness value of the selected transferable human resources information by the active state of the selected transferable human resources information when sharing can be implemented in the following manner, namely: the result obtained by multiplying the active state of the selected transferable human resources information when sharing with the trusted recommendation index of the selected transferable human resources information can be used as the trust effectiveness value of the selected transferable human resources information. In other embodiments, other methods can also be used for determination, which is not limited here; determining the attenuation fluctuation of the selected transferable human resources information in the sharing process by the trusted recommendation index corresponding to the selected transferable human resources information and the trust effectiveness value can be implemented in the following manner, namely: the difference between the value 1 and the trusted recommendation index corresponding to the selected transferable human resources information can be compared with the trust effectiveness value, and then the obtained ratio can be used as the attenuation fluctuation of the selected transferable human resources information in the sharing process. In other embodiments, other methods can also be used for determination, which is not limited here.

[0103] It should be noted that the attenuation fluctuation in the present application reflects the attenuation trend of the activity of the transferable human resources information during the sharing process over time. The larger the attenuation fluctuation, the faster the activity of the transferable human resources information during the sharing process decays over time, and the smaller the attenuation fluctuation, the slower the activity of the transferable human resources information during the sharing process decays over time. Therefore, the attenuation fluctuation can help evaluate whether the human resources sharing information is still reliable after a long time or multiple sharing, which will not be repeated here.

[0104] In step 105, the sharing confidence of each transferable human resource information is determined through all the attenuation fluctuations.

[0105] In some embodiments, determining the sharing confidence of each transferable human resources information when sharing it through all attenuation fluctuations can be implemented by the following steps:

[0106] Get a trustworthy recommendation index for each deliverable HR information;

[0107] The sharing confidence level of each transferable human resource information when sharing the transferable human resource information is determined by the trustworthy recommendation index of each transferable human resource information and all decay fluctuations.

[0108] In specific implementation, the sharing confidence of each transferable human resources information when sharing is determined by using the trustworthy recommendation index and all the attenuation fluctuations of each transferable human resources information. This can be achieved in the following manner, namely: selecting one transferable human resources information as the selected transferable human resources information, and then obtaining the attenuation fluctuation and the corresponding trustworthy recommendation index of the selected transferable human resources information, taking the product of the difference between 1 and the attenuation fluctuation and the trustworthy recommendation index as the sharing confidence of the selected transferable human resources information when sharing, and continuing to determine the sharing confidence of the remaining transferable human resources information when sharing. In other embodiments, other methods may also be used for determination, which are not limited here.

[0109] It should be noted that the sharing confidence in the present application reflects the degree of confidence when the transferable human resources information is shared. The larger the sharing confidence, the greater the degree of confidence when the transferable human resources information is shared, and the smaller the sharing confidence, the smaller the degree of confidence when the transferable human resources information is shared.

[0110] In step 106, all transferable human resource information is screened based on the sharing confidence and uploaded to the human resource information sharing platform, thereby completing the trusted sharing of human resource information on the Internet.

[0111] It should be noted that in the present application, screening out all transferable human resources information based on the sharing confidence and uploading them to the human resources information sharing platform means uploading all transferable human resources information with a sharing confidence greater than or equal to a preset sharing judgment threshold to the human resources information sharing platform. The human resources information sharing platform may be a recruitment website or an enterprise human resources service center, which is not specifically limited here.

[0112] In addition, the sharing judgment threshold in the present application is a threshold used to judge whether human resources information can be shared. In specific implementation, the sharing judgment threshold can be preset in the following manner, namely: the sharing result brought about by the historical sharing confidence (i.e., whether the shared human resources information is authentic and reliable) can be statistically analyzed based on the historical human resources information sharing data through an optimization algorithm, thereby setting the sharing judgment threshold of the human resources information according to the results obtained by the statistical analysis. In some preferred embodiments, the average value of all historical sharing confidences obtained by the statistical analysis and the sum of the standard deviations of all historical sharing confidences can be used as the sharing judgment threshold. In other embodiments, other methods can also be used for preset, which are not limited here.

[0113] In specific implementation, in this application, all transferable human resources information with a sharing confidence less than a preset sharing judgment threshold can be marked as an information status to be re-verified and will not be shared and uploaded for the time being. No specific limitation is made here.

[0114] It should be noted that the present application saves all transferable human resource information to be re-verified in a database for future re-evaluation or consideration of whether to share after conditions change. For example, all the above-mentioned transferable human resource information can be submitted to professionals or algorithms for further verification, or wait for more user feedback to improve its credibility. Other processing can also be performed in other embodiments, which are not limited here. In addition, it should be noted that in this way, the reliability of human resource sharing information can be ensured, and unreliable human resource sharing information can be avoided from being mistakenly shared or disseminated, ensuring that the sharing of human resource information is efficient and reliable, which helps to improve the utilization value and credibility of human resource sharing information.

[0115] In addition, in another aspect of the present application, in some embodiments, the present application provides an Internet-based human resources information sharing system, referring to Figure 4 , which is a schematic diagram of the structure of a human resource information sharing system based on the Internet according to some embodiments of the present application. The human resource information sharing system based on the Internet 400 includes: a receiving module 401, a processing module 402 and an execution module 403, which are described as follows:

[0116] Receiving module 401, in this application, receiving module 401 is mainly used to receive all human resources information to be shared on the Internet;

[0117] Processing module 402, in this application, the processing module 402 is mainly used to divide all human resource information into multiple human resource information classification clusters, and then extract the behavior aggregation features of each human resource information classification cluster, and determine the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavior aggregation features corresponding to each human resource information classification cluster;

[0118] The processing module 402 in the present application is also used to filter out multiple transferable human resource information from all human resource information based on various trustworthy recommendation indexes;

[0119] The processing module 402 in the present application is also used to obtain the feedback frequency of each transferable human resources information within a specified time, and then determine the attenuation fluctuation of each transferable human resources information in the sharing process through the feedback frequency of each transferable human resources information within the specified time;

[0120] The processing module 402 in the present application is also used to determine the sharing confidence when sharing each transferable human resource information through all attenuation fluctuations;

[0121] Execution module 403, in the present application, execution module 403 is mainly used to screen out all transferable human resource information based on the sharing confidence and upload them to the human resource information sharing platform, so as to complete the trusted sharing of human resource information on the Internet.

[0122] The above describes in detail the examples of the Internet-based human resources information sharing system and method provided by the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present application.

[0123] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned Internet-based human resources information sharing method.

[0124] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or the module is optional, and the figure is a schematic diagram of the structure of the computer device for implementing the human resources information sharing method based on the Internet of the present application. The human resources information sharing method based on the Internet in the above embodiment can be Figure 5 The computer device 500 shown in the figure is implemented, and the computer device 500 includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device 500 can be a terminal device, a server or a chip.

[0125] The processor 501 may be a general-purpose processor or a special-purpose processor. For example, the processor 501 may be a central processing unit (CPU), which may be used to control the computer device 500, execute software programs, and process data of the software programs. The computer device 500 may also include a communication unit 505 to implement signal input (reception) and output (transmission).

[0126] For example, the computer device 500 may be a chip, the communication unit 505 may be an input and / or output circuit of the chip, or the communication unit 505 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0127] For another example, the computer device 500 may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0128] The computer device 500 may include one or more memories 502, on which a program 504 is stored. The program 504 can be executed by the processor 501 to generate instructions 503, so that the processor 501 performs the method described in the above method embodiment according to the instructions 503. Optionally, data (such as a target audit model) can also be stored in the memory 502. Optionally, the processor 501 can also read the data stored in the memory 502, and the data can be stored at the same storage address as the program 504, or the data can be stored at a different storage address from the program 504.

[0129] The processor 501 and the memory 502 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0130] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 501. The processor 501 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0131] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0132] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are executed on a computer, the computer implements the above-mentioned Internet-based human resources information sharing method.

[0133] In summary, in the Internet-based human resource information sharing system and method disclosed in the embodiments of the present application, all human resource information to be shared in the Internet is received; all human resource information is divided into multiple human resource information classification clusters, and then the behavior aggregation characteristics of each human resource information classification cluster are extracted, and the trustworthy recommendation index of each human resource information in each human resource information classification cluster is determined based on the behavior aggregation characteristics corresponding to each human resource information classification cluster; multiple transferable human resource information is screened out from all human resource information based on each trustworthy recommendation index; the feedback frequency of each transferable human resource information within a specified time is obtained, and then the attenuation fluctuation of each transferable human resource information in the sharing process is determined through the feedback frequency of each transferable human resource information within the specified time; the sharing confidence when each transferable human resource information is shared is determined through all the attenuation fluctuations; all transferable human resource information is screened out based on the sharing confidence and uploaded to the human resource information sharing platform, so as to complete the trusted sharing of human resource information in the Internet; the trusted sharing of human resource information can be realized in the Internet, thereby increasing the trustworthy range of human resource sharing information.

[0134] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0135] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A human resources information sharing method based on the Internet, characterized in that: The steps include: Receive all human resources information to be shared on the Internet; Divide all human resource information into multiple human resource information classification clusters, extract behavioral aggregation features of each human resource information classification cluster, and determine the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavioral aggregation features corresponding to each human resource information classification cluster; Filter out multiple transferable human resource information from all human resource information based on various credible recommendation indexes; Acquire the feedback frequency of each transferable human resources information within a specified time, and then determine the attenuation fluctuation of each transferable human resources information in the sharing process according to the feedback frequency of each transferable human resources information within the specified time; Determine the sharing confidence of each transferable human resource information when sharing it through all the attenuation fluctuations; Based on the sharing confidence level, all transferable human resource information is screened out and uploaded to the human resource information sharing platform, thereby completing the trusted sharing of human resource information on the Internet.

2. The method according to claim 1, characterized in that All human resource information is divided into multiple human resource information classification clusters, including: Determine the weight of each piece of human resources information; Preset weight level; All human resource information is divided into different human resource information classification clusters according to each emphasis value and the emphasis level, thereby obtaining a plurality of human resource information classification clusters.

3. The method according to claim 1, characterized in that Based on various credible recommendation indexes, multiple pieces of human resources information that can be transferred are selected from all human resources information, including: Determine the delivery factor for each piece of HR information; Determine the information transferability of each piece of human resource information through various transfer factors and various credible recommendation indexes; All human resource information whose information transferability exceeds a preset transfer threshold is regarded as transferable human resource information, thereby obtaining a plurality of transferable human resource information.

4. The method according to claim 1, characterized in that Determining the attenuation fluctuation of each transferable human resource information in the sharing process through the feedback frequency of each transferable human resource information within a specified time specifically includes: Get the recommendation factor for each deliverable human resource information; Determine the amount of shared impact for each deliverable piece of HR information; Determining the active state of each transferable human resources information when being shared according to the feedback frequency and the corresponding sharing influence of each transferable human resources information within a specified time; The attenuation fluctuation of each piece of transferable human resources information during the sharing process is determined according to the active state and the corresponding trustworthy recommendation index corresponding to each piece of transferable human resources information.

5. The method according to claim 4, characterized in that Determining the attenuation fluctuation of each transferable human resource information in the sharing process according to the active state and the corresponding trustworthy recommendation index of each transferable human resource information specifically includes: Selecting a piece of deliverable human resources information as selected deliverable human resources information; Determine the trust efficacy value of the selected transferable human resource information by the active state of the selected transferable human resource information when it is shared; Determining the attenuation fluctuation of the selected transferable human resource information during the sharing process according to the trust recommendation index corresponding to the selected transferable human resource information and the trust effectiveness value; Continue to determine the decay fluctuations of the remaining transferable human resource information during the sharing process.

6. The method according to claim 1, characterized in that Determining the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavior aggregation feature corresponding to each human resource information classification cluster specifically includes: Selecting a human resource information classification cluster as the selected human resource information classification cluster, and then obtaining the behavior aggregation features corresponding to the selected human resource information classification cluster; Determining the importance coefficient of each piece of human resource information in the selected human resource information classification cluster based on the behavior aggregation feature; Determine the trustworthy recommendation index of each piece of human resource information in the selected human resource information classification cluster through various important coefficients and preset adjustment parameters; Continue to determine the trustworthy recommendation index of each piece of human resource information in the remaining human resource information classification clusters.

7. The method according to claim 1, characterized in that The human resources information sharing platform is a recruitment website or an enterprise human resources service center.

8. A human resources information sharing system based on the Internet, characterized in that: include: A receiving module, used for receiving all human resources information to be shared on the Internet; A processing module, used to divide all human resource information into multiple human resource information classification clusters, and then extract the behavior aggregation features of each human resource information classification cluster, and determine the trustworthy recommendation index of each piece of human resource information in each human resource information classification cluster based on the behavior aggregation features corresponding to each human resource information classification cluster; The processing module is further used to filter out multiple transferable human resource information from all human resource information based on each credible recommendation index; The processing module is further used to obtain the feedback frequency of each transferable human resources information within a specified time, and then determine the attenuation fluctuation of each transferable human resources information in the sharing process through the feedback frequency of each transferable human resources information within the specified time; The processing module is further used to determine the sharing confidence when sharing each transferable human resource information through all attenuation fluctuations; The execution module is used to screen out all transferable human resource information based on the sharing confidence and upload them to the human resource information sharing platform to complete the trusted sharing of human resource information on the Internet.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the Internet-based human resources information sharing method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the Internet-based human resources information sharing method as described in any one of claims 1 to 7.

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