CN109960650B - Big data-based application program evaluation method, device, medium and electronic equipment - Google Patents
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Abstract
The invention belongs to the technical field of big data, and relates to an application program evaluation method and device based on big data, electronic equipment and a storage medium. The method comprises the following steps: acquiring a functional module to be evaluated contained in the application program; determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated; calculating and determining index parameter values corresponding to the plurality of evaluation indexes; and determining the score value of the functional module to be evaluated according to the index parameter values. The technical scheme of the embodiment of the invention can improve the accuracy of application program evaluation.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an application program evaluation method, device, medium and electronic equipment based on big data.
Background
With the rapid development of computer software technology, the variety and number of computer applications has also grown exponentially.
In general, different applications can implement different functions, but the types of applications that can implement the same type of functions are very large. For example, when a mobile phone user selects an application program, the application programs which can be screened by the same function are very many, in an application store of the mobile phone, the user can select according to the score of each application program, but the score of the application program is often determined by the subjective evaluation of the used user, and the difference of terminal equipment can also cause the experience of the user to be different when the user uses, and the existing scores of the application programs lack evaluation for the function. Therefore, the evaluation system of the existing application program lacks objectivity to the evaluation of each application program, so that the evaluation is inaccurate and the reference value is low.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the invention and thus may include information that does not form the prior art that is already known to those of ordinary skill in the art.
Disclosure of Invention
The embodiment of the invention aims to provide an application program evaluation method based on big data, so as to solve the problem of inaccurate evaluation of an application program at least to a certain extent.
Other features and advantages of the invention will be apparent from the following detailed description, or may be learned by the practice of the invention.
According to a first aspect of an embodiment of the present invention, there is provided an application program evaluation method based on big data, including:
acquiring a functional module to be evaluated contained in the application program;
determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated;
calculating and determining index parameter values corresponding to the plurality of evaluation indexes;
and determining the score value of the functional module to be evaluated according to the index parameter values.
In an example embodiment of the present invention, the determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated includes:
inputting the characteristics of the functional module to be evaluated into a machine learning model, and determining a plurality of evaluation indexes of the functional module to be evaluated according to the output of the machine learning model.
In one example embodiment of the invention, the machine learning model comprises:
acquiring a preset number of sample data, wherein the sample data comprises a plurality of characteristics of a functional module and evaluation indexes corresponding to each characteristic;
the machine learning model is trained based on the sample data.
In an example embodiment of the present invention, the features of the functional module under evaluation include:
one or more of engagement, acceptance, retention;
in an example embodiment of the present invention, the determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated includes:
determining the evaluation index as an liveness contribution rate according to the participation degree;
determining the evaluation index as a target achievement rate according to the acceptance degree;
and determining the evaluation index as an active growth rate according to the retention.
In an example embodiment of the present invention, said determining a score value of the functional module under evaluation according to the index parameter values comprises:
obtaining output parameters of the functional module to be evaluated;
calculating the distance between the output parameter and the characteristic of the functional module to be evaluated;
and determining the weight of the evaluation index according to the distance, and carrying out weighted summation on index parameter values corresponding to the evaluation indexes to obtain the score value of the module to be evaluated.
In an example embodiment of the present invention, said determining a score value of the functional module under evaluation according to the index parameter values comprises:
and carrying out logarithmic calculation on the index parameter values, and taking the sum of the index parameter values after logarithmic calculation as the score value of the functional module to be evaluated.
According to a second aspect of the embodiment of the present invention, there is provided an application program evaluation device based on big data, including:
the module unit to be evaluated is used for acquiring the functional module to be evaluated;
the evaluation index determining unit is used for determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated;
a calculating unit, configured to calculate and determine index parameter values corresponding to the plurality of evaluation indexes;
and the score determining unit is used for determining the score value of the functional module to be evaluated according to the index parameter values.
According to a third aspect of embodiments of the present invention, there is provided a computer readable medium having stored thereon a computer program which when executed by a processor implements the big data based application evaluation method as described in the first aspect of the above embodiments.
According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device including: one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the big data based application evaluation method as described in the first aspect of the embodiments above.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects:
in the technical schemes provided by some embodiments of the present invention, by acquiring a to-be-evaluated functional module included in an application program, determining a plurality of evaluation indexes of the to-be-evaluated functional module according to characteristics of the to-be-evaluated functional module, calculating and determining index parameter values corresponding to the evaluation indexes, and determining a score value of the to-be-evaluated functional module according to the index parameter values, each function in the application program can be evaluated in a targeted manner, so that accuracy and referenceof scoring of the application program are improved; meanwhile, the evaluation can be objectively carried out, and the reliability of the application evaluation is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 schematically illustrates a flow chart of a big data based application evaluation method according to an embodiment of the invention;
FIG. 2 schematically illustrates a flow chart of a big data based application evaluation method according to another embodiment of the invention;
FIG. 3 schematically illustrates a block diagram of a big data based application evaluation device according to an embodiment of the invention;
fig. 4 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The embodiment of the invention firstly provides an application program evaluation method based on big data. As shown in fig. 1, the method may include steps S110, S120, S130, S140. Wherein:
step S110, obtaining a functional module to be evaluated contained in the application program;
step S120, determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated;
step S130, calculating and determining index parameter values corresponding to the plurality of evaluation indexes;
and step S140, determining the score value of the functional module to be evaluated according to the index parameter values.
According to the application program evaluation method based on big data in the present exemplary embodiment, by acquiring a to-be-evaluated function module included in an application program, determining a plurality of evaluation indexes of the to-be-evaluated function module according to characteristics of the to-be-evaluated function module, calculating and determining each index parameter value corresponding to the plurality of evaluation indexes, and determining a score value of the to-be-evaluated function module according to each index parameter value, each function in the application program can be evaluated in a targeted manner, so that accuracy and referenceof scoring of the application program are improved; meanwhile, the evaluation can be objectively carried out, and the reliability of the application evaluation is improved.
Next, the respective steps of the big data based application evaluation method in the present exemplary embodiment will be described in more detail with reference to fig. 1.
Step S110, a function module to be evaluated contained in the application program is obtained.
In the present example embodiment, the application program may be an executable file, or a computer program. The application may run on an operating system, interact with a user to perform a function, and may have a visual interface. Such as text editors, multimedia players, management systems, etc. The present exemplary embodiment is not particularly limited thereto. An application may be made up of a number of functional modules. For example, the WeChat may be a function module such as a send message function, a receive message function, etc. The functional module to be evaluated may be a functional module to be evaluated, such as a display function, a voice function, etc.
Step S120, determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated.
The characteristics of the functional module under evaluation may be used to represent the use of the functional module under evaluation by the user. For example, the feature of the functional module to be evaluated may be engagement, acceptance, retention, etc. In this example embodiment, the features may be one or more of the engagement, acceptance, retention described above. The engagement may represent the degree to which the user registers to use the function, the acceptance may be the user's acceptance of the function, and the retention may represent that the user remains or participates in using the function during use of the application. Of course, the characteristics of the band assessment function module may be other characteristics, such as activity, conversion degree, etc., according to actual situations.
And determining the evaluation index of the functional module to be evaluated according to the characteristics of the functional module to be evaluated. Each feature may determine an evaluation index, may determine an evaluation index from multiple features, or may determine multiple evaluation indexes from a feature. In this example embodiment, according to the above features, it may be determined that the evaluation index of the engagement degree is an activity contribution rate, the evaluation index of the acceptance degree is a target achievement rate, and the evaluation index of the retention degree is an activity growth rate. Of course, in practical situations, the evaluation index may also be other indexes, such as the yield, the conversion rate, and the like.
In the present example embodiment, the machine learning model may also be utilized to determine the evaluation index, thereby making the determination of the evaluation index more intelligent. After the characteristics of the functional module to be evaluated are input into the machine learning model, an evaluation index can be determined according to the output result of the machine learning model. For example, the degree of conversion is input as a feature to a machine learning model, which may output a yield rate or the like. Further, the machine learning model may be obtained by a training method of machine learning. By acquiring a preset number of sample data, the machine learning model may be trained using the sample data. The sample data may be data of sets of features corresponding to the evaluation index. Each sample may be a feature and an evaluation index. The number of samples may be determined according to practical situations, for example, 1000, 100, etc.
Step S130, calculating and determining index parameter values corresponding to the plurality of evaluation indexes.
The index parameter values may be determined from calculations between features. For example, for an application, if the engagement feature indicates the number of users using the function module, then the evaluation index of the feature is the target achievement rate, and the index parameter value of the target achievement rate may be the number of users using the function module divided by the download amount of the application. The index parameter value may also be calculated based on the weight of each evaluation index, for example, the index parameter value of the target achievement rate x the weight of the target achievement rate, and the like. Furthermore, the index parameter value may also be calculated using a logarithmic method, and the logarithmic formula used may be:wherein x is i The index parameter value, max (x), is the maximum index parameter value corresponding to the evaluation index. Of course, the index parameter value may be determined by other methods, for example, the index parameter value of the active growth rate may be a value obtained by dividing the receptivity by the participation degree, and the like.
And step S140, determining the score value of the functional module to be evaluated according to the index parameter values.
The score value of the functional module under evaluation may be used to evaluate the functional module under evaluation. A higher score value may indicate that the functional module under evaluation of the application under evaluation is better. The score value of the functional module to be evaluated can be determined by adding the values of the index parameters. Of course, it may also be determined in other ways, for example, by weighted averaging of the individual index parameter values, by averaging of the individual index parameter values, etc. Furthermore, the index parameter values can be balanced by a logarithmic calculation method, so that the influence of overlarge differences among the index parameter values on the score value is avoided. The formula for the logarithmic calculation may be: log of 2 (1+a/max (A)), where A is one of all index parameter values, a. Or can be determined by a method of weighted average of the parameter values of the respective indexes, the weight of each evaluation index can be obtained, the parameter value of the index corresponding to the respective evaluation index is multiplied by the weight of the evaluation index,the sum of the products is then added as the score value of the functional module to be evaluated. Accordingly, steps S210, S220, S230 are also included in the present exemplary embodiment. As shown in fig. 2, wherein:
step S210, obtaining the output parameters of the functional module to be evaluated.
The output parameter may be an output result of the functional module to be evaluated. The output result of the functional module to be evaluated may include features, such as retention, degree of success, etc., amount of success, etc., and may also include values corresponding to the features, for example, the output parameters of the functional module to be evaluated are: "retention was 0.5".
Step S220, calculating the distance between the output parameter and the feature of the functional module to be evaluated.
The distance of the output parameter from the feature of the functional module to be evaluated may be calculated using a distance calculation technique. Such as euclidean distance, mahalanobis distance, etc. The output parameter can be used as a characteristic, so that the similarity between the output parameter and the characteristic of the functional module to be evaluated is calculated by using the Euclidean distance calculation method, namely the distance between the output parameter and the characteristic. The output parameter and the feature may be converted into a vector, and the result of multiplying the vector may be used as the distance between the output parameter and the feature. Of course, other methods may be used, for example, the output parameters are: the retention, the feature of the functional module to be evaluated is: engagement, then the formula can be used:to calculate the distance, where x is the output parameter retention and y is the engagement.
And step S230, determining the weight of the evaluation index according to the distance.
The distance between the feature of the functional module to be evaluated and the output parameter of the functional module to be evaluated can be used as the weight of the evaluation index corresponding to the feature of the functional module to be evaluated. In step S220, the distance between the feature of each functional module to be evaluated and the output parameter may be calculated, so as to determine the weight of each evaluation index.
The following describes an embodiment of the apparatus of the present invention that may be used to perform the big data based application evaluation method of the present invention described above. As shown in fig. 3, the big data based application evaluation device 300 includes:
an obtaining module to be evaluated unit 310, configured to obtain a function module to be evaluated;
a determining and evaluating index unit 320, configured to determine a plurality of evaluating indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated;
a calculating unit 330, configured to calculate and determine index parameter values corresponding to the plurality of evaluation indexes;
and a score determining unit 340, configured to determine a score value of the functional module to be evaluated according to the index parameter values.
Since each functional module of the big data based application evaluation device according to the exemplary embodiment of the present invention corresponds to a step of the above-mentioned exemplary embodiment of the big data based application evaluation method, for details not disclosed in the embodiment of the device according to the present invention, please refer to the above-mentioned exemplary embodiment of the big data based application evaluation method according to the present invention.
Referring now to FIG. 4, there is illustrated a schematic diagram of a computer system 400 suitable for use in implementing an electronic device of an embodiment of the present invention. The computer system 400 of the electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of embodiments of the invention.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data required for the system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output portion 407 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker, and the like; a storage section 408 including a hard disk or the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. The drive 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.
In particular, according to embodiments of the present invention, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 409 and/or installed from the removable medium 411. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 401.
The computer readable medium shown in the present invention may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
As another aspect, the present application also provides a computer-readable medium that may be contained in the electronic device described in the above embodiment; or may exist alone without being incorporated into the electronic device. The computer-readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to implement the big data based application evaluation method as described in the above embodiments.
For example, the electronic device may implement the method as shown in fig. 1: step S110, obtaining a functional module to be evaluated contained in the application program; step S120, determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated; step S130, calculating and determining index parameter values corresponding to the plurality of evaluation indexes; and step S140, determining the score value of the functional module to be evaluated according to the index parameter values.
As another example, the electronic device may implement the steps shown in fig. 2.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the invention. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present invention.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (5)
1. An application program evaluation method based on big data, comprising:
acquiring a functional module to be evaluated contained in the application program;
determining a plurality of evaluation indexes of the functional module to be evaluated according to the characteristics of the functional module to be evaluated, including: inputting the characteristics of the functional module to be evaluated into a machine learning model, and determining a plurality of evaluation indexes of the functional module to be evaluated according to the output of the machine learning model, wherein the characteristics of the functional module to be evaluated comprise: engagement, acceptance, retention; determining the evaluation index as an activity contribution rate according to the participation degree; determining the evaluation index as a target achievement rate according to the acceptance degree; determining the evaluation index as an active growth rate according to the retention;
calculating and determining index parameter values corresponding to the plurality of evaluation indexes, wherein the index parameter value of the target achievement rate is a value obtained by dividing the number of users using the functional module by the downloading amount of an application program; the index parameter value of the active growth rate is a value of acceptance divided by participation;
determining a score value of the functional module to be evaluated according to the index parameter values, including:
obtaining output parameters of the functional module to be evaluated;
calculating the distance between the output parameter and the feature of the functional module to be evaluated, including: by means ofCalculating a distance, wherein x is the retention of output parameters, and y is the participation;
determining the weight of the evaluation index according to the distance, and carrying out weighted summation on index parameter values corresponding to the evaluation indexes to obtain a score value of the module to be evaluated;
the determining the score value of the functional module to be evaluated according to the index parameter values further includes:
carrying out logarithmic calculation on the index parameter values, and taking the sum of the index parameter values after logarithmic calculation as the score value of the functional module to be evaluated, wherein the formula of logarithmic calculation is as follows: log of 2 (1+a/max (A)), where A is one of all index parameter values, a.
2. The big data based application evaluation method of claim 1, wherein the machine learning model comprises:
acquiring a preset number of sample data, wherein the sample data comprises a plurality of characteristics of a functional module and evaluation indexes corresponding to each characteristic;
the machine learning model is trained based on the sample data.
3. An application evaluation device based on big data, comprising:
the module unit to be evaluated is used for acquiring the functional module to be evaluated;
the evaluation index determining unit is configured to determine a plurality of evaluation indexes of the to-be-evaluated functional module according to characteristics of the to-be-evaluated functional module, and includes: inputting the characteristics of the functional module to be evaluated into a machine learning model, and determining a plurality of evaluation indexes of the functional module to be evaluated according to the output of the machine learning model, wherein the characteristics of the functional module to be evaluated comprise: participation, acceptance, retention; determining the evaluation index as an activity contribution rate according to the participation degree; determining the evaluation index as a target achievement rate according to the acceptance degree; determining the evaluation index as an active growth rate according to the retention;
a calculating unit, configured to calculate and determine index parameter values corresponding to the plurality of evaluation indexes, where the index parameter value of the target achievement rate is a value obtained by dividing the number of users using the functional module by a download amount of an application program; the index parameter value of the active growth rate is a value of acceptance divided by participation;
the scoring unit is used for determining the scoring value of the functional module to be evaluated according to the index parameter values, and comprises the steps of obtaining the output parameter of the functional module to be evaluated; calculating the distance between the output parameter and the characteristic of the functional module to be evaluated; determining the weight of the evaluation index according to the distance, and carrying out weighted summation on index parameter values corresponding to the evaluation indexes to obtain a score value of the module to be evaluated, wherein the method comprises the following steps: by means ofCalculating a distance, wherein x is the retention of output parameters, and y is the participation;
the determining and scoring unit is further configured to perform logarithmic calculation on the index parameter values, and sum of the index parameter values after logarithmic calculation is used as a score value of the functional module to be evaluated, where a formula of logarithmic calculation is: log of 2 (1+a/max (A)), where A is one of all index parameter values, a.
4. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the big data based application evaluation method according to any of claims 1 or 2.
5. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the big data based application evaluation method of any of claims 1 or 2.
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| CN201811026551.XA CN109960650B (en) | 2018-09-04 | 2018-09-04 | Big data-based application program evaluation method, device, medium and electronic equipment |
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| CN111754126A (en) * | 2020-06-29 | 2020-10-09 | 支付宝(杭州)信息技术有限公司 | Method and system for evaluating applications |
| CN112270486A (en) * | 2020-11-04 | 2021-01-26 | 医渡云（北京）技术有限公司 | Data quality evaluation method and device, electronic equipment and readable medium |
| CN112988542B (en) * | 2021-04-08 | 2021-11-30 | 马上消费金融股份有限公司 | Application scoring method, device, equipment and readable storage medium |
| CN113807717A (en) * | 2021-09-23 | 2021-12-17 | 深圳市易平方网络科技有限公司 | Application program function evaluation method and device, terminal equipment and storage medium |
| CN114416208A (en) * | 2022-01-24 | 2022-04-29 | 杭州迪普科技股份有限公司 | Application program adjusting method and device, electronic equipment and medium |
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| WO2017167071A1 (en) * | 2016-03-30 | 2017-10-05 | 阿里巴巴集团控股有限公司 | Application program project evaluation method and system |
| CN107749006A (en) * | 2017-11-01 | 2018-03-02 | 广州爱九游信息技术有限公司 | Game appraisal procedure, device and equipment |
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| WO2017167071A1 (en) * | 2016-03-30 | 2017-10-05 | 阿里巴巴集团控股有限公司 | Application program project evaluation method and system |
| CN107749006A (en) * | 2017-11-01 | 2018-03-02 | 广州爱九游信息技术有限公司 | Game appraisal procedure, device and equipment |
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