CN111242656A - User credit evaluation method and system based on telecommunication big data - Google Patents
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Abstract
The embodiment of the invention provides a user credit evaluation method and a user credit evaluation system based on telecommunication big data, wherein the method comprises the following steps: acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users; acquiring a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user. The social relationship network constructed by the method is higher in credibility, and the basic credit value related to the attribute of the user is used as initial input in the iteration process, so that the finally obtained actual credit value of the user is more accurate, and the credit evaluation of the user is more credible.
Description
Technical Field
The embodiment of the invention relates to the technical field of big data, in particular to a user credit evaluation method and system based on telecommunication big data.
Background
With the continuous development of big data technology, the value of big data is continuously reflected, wherein big data credit is more and more emphasized by various industries as the important content of big data application. The user credit is the most direct standard for measuring the credit condition of the user and has important application value for various industries. For mobile operators, credit assessment of users has good reference values for accurate marketing, fraud prevention, mobile phone credit delay, cost overdraft and the like of the users, and is an important data manifestation method serving as capability openness after data desensitization is carried out on the users. Meanwhile, when the user's behavior is changing continuously, the credit evaluation should be adjusted in time according to the user's behavior, and reflect the latest credit of the user in time.
In the prior art, a plurality of user groups are formed based on internet social relations, and according to the assumption that the real credit assessment score of each user in the user groups is relatively close, the current credit assessment score of the user is determined by using the last credit assessment score of the user having the social relation with the user until the current credit assessment score of each user in the user groups meets a preset convergence condition, and the credit assessment score of a target user is determined.
However, in the prior art, the social relationship formed by virtual friends in the internet is taken as the basis, the social relationship of the internet is taken as the virtual social relationship, the theoretical assumption that the credit of users in the same user group is relatively close is weak, and users without similarity such as phishing are not rare, and the part of users will greatly interfere with the final credit evaluation.
Disclosure of Invention
Embodiments of the present invention provide a method and system for evaluating user credit based on telecom big data, which overcome the above problems or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a method for evaluating user credit based on telecommunications big data, including:
acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users;
acquiring a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user;
acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
In another aspect, an embodiment of the present invention provides a user credit evaluation system based on telecommunications big data, including:
the associated user acquisition module is used for acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users;
the first credit value acquisition module is used for acquiring the first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user;
the iteration module is used for acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
Third aspect, the embodiments of the present invention provide a system, a device and a method for evaluating user credit based on telecom big data, where the system includes a processor, a communication interface, a memory and a bus, where the processor, the communication interface, and the memory complete communication with each other through the bus, and the processor may call logic instructions in the memory to execute the method for evaluating user credit based on telecom big data provided in the first aspect.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for evaluating user credit based on telecom big data provided in the first aspect.
According to the method and the system for evaluating the credit of the user in the telecommunication big data, a social relationship network comprising a plurality of associated users is established through the calling and called times of each user and other users, the basic credit value of each associated user in the social relationship network is used as input, the actual credit value of each user is obtained through multiple iterations, the credibility of the social relationship network established by the method is higher, and the basic credit value related to the attribute of the user is used as initial input in the iteration process, so that the finally obtained actual credit value of the user is more accurate, and the credit evaluation of the user is more credible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a flowchart of a user credit evaluation method based on telecom big data according to an embodiment of the present invention;
fig. 2 is a block diagram of a user credit evaluation system based on telecommunications big data according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a user credit evaluation method based on telecommunication big data according to an embodiment of the present invention, as shown in fig. 1, including:
s101, acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users;
s102, acquiring a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user;
s103, acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
In step S101, the number of calls between all users and other users, i.e. the number of calling and called, can be obtained through the telecommunication big data. And for each user, selecting a plurality of users from other users having calling and called relations with the user as associated users according to the calling and called times between each user and other users. In this step, a social relationship network of each user is actually determined, and the social relationship network is composed of each user and a plurality of associated users corresponding to each user, and is centered on each user.
In step S102, in the social relationship network corresponding to each user obtained in step S101, the degree of closeness of the relationship between each user and each associated user may be measured by the number of times of corresponding calling and called, and the credit value of each user may be obtained according to the credit value of the associated user and the degree of closeness of the relationship between the associated user and each user. In this step, when calculating the first credit value of each user, the corresponding associated user adopts the basic credit, i.e. the first credit value is calculated from the basic credit values of the associated users. The basic credit value is an initial credit value related to the attribute of the user, which can approximately reflect the credit condition of the user, but the accuracy is relatively low.
In step S103, the calculation method of calculating the first credit value according to the basic credit value of the associated user in step S102 is utilized, the first credit value is utilized to calculate the second credit value of each user, that is, the calculation result of the previous step is the input of the next calculation, and the iteration steps are repeated until the preset iteration termination condition is met, so that the high-precision actual credit values of all users can be obtained.
Specifically, after the social relationship network of each user is established according to the telecommunication big data, the iterative calculation of the credit values of all the users can be started by taking the basic credit value as the initial input. And when the iteration times are increased continuously, the obtained credit value of each user is more and more accurate until a preset iteration termination condition is withheld, stopping iteration and outputting the credit value of each user.
The embodiment of the invention provides a user credit evaluation method for telecommunication big data, which is characterized in that a social relationship network comprising a plurality of associated users is established through the calling and called times of each user and other users, the basic credit value of each associated user in the social relationship network is taken as input, and the actual credit value of each user is obtained through multiple iterations.
In the above embodiment, before obtaining the first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user, the method further includes:
and acquiring the basic credit value of each user according to the social attribute, the credit history, the communication behavior and the default cost of each user.
Specifically, the basic credit needs to construct a credit index system to measure the credit status of the user in all directions, and then the basic credit is calculated based on the credit index system. The credit index system in the embodiment of the invention is mainly developed around 5 aspects (five-dimensional credit index system), namely the social attribute of the user, credit history, communication behavior, payment capability and default cost, and secondary indexes are shown in table 1.
TABLE 1
The basic credit calculation refers to the layer-by-layer calculation of a credit index system from bottom to top, the calculation core comprises variable calculation, index calculation and credit score calculation, and the basic credit construction can adopt a weighting model for calculation. The result of the basic credit calculation can preliminarily reflect the credit condition of the user, but the constructed credit indexes indirectly reflect the credit condition of the user and cannot be met at the same time, so the basic credit result still has a larger promotion space. It should be noted that each secondary index in table 1 is only a partial list, and in practice, other earphone indexes may be added according to actual needs.
In the above embodiment, the obtaining a plurality of associated users corresponding to each user according to the number of calling and called times between each user and other users specifically includes:
and taking a plurality of other users with the calling and called times larger than the first preset times between the other users and each user as a plurality of associated users corresponding to each user.
Specifically, when constructing the social relationship network of each user, in order to exclude annoying users and some users having a low degree of closeness with the user, a first preset number of times is set, that is, users whose number of calling and called with each user is less than the first preset number of times are screened out.
In the above embodiment, the obtaining the first credit value of each user according to the basic credit values of the multiple associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user specifically includes:
taking the calling and called times between each user and each corresponding associated user as a weight, and carrying out weighted average on the basic credit values of the associated users corresponding to each user to obtain a first credit value of each user; accordingly, the number of the first and second electrodes,
the obtaining a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user specifically includes:
and taking the calling and called times between each user and each corresponding associated user as a weight, and carrying out weighted average on the first credit values of the plurality of associated users corresponding to each user to obtain a second credit value of each user.
Specifically, the formula for iterating through all users is as follows:
A'i＝MAi
wherein, M represents a graph matrix, which represents the communication relation between users, and the value of the specific element is the result of the normalization of the calling and called times between users. A. theiRepresenting a user credit score adjacency matrix.
The formula for the weighted average for a single iteration of each user is as follows:
aiis a composition matrix AiAn element of aijDenotes aiOf the jth associated user, SijDenotes aiIs associated with the corresponding number of callers and callees of the user.
In each iteration, all users calculate at the same time, and the calculation result is used as the initial value of the next iteration calculation.
In the above embodiment, the preset iteration termination condition is:
the iteration times reach a second preset time, or the absolute value of the difference between the credit value obtained by each user iteration and the credit value before iteration is smaller than a preset value.
Specifically, in view of the huge number of users, a very large number of iterations may be required to reach convergence, a dual iteration termination condition of the number of iterations and the convergence accuracy may be set, and if one termination condition is reached first, the iteration is stopped.
In the above embodiment, after obtaining the actual credit value of each user, the method further includes:
and updating the basic credit value of each user according to the feedback result of the credit application of each user.
Further, the feedback result is divided into a positive feedback result and a negative feedback result; accordingly, the number of the first and second electrodes,
the updating the basic credit value of each user according to the feedback result of the credit application of each user specifically includes:
if the feedback result is judged and known to be a forward feedback result, forward correction is carried out on the basic credit value of each user; and if the feedback result is judged to be a negative feedback result, performing negative correction on the basic credit value of each user.
The credit application directions are various, such as telephone charge overdraft, mobile phone credit delay and the like, and services such as small loan in the financial field, consumption in the life service field, accommodation free reservation and the like can be provided for the outside at the same time. Any credit application is essentially the credit of the overdraft user, and when the user shows good performance behaviors after overdraft credit, such as overdraft telephone charge amortization, exempt-booking hotel check-in, etc., the user is indicated to have good credit, otherwise, the user is proved to have poor credit. The result feedback of the credit application is represented by positive and negative sides, and provides a timely correction basis for the credit evaluation of the user.
Specifically, for illustrating a specific embodiment of the feedback result according to the credit application of each user, the basic credit value of each user is updated, and the credit application is only used as an example, but not for limiting the present invention. The method comprises the following specific steps:
1) positive feedback
Positive feedback refers to the corresponding increase of the user credit score for the positive result feedback of the user. In the application of telephone charge overdraft, overdraft is essentially consumed in advance, similar to credit card repayment, and the repayment of overdraft in a specified period is positive feedback.
Wherein, b1、b2、b3The base numbers of the added marks under the three conditions of delay, normal and advanced repayment,
the proportion of the refunds is the proportion of the refunds,
is the repayment proportion.
2) Negative feedback
Negative feedback refers to a corresponding reduction in the user's credit score for negative result feedback by the user. In the application of telephone charge overdraft, the overdrawn amount is not fully paid back within a specified time limit, namely negative feedback.
Wherein, B1、B2The base numbers of credit and refund deductions,
is a refund proportion,
In a repayment proportion, C is a constant>＝0)。
3) Credit score correction
The credit score is modified simultaneously by positive feedback and negative feedback, as follows:
S＝S'+a*S+-(1-a)*S_
and the user refunds the lowest payment amount a to 1, otherwise, a to 0, S is the updated credit value, and S' is the credit value before updating.
The closed loop system with complete credit evaluation is formed, each updating is personalized calculation, the calculation complexity is low, the closed loop system is very suitable for online real-time dynamic credit updating, real-time calculation updating can be carried out by receiving online real-time generated feedback data with the aid of a real-time processing tool, and more time-efficient credit service is provided for users.
Fig. 2 is a block diagram of a user credit evaluation system based on telecommunications big data according to an embodiment of the present invention, as shown in fig. 2, including: an associated user acquisition module 201, a first credit acquisition module 202, and an iteration module 203. Wherein:
the associated user acquiring module 201 is configured to acquire a plurality of associated users corresponding to each user according to the number of times of calling and called between each user and other users. The first credit value obtaining module 202 is configured to obtain a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user. The iteration module 203 is configured to obtain a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
Specifically, the system further comprises a basic credit value acquisition module, which is used for acquiring the basic credit value of each user according to the social attribute, the credit history, the communication behavior and the default cost of each user.
Further, the associated user obtaining module 201 is specifically configured to:
and taking a plurality of other users with the calling and called times larger than the first preset times between the other users and each user as a plurality of associated users corresponding to each user.
Further, the first credit obtaining module 202 is specifically configured to:
and taking the calling and called times between each user and each corresponding associated user as a weight, and carrying out weighted average on the basic credit values of the plurality of associated users corresponding to each user to obtain a first credit value of each user.
Accordingly, the iteration module 203 is specifically configured to:
and taking the calling and called times between each user and each corresponding associated user as a weight, and carrying out weighted average on the first credit values of the plurality of associated users corresponding to each user to obtain a second credit value of each user.
Further, the system also comprises a basic credit value updating module which is used for updating the basic credit value of each user according to the feedback result of the credit application of each user.
Further, the basic credit value updating module is specifically configured to:
if the feedback result is judged and known to be a forward feedback result, forward correction is carried out on the basic credit value of each user; and if the feedback result is judged to be a negative feedback result, performing negative correction on the basic credit value of each user.
According to the user credit evaluation system for telecommunication big data, provided by the embodiment of the invention, a social relationship network comprising a plurality of associated users is established through the calling and called times of each user and other users, then the basic credit value of each associated user in the social relationship network is taken as input, the actual credit value of each user is obtained through multiple iterations, the credibility of the social relationship network established by the system is higher, and the basic credit value related to the attributes of the user is taken as initial input in the iteration process, so that the finally obtained actual credit value of the user is more accurate, and further the credit evaluation of the user is more credible.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes: a processor (processor)301, a communication Interface (communication Interface)302, a memory (memory)303 and a bus 304, wherein the processor 301, the communication Interface 302 and the memory 303 complete communication with each other through the bus 304. Processor 301 may call logic instructions in memory 303 to perform methods including, for example: acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users; acquiring a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
The logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention provide a non-transitory computer-readable storage medium, which stores computer instructions, where the computer instructions cause the computer to perform the methods provided by the above method embodiments, for example, the methods include: acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users; acquiring a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the communication device and the like are merely illustrative, and units illustrated as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods of the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A user credit evaluation method based on telecommunication big data is characterized by comprising the following steps:
acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users;
acquiring a first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user;
acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
2. The method of claim 1, further comprising, before obtaining the first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user, the steps of:
and acquiring the basic credit value of each user according to the social attribute, the credit history, the communication behavior and the default cost of each user.
3. The method according to claim 1, wherein the obtaining a plurality of associated users corresponding to each user according to the number of calling and called times between each user and other users specifically comprises:
and taking a plurality of other users with the calling and called times larger than the first preset times between the other users and each user as a plurality of associated users corresponding to each user.
4. The method according to claim 1, wherein the obtaining the first credit value of each user according to the basic credit values of the associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user specifically comprises:
taking the calling and called times between each user and each corresponding associated user as a weight, and carrying out weighted average on the basic credit values of the associated users corresponding to each user to obtain a first credit value of each user; accordingly, the number of the first and second electrodes,
the obtaining a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the number of times of calling and called between each user and each corresponding associated user specifically includes:
and taking the calling and called times between each user and each corresponding associated user as a weight, and carrying out weighted average on the first credit values of the plurality of associated users corresponding to each user to obtain a second credit value of each user.
5. The method according to claim 1, wherein the preset iteration termination condition is:
the iteration times reach a second preset time, or the absolute value of the difference between the credit value obtained by each user iteration and the credit value before iteration is smaller than a preset value.
6. The method of claim 1, further comprising, after obtaining the actual credit value of each user:
and updating the basic credit value of each user according to the feedback result of the credit application of each user.
7. The method of claim 6, wherein the feedback result is divided into a positive feedback result and a negative feedback result; accordingly, the number of the first and second electrodes,
the updating the basic credit value of each user according to the feedback result of the credit application of each user specifically includes:
if the feedback result is judged and known to be a forward feedback result, forward correction is carried out on the basic credit value of each user; and if the feedback result is judged to be a negative feedback result, performing negative correction on the basic credit value of each user.
8. A telecommunication big data based user credit evaluation system, comprising:
the associated user acquisition module is used for acquiring a plurality of associated users corresponding to each user according to the calling and called times between each user and other users;
the first credit value acquisition module is used for acquiring the first credit value of each user according to the basic credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user;
the iteration module is used for acquiring a second credit value of each user according to the first credit values of a plurality of associated users corresponding to each user and the calling and called times between each user and each corresponding associated user; and repeating the iteration steps until a preset iteration termination condition is met, and obtaining the actual credit value of each user.
9. An electronic device, comprising a processor, a communication interface, a memory and a bus, wherein the processor, the communication interface and the memory communicate with each other through the bus, and the processor can call logic instructions in the memory to execute the method for evaluating user credit based on telecom big data according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method for telecommunication big data based user credit evaluation according to any one of claims 1 to 7.
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| CN112270463A (en) * | 2020-10-16 | 2021-01-26 | 北京师范大学珠海校区 | Multi-party data aggregation processing method and device, electronic equipment and medium |
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