CN113590679A

CN113590679A - Clustering analysis method based on internet finance and big data analysis server

Info

Publication number: CN113590679A
Application number: CN202110809275.XA
Authority: CN
Inventors: 陈非
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-12-30
Filing date: 2020-12-30
Publication date: 2021-11-02
Also published as: CN113590678A; CN112765230A; CN112765230B

Abstract

The embodiment of the invention provides a clustering analysis method based on internet finance and a big data analysis server, which are used for calculating first feature differences between a user portrait feature corresponding to each payment user in a pre-collected payment user sample set and a plurality of first clustering feature representations according to a plurality of preset first clustering feature representations, determining a matching relation between each user portrait feature and each first clustering feature representation and carrying out clustering analysis on the payment users. And then, according to a plurality of preset second clustering feature representations, calculating second feature differences between the service image feature corresponding to each payment service in the pre-collected payment service sample set and the plurality of second clustering feature representations, and determining a matching relation between each service image feature and each second clustering feature representation for carrying out clustering analysis on the payment services.

Description

Clustering analysis method based on internet finance and big data analysis server

The application is a divisional application of an invention patent application with the application number of 202011604964.9, the application date of 2020, 12 and 30, and the invention name of 'internet finance-based payment big data analysis method and big data analysis system'.

Technical Field

The invention relates to the technical field of big data analysis, in particular to a payment big data analysis method and a big data analysis system based on internet finance.

Background

With the rapid development of internet technology, the amount of data related to payment on internet financial platforms is rapidly increasing in exponential order, and accordingly, providers providing various internet financial services are emerging as in spring. As such, now and in the near future, the data accumulated in the internet financial network will be huge, so it is important how to mine and analyze the huge amount of financial payment data to improve the quality of financial services provided by each provider to the user.

Disclosure of Invention

Based on the defects of the existing design, in a first aspect, an embodiment of the present invention provides a payment big data analysis method based on internet finance, which is applied to a big data analysis server, and the method includes:

acquiring a payment user cluster obtained by carrying out cluster analysis on user portrait characteristics corresponding to each payment user in a pre-collected payment user sample set and a payment service cluster obtained by carrying out cluster analysis on service portrait characteristics corresponding to each payment service in a pre-collected payment service sample set;

according to user portrait characteristics corresponding to a target payment user, acquiring a payment user, of which the matching degree with the target payment user meets a first matching condition, in a payment user cluster corresponding to the target payment user, and using the payment user as an extended payment user corresponding to the target payment user;

adding the payment service operated by the target payment user in a preset time period and the payment service operated by the expanded payment user in the preset time period into a target payment service sequence as target payment services;

according to the service portrait characteristics corresponding to the target payment service, acquiring a payment service of which the matching degree with the target payment service meets a second matching condition in a payment service cluster corresponding to the target payment service, serving as an expanded payment service corresponding to the target payment service, and adding the expanded payment service into the target payment service sequence;

and calculating a matching probability value between the target payment user and each payment service in the target payment service sequence, and selecting a recommended payment service corresponding to the target payment user in the target payment service sequence according to the matching probability value so as to recommend the payment service to the target payment user.

According to an implementation manner of the first aspect, the obtaining, according to a user portrait feature corresponding to a target payment user, a payment user whose matching degree with the target payment user satisfies a first matching condition in a payment user cluster corresponding to the target payment user as an extended payment user corresponding to the target payment user includes:

determining payment users other than the target payment user in a payment user cluster corresponding to the target payment user as alternative payment users, and acquiring user portrait characteristics corresponding to the target payment user and the alternative payment users respectively;

acquiring cross payment service between the target payment user and the alternative payment user, and calculating the matching degree of the payment users between the target payment user and the alternative payment user according to a first average operation heat degree of service tags matched with the cross payment service in user portrait characteristics respectively corresponding to the target payment user and the alternative payment user and a second average operation heat degree of all service tags in user portrait characteristics respectively corresponding to the target payment user and the alternative payment user; the cross payment service is the intersection between the payment service operated by the target payment user and the payment service operated by the alternative payment user;

and sequencing all the alternative payment users according to the matching degree of the payment users, selecting a preset number of the alternative payment users according to a sequencing result, determining the alternative payment users as the payment users meeting a first matching condition, and using the alternative payment users as the extended payment users corresponding to the target payment users.

According to an implementation manner of the first aspect, the obtaining, according to the service representation feature corresponding to the target payment service, a payment service whose matching degree with the target payment service satisfies a second matching condition in a payment service cluster corresponding to the target payment service as an extended payment service corresponding to the target payment service includes:

determining payment services except the target payment service in a payment service cluster corresponding to the target payment service as payment services to be matched, and acquiring service portrait characteristics corresponding to the target payment service and the payment services to be matched respectively;

acquiring a service tag combination between the target payment service and the payment service to be matched, and calculating the matching degree of the payment service between the target payment service and the payment service to be matched according to service tags matched with the service tag combination in service portrait characteristics respectively corresponding to the target payment service and the payment service to be matched; the service label combination is a union between the payment service attribute corresponding to the target payment service and the payment service attribute corresponding to the payment service to be matched;

and sequencing all the payment services to be matched according to the matching degree of the payment services, selecting a preset number of payment services to be matched according to a sequencing result, determining the payment services meeting a second matching condition, and determining the payment services meeting the second matching condition as extended payment services corresponding to the target payment services.

According to an implementation manner of the first aspect, the calculating a matching probability value between the target payment user and each payment service in the target payment service sequence, and selecting a recommended payment service corresponding to the target payment user in the target payment service sequence according to the matching probability value includes:

taking each payment service in the target payment service sequence as a payment service to be recommended;

calculating a matching probability value between the target payment user and each payment service to be recommended;

and sequencing all the payment services to be recommended according to the matching probability values, and selecting a plurality of payment services to be recommended as the recommended payment services corresponding to the target payment users according to sequencing results.

According to an implementation manner of the first aspect, the calculating a matching probability value between the target payment user and each payment service to be recommended includes:

acquiring a first operation frequency of the extended payment user in the preset time period for each payment service in the target payment service sequence;

calculating a first total number of times that each payment service is operated by all the extended payment users within the preset time period according to the first operation number;

acquiring a second operation frequency of the target payment user in the preset time period for each payment service in the target payment service sequence;

calculating a second total number of times of each payment service operated by the target payment user in the preset time period according to the second operation times;

calculating to obtain a matching probability value of the target payment user and the payment service according to the first operation times, the first total times, the second operation times and the second total times corresponding to each payment service in the target payment service sequence; the method specifically comprises the following steps:

calculating the ratio of the first operation times corresponding to each payment service to the first total times to obtain a first percentage corresponding to each payment service;

calculating the ratio of the second operation times corresponding to each payment service to the second total times to obtain a second percentage corresponding to each payment service;

acquiring a preset first weight corresponding to the first percentage and a preset second weight corresponding to the second percentage;

according to the first percentage, the second percentage, the first weight and the second weight, calculating a matching probability value of the target payment user and each payment service, specifically comprising: and multiplying the first percentage corresponding to each payment service by the first weight to obtain a first probability value, multiplying the second percentage by the second weight to obtain a second probability value, and taking the sum of the first probability value and the second probability value as the matching probability value of the target payment user and the payment service.

According to an embodiment of the first aspect, the method further comprises:

establishing service heat distribution corresponding to the target payment user according to the payment service operated by the target payment user within a preset time period; the service heat distribution comprises an operation heat value corresponding to the payment service operated by the target payment user;

calculating according to a preset time heat relation function and the service heat distribution to obtain a heat characteristic distribution set corresponding to the payment service operated by the target payment user;

and calculating to obtain user portrait characteristics corresponding to the target payment user according to the heat characteristic distribution set.

According to an embodiment of the first aspect, the method further comprises:

acquiring an operation heat value of each payment service operated by the target payment user in the preset time period;

selecting a plurality of payment services as payment services to be selected according to the operation heat value;

and acquiring a service tag matched with the payment service to be selected from a payment service tag set corresponding to the pre-collected payment service sample set according to the payment service to be selected, and acquiring a service portrait characteristic corresponding to the target payment service according to the service tag matched with the payment service to be selected.

According to an embodiment of the first aspect, the method further comprises:

calculating a first feature difference between a user portrait feature corresponding to each payment user in the pre-collected payment user sample set and a plurality of first cluster feature representations according to the preset plurality of first cluster feature representations;

determining a matching relation between each user portrait feature and each first cluster feature representation according to the first feature difference, and classifying payment users corresponding to the user portrait features matched with the same first cluster feature representation into the same payment user cluster; wherein the number of payment user clusters is the same as the number of first cluster feature representations;

according to a plurality of preset second clustering feature representations, calculating second feature differences between service portrait features corresponding to each payment service in the pre-collected payment service sample set and the plurality of second clustering feature representations;

determining a matching relation between each service portrait feature and each second clustering feature representation according to the second feature difference, and classifying the payment services corresponding to the service portrait features which are matched with the same second clustering feature representation into the same payment service cluster; wherein the number of payment service clusters is the same as the number of second cluster feature representations.

According to an implementation manner of the first aspect, the adding, as a target payment service, a payment service operated by the target payment user within a preset time period and a payment service operated by the extended payment user within the preset time period into a target payment service sequence includes:

acquiring payment data which is matched with the user information and is generated in the preset time period from the payment data stored in the big data analysis server according to the respective user information of the target payment user and the extended payment user;

performing data cleaning on the payment data by adopting a preset data cleaning algorithm, and removing invalid data in the payment data;

extracting service identification information of all payment services contained in the payment data from the cleaned payment data;

and performing duplicate removal processing on all the extracted service identification information, and adding the duplicate-removed service identification into a preset data sequence to obtain the target payment service sequence.

According to a second aspect of the embodiments of the present invention, there is also provided a big data analysis system, including a big data analysis server and a plurality of payment terminals communicatively connected to the big data analysis server and respectively corresponding to a plurality of payment users, the big data analysis server including a big data analysis apparatus, a processor, and a machine-readable storage medium connected to the processor, the machine-readable storage medium storing a program, instructions, or codes included in the big data analysis apparatus; wherein the big data analysis device includes:

the system comprises a cluster acquisition module, a service acquisition module and a service processing module, wherein the cluster acquisition module is used for acquiring payment user clusters obtained by carrying out cluster analysis on user portrait characteristics corresponding to each payment user in a pre-collected payment user sample set and payment service clusters obtained by carrying out cluster analysis on service portrait characteristics corresponding to each payment service in the pre-collected payment service sample set;

the system comprises an extended user determining module, a payment user determining module and a payment user selecting module, wherein the extended user determining module is used for acquiring payment users, of which the matching degree with a target payment user meets a first matching condition, in a payment user cluster corresponding to the target payment user according to user portrait characteristics corresponding to the target payment user, and the payment users are used as extended payment users corresponding to the target payment user;

a service sequence generation module, configured to add a payment service operated by the target payment user in a preset time period and a payment service operated by the extended payment user in the preset time period as target payment services to a target payment service sequence;

the extended service adding module is used for acquiring a payment service of which the matching degree with the target payment service meets a second matching condition in a payment service cluster corresponding to the target payment service according to the service portrait characteristics corresponding to the target payment service, and adding the acquired payment service as an extended payment service corresponding to the target payment service into the target payment service sequence; and

and the recommendation service calculation module is used for calculating a matching probability value between the target payment user and each payment service in the target payment service sequence, and selecting a recommendation payment service corresponding to the target payment user in the target payment service sequence according to the matching probability value so as to recommend the payment service to the target payment user.

In summary, compared with the prior art, the payment big data analysis method and the payment big data analysis system based on internet finance provided by the embodiment of the invention are based on a clustering mining method, so that the recommendation of payment service for a target payment user is realized, and the use experience of the user when using an internet finance platform can be improved. Meanwhile, the payment services provided by the financial platform are accurately pushed to the user through a clustered data mining method, so that the utilization rate of each payment service of the platform can be improved, the user flow of the platform is favorably improved, and the financial service quality is improved.

Further, according to the user portrait characteristics corresponding to the target payment user, the payment user whose matching degree with the target payment user meets the first matching condition is obtained in the payment user cluster corresponding to the target payment user and serves as an extended payment user corresponding to the target payment user, then the payment service operated by the target payment user and the payment service operated by the extended payment user serve as the target payment service and serve as data support recommended by the payment service, so that the target payment service sequence not only can include the payment service operated by the extended payment user, but also can include the extended payment service corresponding to the target payment service. Therefore, even if the number of the payment services operated by the payment user is too small, the number of the payment services in the target payment service sequence can be increased through the expanded payment services corresponding to the target payment services, and then the recommendation calculation of the payment services is carried out on the target payment user, so that the accuracy and the recommendation effect of the payment service recommendation for the target user can be improved. Meanwhile, the expanded payment users are payment users with similar characteristics to the target payment users, and then the expanded payment users are searched in the payment user cluster where the target payment users are located, so that the index range can be narrowed, the related calculated amount is reduced, and the operation efficiency is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a schematic network architecture diagram of a big data analysis system according to an embodiment of the present invention.

Fig. 2 is a schematic flowchart of a payment big data analysis method based on internet finance according to an embodiment of the present invention.

Fig. 3 is a flow chart illustrating the sub-steps of step S12 in fig. 2.

Fig. 4 is a flowchart illustrating the sub-steps of step S14 in fig. 2.

Fig. 5 is a flowchart illustrating a specific implementation of calculating the matching probability value in step S15 in fig. 2.

Fig. 6 is a block schematic diagram of the big data analysis server in fig. 1.

Fig. 7 is a functional block diagram of the payment big data analysis apparatus in fig. 6.

Detailed Description

The technical solutions in the embodiments will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, wherein like reference numerals represent like elements in the drawings. It is apparent that the embodiments to be described below are only a part of the embodiments of the present invention, and not all of them. 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.

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

In addition, flow charts are used in this specification to illustrate operations performed by systems according to embodiments of the specification. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to these processes, or one or more operations may be removed from these processes.

To solve the problems described in the background, embodiments of the present invention innovatively provide a payment big data analysis method and a big data analysis system based on internet finance, and an alternative embodiment of the present invention is specifically described below with reference to the accompanying drawings.

Fig. 1 is a schematic diagram of a network architecture of a big data analysis system according to an embodiment of the present invention. The big data analysis system may include a big data analysis server 100 and a plurality of payment terminals 200. The big data analysis server 100 may be a server provided by an internet financial platform for providing a payment service, or may be a server separately used as a big data analysis server from the server. The payment terminal 200 may be a personal computer, a smart phone, an intelligent wearable device, or the like. Various payment services provided by the payment terminal 200 using the internet financial platform can be used by various payment users, the payment service described in this embodiment may be any payment-related service, and the payment-related service may be a payment-completed service, a payment-uncompleted service, a related service that has been operated once, and is not particularly related to whether a payment action is completed, for example, the payment service may be, but is not limited to, any payment-related service such as e-commerce products, insurance, loan, stock, fund, consulting service, and the like, and may be considered as the payment service described in the embodiment of the present invention. The big data analysis server 100 may be respectively connected to each payment terminal 200 through a network, each payment terminal 200 may implement a corresponding payment service, generate corresponding payment data, and send the payment data to the big data analysis server 100, and the big data analysis server 100 may perform data mining analysis according to the payment data of all the payment terminals 200.

Further, referring to fig. 2, a flow chart of the internet finance-based payment big data analysis method according to the embodiment of the present invention is shown. In this embodiment, the above method is executed by the big data analysis server 100. The detailed steps of the method will be described and explained in detail below with reference to the accompanying drawings.

Step S11, obtaining a payment user cluster obtained by performing cluster analysis on the user profile characteristic corresponding to each payment user in the pre-collected payment user sample set and a payment service cluster obtained by performing cluster analysis on the service profile characteristic corresponding to each payment service in the pre-collected payment service sample set.

In this embodiment, the payment user cluster may include a plurality of clusters (may be understood as a "cluster") corresponding to different user categories, and each cluster corresponding to a user category may include payment users having corresponding user category attributes. For example, the user category may be obtained by performing cluster analysis or cluster analysis on a large number of payment user samples in the payment user sample set according to category division of the payment user in each dimension, for example, the user category attributes of different dimensions may be an annual segment, occupation, consumption level, gender, and the like of the payment user, which is not specifically limited in this embodiment.

Accordingly, the payment service cluster may be a cluster comprising a plurality of different service classes, and the corresponding cluster for each service class may comprise payment services having a corresponding service class attribute. For example, the service class may be obtained by performing cluster analysis or cluster analysis on a large number of payment service samples in the payment service sample set according to the class division of the payment service in each dimension. For example, the service category attributes of different dimensions may be divided according to different functions or service types of the payment service, and may include, for example, cash services, insurance services, shopping services, and the like, which is not specifically limited in this embodiment.

Step S12, according to the user portrait characteristics corresponding to the target payment user, obtaining the payment users whose matching degree with the target payment user meets the first matching condition in the payment user cluster corresponding to the target payment user, and using the payment users as the extended payment users corresponding to the target payment user.

In this embodiment, the user profile feature may be obtained in advance in the following manner, which is specifically described below.

For example, the service heat degree distribution corresponding to the target payment user may be established according to the payment service operated by the target payment user within a preset time period. The service heat distribution comprises corresponding operation heat values of the payment services operated by the target payment users.

And then, calculating according to a preset time heat relation function and the service heat distribution to obtain a heat characteristic distribution set corresponding to the payment service operated by the target payment user.

And finally, calculating according to the heat characteristic distribution set to obtain the user portrait characteristics corresponding to the target payment user.

In detail, in this embodiment, the time heat relation function may be a heat decay function in which the heat decays with time, so that the operation heat of the payment user on the payment service and the factor decaying with time may be considered to perform corresponding user portrayal, so that the portrayal is more accurate and the usability is better. In addition, the heat feature distribution set can be input into a network model obtained through pre-training for calculation, and user portrait features corresponding to the payment users can be obtained. The heat characteristic distribution set may include payment services operated by the target payment user in the preset time period and an operation heat coefficient corresponding to each payment service, where the operation heat coefficient may be calculated according to an operation frequency of each payment service, or may be represented by the operation frequency, and a specific manner is not limited.

Further, the service profile feature may be obtained in advance in the following manner, which is specifically described below.

Firstly, obtaining an operation heat value of each payment service operated by the target payment user in the preset time period.

And then, selecting a plurality of payment services as the payment services to be selected according to the operation heat value.

And finally, acquiring a service tag matched with the payment service to be selected from a payment service tag set corresponding to the pre-collected payment service sample set according to the payment service to be selected, and acquiring a service portrait characteristic corresponding to the target payment service according to the service tag matched with the payment service to be selected.

In detail, similarity matching can be performed on each payment service sample in the payment service sample set according to attribute characteristics of each payment service, such as payment amount, service type and the like, a service tag corresponding to the payment service sample meeting the similarity matching condition is used as a service tag of a corresponding target payment service, and finally, a plurality of tags of the target payment service can be combined to obtain a service portrait characteristic corresponding to the target payment service.

Step S13, adding the payment service operated by the target payment user in a preset time period and the payment service operated by the extended payment user in a preset time period as target payment services into a target payment service sequence. Therefore, the target payment service sequence not only considers the payment service corresponding to the target payment user, but also considers the payment service of the expanded payment user similar to the image of the target payment user, and is used for data support subsequently used as service recommendation of the target payment user. Therefore, under the condition that the payment service quantity of the target payment user is small, the historical payment service quantity which can be used for service recommendation can be expanded, so that subsequent service recommendation can be more accurate, the effect is better, and the recommendation range is wider.

In detail, in this embodiment, the payment data that is matched with the user information and is generated within the preset time period may be first obtained from the payment data stored in the big data analysis server 100 according to the respective user information of the target payment user and the extended payment user; then, data cleaning is carried out on the payment data by adopting a preset data cleaning algorithm, and invalid data in the payment data are removed; for example, the payment data without actual payment operation, the payment data with the retention time of the operation page corresponding to the corresponding payment service being lower than the preset time, the repeated data and the data with the missing attribute tag can be removed through data cleaning; then, extracting service identification information of all payment services contained in the payment data from the cleaned payment data; and finally, performing duplicate removal processing on all the extracted service identification information, and adding the duplicate-removed service identification into a preset data sequence to obtain the target payment service sequence.

Step S14, according to the service portrait characteristics corresponding to the target payment service, obtaining the payment service of which the matching degree with the target payment service satisfies a second matching condition in the payment service cluster corresponding to the target payment service, and adding the payment service as the expanded payment service corresponding to the target payment service into the target payment service sequence. Therefore, the target payment service sequence not only considers the target payment user and the payment service of the expanded payment user similar to the target payment user in image, but also further expands the payment service of other payment users similar to the service image characteristic of the target payment service, and is used for data support of service recommendation of the target payment user in the follow-up process. Therefore, under the condition that the payment service quantity of the target payment user is small, the historical payment service quantity which can be used for service recommendation can be expanded, so that subsequent service recommendation can be more accurate, the effect is better, and the recommendation range is wider.

Step S15, calculating a matching probability value between the target payment user and each payment service in the target payment service sequence, and selecting a recommended payment service corresponding to the target payment user in the target payment service sequence according to the matching probability value, so as to recommend the payment service to the target payment user.

In this embodiment, the payment user clustering and the payment service clustering in step S11 may be implemented by the following method, which is described in detail below.

Firstly, according to a plurality of preset first cluster feature representations, calculating a first feature difference between a user portrait feature corresponding to each payment user in the pre-collected payment user sample set and the plurality of first cluster feature representations.

Then, according to the first feature difference, determining a matching relation between each user portrait feature and each first cluster feature representation, and classifying payment users corresponding to the user portrait features matched with the same first cluster feature representation into the same payment user cluster; wherein the number of payment user clusters may be the same as the number of first cluster feature representations. In detail, in this embodiment, the first clustering feature representation may be an expression form of a feature vector, may be a preset central feature vector, and each user portrait feature may also be represented in a feature vector form, and the first feature difference is a vector difference between the theme portrait feature and each first clustering feature, and may be represented by, for example, a euclidean distance, a manhattan distance, and the like, which is not limited specifically.

Secondly, according to a plurality of preset second clustering feature representations, calculating second feature differences between service portrait features corresponding to each payment service in the pre-collected payment service sample set and the plurality of second clustering feature representations.

Finally, according to the second characteristic difference, determining a matching relation between each service portrait characteristic and each second clustering characteristic representation, and classifying the payment services corresponding to the service portrait characteristics which are matched with the same second clustering characteristic representation into the same payment service clustering; wherein the number of payment service clusters may be the same as the number of second cluster feature representations. In detail, in this embodiment, the second clustering feature representation may also be an expression form of a feature vector, and may also be a preset central feature vector, each service portrait feature may also be represented in a form of a feature vector, and the second feature difference is a vector difference between the subject portrait feature and each second clustering feature, and may also be represented by, for example, a euclidean distance, a manhattan distance, and the like, which is not limited specifically.

Further, as shown in fig. 3, in the present embodiment, the step S12 can be completed through the following sub-steps, which are specifically described as follows.

And a substep S121, determining the payment users other than the target payment user in the payment user cluster corresponding to the target payment user as alternative payment users, and acquiring user portrait characteristics respectively corresponding to the target payment user and the alternative payment users.

And a substep S122, obtaining the cross payment service between the target payment user and the alternative payment user, and calculating the matching degree of the payment users between the target payment user and the alternative payment user according to the first average operation heat of the service labels matched with the cross payment service in the user portrait characteristics respectively corresponding to the target payment user and the alternative payment user and the second average operation heat of all the service labels in the user portrait characteristics respectively corresponding to the target payment user and the alternative payment user. In this embodiment, the cross payment service may refer to an intersection between the payment service operated by the target payment user and the payment service operated by the alternative payment user. Further, a ratio of the first average heat of operation to the second average heat of operation value may be used as the payment user matching degree. Therefore, the matching degree can be calculated according to the similarity degree of the operation heat of the target payment user and the expanded payment user on the corresponding payment service.

And a substep S123 of sorting all the alternative payment users according to the matching degree of the payment users, and selecting a preset number of the alternative payment users according to a sorting result to determine the alternative payment users as the payment users meeting the first matching condition, wherein the selected alternative payment users are used as the expanded payment users corresponding to the target payment users.

In detail, in this embodiment, the alternative payment users may be sorted according to the matching degree of the payment users in a descending order, and then a preset number of alternative payment users with a smaller sorting number are selected as the payment users meeting the first matching condition as the expanded payment users according to a sorting result.

Referring to fig. 4, in the step S14, according to the service profile feature corresponding to the target payment service, a payment service whose matching degree with the target payment service satisfies a second matching condition is obtained in a payment service cluster corresponding to the target payment service, and a specific implementation manner of the payment service is described as follows.

And a substep S141, determining the payment service except the target payment service in the payment service cluster corresponding to the target payment service as the payment service to be matched, and acquiring the service portrait characteristics corresponding to the target payment service and the payment service to be matched respectively.

And a substep S142, obtaining a service label combination between the target payment service and the payment service to be matched, and calculating the matching degree of the payment service between the target payment service and the payment service to be matched according to the service labels matched with the service label combination in the service portrait features respectively corresponding to the target payment service and the payment service to be matched. In this embodiment, the service tag combination may be a union between a payment service attribute corresponding to the target payment service and a payment service attribute corresponding to the payment service to be matched. For example, the first tag quantity of the service tags matched in the target payment service and the service tag combination can be obtained according to the service portrait characteristic of the target payment service, the second tag quantity of the service tags matched in the service tag combination can be obtained according to the service portrait characteristic of the payment service to be matched, and then the ratio of the first tag quantity to the second tag quantity is used as the matching degree of the payment service.

And a substep S143, sorting all the payment services to be matched according to the matching degree of the payment services, selecting a preset number of payment services to be matched according to a sorting result, determining the payment services meeting the second matching condition, and determining the payment services meeting the second matching condition as the expanded payment services corresponding to the target payment services. For example, in this embodiment, the payment services to be matched may be sorted according to the sequence from the large matching degree to the small matching degree of the payment services, and then two preset payment services to be matched with a smaller sorting sequence number are selected as the extended payment services according to a sorting result.

In the step S15, a matching probability value between the target payment user and each payment service in the target payment service sequence is calculated, and a recommended payment service corresponding to the target payment user is selected from the target payment service sequence according to the matching probability value, and a specific implementation manner is exemplarily described as follows.

Firstly, a matching probability value between the target payment user and each payment service to be recommended is calculated.

And then, sequencing all the payment services to be recommended according to the matching probability values, and selecting a plurality of payment services to be recommended as the recommended payment services corresponding to the target payment users according to sequencing results. For example, the payment services to be recommended may be ranked according to the sequence of the matching probability values from top to bottom, where the top N payment services to be recommended are used as the recommended payment services of the target payment user, and N is a preset positive integer.

Further, referring to fig. 5, a specific implementation manner of calculating the matching probability value between the target payment user and each payment service in the target payment service sequence in step S15 may be implemented with reference to the step flow shown in fig. 5, which is described in detail as follows.

Step S61, acquiring a first operation number of the extended payment user in the preset time period for each payment service in the target payment service sequence.

Step S62, calculating a first total number of times that each payment service is operated by all the extended payment users within the preset time period according to the first operation number.

Step S63, acquiring a second operation number of the target payment user in the preset time period for each payment service in the target payment service sequence.

Step S64, calculating a second total number of times that each payment service is operated by the target payment user within the preset time period according to the second number of operations.

Step S65, calculating a matching probability value between the target payment user and the payment service according to the first operation times, the first total times, the second operation times and the second total times corresponding to each payment service in the target payment service sequence. In detail, first, a ratio of a first operation frequency corresponding to each payment service to a first total frequency may be calculated to obtain a first percentage corresponding to each payment service; then, calculating the ratio of the second operation times corresponding to each payment service to the second total times to obtain a second percentage corresponding to each payment service; then, acquiring a preset first weight corresponding to the first percentage and a preset second weight corresponding to the second percentage; and finally, calculating to obtain a matching probability value of the target payment user and each payment service according to the first percentage, the second percentage, the first weight and the second weight. For example, a first percentage corresponding to each payment service may be multiplied by a first weight to obtain a first probability value, a second percentage may be multiplied by a second weight to obtain a second probability value, and then a sum of the first probability value and the second probability value may be used as a matching probability value of the target payment user and the payment service. The operation times may be the sum of the times of any operation type of operations such as payment operation, consultation operation, browsing operation, entering a page, and the like performed on any payment service, and may be determined according to the actual situation, and is not limited specifically here.

On the basis of the above steps, a recommended payment service list for the target payment user may be obtained, and then, payment service pushing may be performed based on the recommended payment service list, in a further implementation, the method of the embodiment of the present application may further include the following steps:

step S16, obtaining a recommended payment service list aiming at a target payment user, wherein the recommended payment service list comprises a plurality of payment services to be recommended obtained by aiming at a large data user portrait of the target payment user.

In this embodiment, the recommended payment service list may be obtained by obtaining a plurality of payment services to be recommended.

Step S17, obtaining a first weight characteristic of each to-be-recommended payment service in the recommended payment service list, and a second weight characteristic of each page payment service on a display page of the payment service provided by the big data analysis server. In this embodiment, the first weight feature includes a weight coefficient used for determining a display priority of the payment service to be recommended, and the second weight feature includes a weight coefficient used for determining a display priority of the page payment service. The page payment service refers to a payment service displayed or to be displayed on the display page, and the payment service displayed on the page may refer to an operation option corresponding to the payment service displayed on the page.

Step S18, displaying the payment service to be recommended in the recommended payment service list on the display page of the payment service according to the first weight characteristic of each payment service to be recommended and the second weight characteristic of each payment service on the page, so as to implement recommendation of the payment service to be recommended.

In detail, in this embodiment, in step S17, the first weight characteristic of the payment service to be recommended may be implemented by the following alternative exemplary schemes, which are specifically described as follows:

firstly, for each payment service to be recommended, obtaining a first weight coefficient of the payment service to be recommended, wherein the first weight coefficient is a weight value corresponding to the last time the payment service to be recommended is displayed. For example, for a payment service, a first weight coefficient may be set in advance at the beginning, and in the subsequent service use process, the first weight coefficient may be updated according to a pre-specified correlation algorithm according to the use condition of the payment service. For example, for each payment service to be recommended, when it was last shown, a weighting coefficient may be obtained according to the frequency of operation in the previous cycle, and a weight value obtained by multiplying the weighting coefficient by the previous first weight coefficient may be updated to the corresponding first weight coefficient when the payment service to be recommended was last shown.

Then, determining a first weighting coefficient corresponding to each first weighting coefficient in the recommended payment service list, and taking the first weighting coefficient and the first weighting coefficient as a first weighting characteristic of the payment service to be recommended. For example, the first weighting factor may be determined according to the frequency of the operation of the corresponding payment service to be recommended in the previous period, as described above. Alternatively, in this embodiment, the first weighting coefficient is determined according to an arrangement order of the payment services to be recommended in the payment service list to be recommended, for example, if the payment service list to be recommended includes three payment services to be recommended, i.e., a1, a2, and A3, and the three payment services to be recommended are arranged sequentially, the first weighting coefficient corresponding to a1 may be 2.0, the first weighting coefficient corresponding to a2 may be 1.8, and the first weighting coefficient corresponding to A3 may be 1.5. In this embodiment, the larger the first weighting coefficient value is, the higher the recommendation priority of the corresponding payment item to be recommended is, and the payment item to be recommended should be recommended preferentially.

Second, the second weight characteristic of the page payment service can be obtained through the following exemplary scheme, which is described in detail as follows.

Firstly, for each page payment service, a second weight coefficient of the page payment service is obtained, wherein the second weight coefficient is a weight value corresponding to the last time the page payment service is displayed. The meaning of the second weight coefficient is the same as that of the first weight coefficient, and is not described herein again.

Then, determining a second weighting coefficient corresponding to the second weighting coefficient, and taking the second weighting coefficient and the second weighting coefficient as the second weighting characteristic, wherein the second weighting coefficient of each page payment service is the same and smaller than the first weighting coefficient of any one payment service to be recommended, and the first weighting coefficients corresponding to the payment services to be recommended in the payment service list to be recommended are sequentially reduced according to the arrangement sequence of the payment services to be recommended. In this embodiment, the second weighting coefficients corresponding to different service payment services may be the same or different, and in this embodiment, each second weighting coefficient may be sequentially determined according to a display sequence of each page payment service on a page.

In detail, in this embodiment, in step S18, the to-be-recommended payment service in the recommended payment service list is displayed on the display page of the payment service according to the first weight characteristic of each to-be-recommended payment service and the second weight characteristic of each page payment service, and an alternative implementation manner is exemplarily described as follows.

Firstly, determining a first display priority parameter of the payment service to be recommended according to the first weight characteristic, and determining a second display priority parameter of the page payment service according to the second weight characteristic.

And then, determining a final display priority parameter of the payment service to be recommended according to the first display priority parameter and the second display priority parameter, and displaying the payment service to be recommended in the payment service list to be recommended on a display page of the payment service according to the final display priority parameter.

For example, the first display priority parameter of the payment service to be recommended is determined according to the first weight characteristic, and the second display priority parameter of the page payment service is determined according to the second weight characteristic, and a specific implementation manner may be exemplarily described as follows.

Firstly, regarding each payment service to be recommended, taking the product of a first weighting coefficient of the payment service to be recommended and a corresponding first weighting coefficient as the first display priority parameter.

Then, for each page payment service, taking the product of the second weighting coefficient of the page payment service and the corresponding second weighting coefficient as the second display priority parameter.

On the basis, the final display priority parameter of the payment service to be recommended is determined according to the first display priority parameter and the second display priority parameter, and the payment service to be recommended in the payment service list to be recommended is displayed on the display page of the payment service according to the final display priority parameter, and a specific alternative implementation manner can be described as follows.

First, the first display priority parameter corresponding to each payment service to be recommended and the second display priority parameter corresponding to each page payment service can be sorted from large to small, the sorting sequence number of each payment service to be recommended is used as the final display priority parameter of each payment service to be recommended according to the sorting result, and the sorting sequence number of each page payment service is used as the final display priority parameter of each page payment service.

And then, displaying each payment service to be recommended at a page position corresponding to the corresponding final display priority parameter and displaying each page payment service at a display position corresponding to the final display priority parameter of each page payment service, wherein the display page of the payment service comprises a plurality of display positions, and each display position is provided with a position identifier corresponding to the final display priority parameter of one payment service.

Further, in another alternative embodiment, the final display priority parameter of the payment service to be recommended is determined according to the first display priority parameter and the second display priority parameter, and the payment service to be recommended is displayed on the display page of the payment service according to the final display priority, which is described as follows in an exemplary manner.

Firstly, for each payment service to be recommended, according to a service label of the payment service to be recommended, a first target service sequence matched with the service label is inquired from a plurality of preset service sequences, and the payment service to be recommended is added into the matched first target service sequence. Each service sequence comprises a plurality of payment services, and each service sequence corresponds to one display area on the display page. In this embodiment, the service tag may be an attribute ID of each payment service to be recommended, and may be attribute information configured in advance according to a type of each payment service, which is not specifically limited herein.

Then, after each payment service to be recommended is added to the corresponding matched first target service sequence, the payment services in each first target service sequence are ranked according to the descending order of the first display priority parameter and the second display priority parameter, and the ranking number of each payment service in the first target service sequence is used as the final display priority parameter corresponding to each payment service. It should be understood that, after each payment service to be recommended is added to the corresponding matched first target service sequence, the payment services in the first target service sequence include the added payment service to be recommended and the payment services existing in the first target service sequence.

And finally, displaying each payment service in a corresponding display position in the display area according to the final display priority parameter of each payment service in the first target service sequence in the display area corresponding to each first target service sequence. The presentation area includes a plurality of presentation positions, each presentation position having a position identification corresponding to a final presentation priority parameter of a payment service.

Exemplarily, if the payment service to be recommended includes three service types a1, a2, and A3, the first weighting coefficients corresponding to the service types a1, a2, and A3 are x1, x2, and x3, respectively. The first target service sequence added with a1 has four page payment services B1, B2, B3 and B4 before, the corresponding second weighting coefficients B1, B2, B3 and B4, and the corresponding second weighting coefficients y1, y2, y3 and y 4. Through calculation, the final display priority parameters corresponding to the payment services in the first target service sequence are arranged in an order [ b1 × y1, a1 × x1, b2 × y2, b3 × y3, b4 × y4 ]. Then, finally, in the display area corresponding to the first target service sequence, the final display order of each payment service is: b1, A1, B2, B3 and B4.

In summary, the service recommendation method can perform recommendation display of the payment service to be recommended according to comparison between the first weight characteristics of the payment service to be recommended and the second weight characteristics corresponding to the payment services on each page. Because the first weighting coefficient which is larger than the second weighting coefficient of the second weighting characteristic is added into the first weighting characteristic, the recommendation display sequence of the payment service to be recommended can be properly moved to a more important display position (in front), so that the purpose of service recommendation is achieved, meanwhile, other important page payment services on the page are referred, and the situation that all the more important page payment services are placed at a secondary position behind the payment service to be recommended to be displayed when the payment service is recommended is avoided, compared with the common method that the payment service is placed at a secondary position behind the payment service to be recommended, the recommendation method is more in line with the service practice.

Further, referring to fig. 6, fig. 6 is a block diagram illustrating a big data analysis server 100 according to an embodiment of the present invention. In this embodiment, the big data analysis server 100 may include a big data analysis apparatus 110, a machine-readable storage medium 120, and a processor 130. The machine-readable storage medium 120 is connected to the processor 130, and the machine-readable storage medium 120 is used for storing programs, instructions or codes included in the big data analysis apparatus, for example, instructions or codes corresponding to various software functional modules included in the big data analysis apparatus 110.

In this embodiment, referring to fig. 7, the big data analysis apparatus 110 may include a cluster obtaining module 111, an expansion user determining module 112, a service sequence generating module 113, an expansion service adding module 114, and a recommendation service calculating module 115. The detailed description about each functional module described above is as follows.

The cluster obtaining module 111 is configured to obtain a payment user cluster obtained by performing cluster analysis on a user profile feature corresponding to each payment user in a pre-collected payment user sample set and a payment service cluster obtained by performing cluster analysis on a service profile feature corresponding to each payment service in a pre-collected payment service sample set. It can be understood that, for further implementation and content of the cluster obtaining module 111, reference may be made to the above detailed description for step S11, and details are not described herein again.

The extended user determining module 112 is configured to obtain, according to the user portrait features corresponding to the target payment user, a payment user whose matching degree with the target payment user meets a first matching condition in the payment user cluster corresponding to the target payment user, as an extended payment user corresponding to the target payment user. It is understood that, regarding more implementation manners and contents of the extended user determination module 112, reference may be made to the above detailed description related to step S12, and details are not described herein again.

The service sequence generating module 113 is configured to add the payment service operated by the target payment user in a preset time period and the payment service operated by the extended payment user in the preset time period as target payment services into a target payment service sequence. It can be understood that, regarding more implementation manners and contents of the service sequence generating module 113, reference may be made to the above-mentioned detailed description for step S13, and details are not described herein again.

The extended service adding module 114 is configured to obtain, according to the service representation feature corresponding to the target payment service, a payment service whose matching degree with the target payment service satisfies a second matching condition in a payment service cluster corresponding to the target payment service, and add the obtained payment service as an extended payment service corresponding to the target payment service sequence. It can be understood that, regarding more implementation manners and contents of the extended service adding module 114, reference may be made to the above detailed description related to step S14, and details are not described herein again.

The recommendation service calculation module 115 is configured to calculate a matching probability value between the target payment user and each payment service in the target payment service sequence, and select a recommendation payment service corresponding to the target payment user in the target payment service sequence according to the matching probability value, so as to recommend the payment service to the target payment user. It can be understood that, regarding more implementation manners and contents of the recommendation service calculating module 115, reference may be made to the above-mentioned detailed description related to step S15, and details are not described herein again.

In this embodiment, the machine-readable storage medium 120 and the processor 130 may be located in the big data analysis server 100 and separately provided. However, it should be understood that the machine-readable storage medium 120 may also be separate from the big data analytics server 100 and may be accessed by the processor 130 through a bus interface. Alternatively, the machine-readable storage medium 120 may be integrated into the processor 130, e.g., may be a cache and/or general purpose registers.

Since the big data analysis server 100 provided in the embodiment of the present invention is another implementation form of the above method embodiment, and the big data analysis server 100 can be used to execute each method step provided in the above method embodiment, the technical effect obtained by the big data analysis server can refer to the above method embodiment, and is not described herein again.

Those skilled in the art will appreciate that embodiments of the present application may provide methods, systems, or computer program products in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

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

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

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

In summary, the payment big data analysis method and the payment big data analysis system based on internet finance provided by the embodiments of the present invention can implement recommendation of payment services for a target payment user based on a clustering analysis and mining method, and can improve the user experience when the user uses an internet finance platform. Meanwhile, the payment services provided by the financial platform are accurately pushed to the user through a clustered data mining method, so that the utilization rate of each payment service of the platform can be improved, the user flow of the platform is favorably improved, and the financial service quality is improved.

Further, according to the user portrait characteristics corresponding to the target payment user, the payment user whose matching degree with the target payment user meets the first matching condition is obtained in the payment user cluster corresponding to the target payment user and serves as an extended payment user corresponding to the target payment user, then the payment service operated by the target payment user and the payment service operated by the extended payment user serve as the target payment service and serve as data support recommended by the payment service, so that the target payment service sequence not only can include the payment service operated by the extended payment user, but also can include the extended payment service corresponding to the target payment service. Therefore, even if the number of the payment services operated by the payment user is too small, the number of the payment services in the target payment service sequence can be increased through the expanded payment services corresponding to the target payment services, and then the recommendation calculation of the payment services is carried out on the target payment user, so that the accuracy and the recommendation effect of the payment service recommendation can be improved. Meanwhile, the expanded payment users are payment users with similar characteristics to the target payment users, and then the expanded payment users are searched in the payment user cluster where the target payment users are located, so that the index range can be narrowed, the related calculated amount is reduced, and the operation efficiency is improved.

The embodiments described above are only a part of the embodiments of the present invention, and not all of them. The components of embodiments of the present invention generally described and illustrated in the figures can be arranged and designed in a wide variety of different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the present invention, but is merely representative of selected embodiments of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims. Furthermore, many other embodiments that can be made by one skilled in the art based on the embodiments of the invention without inventive step should fall within the scope of protection of the invention.

Claims

1. An internet finance-based cluster analysis method, the method comprising:

calculating a first feature difference between a user portrait feature corresponding to each payment user in a pre-collected payment user sample set and a plurality of first clustering feature representations according to the preset plurality of first clustering feature representations;

determining a matching relation between each user portrait feature and each first cluster feature representation according to the first feature difference, and classifying payment users corresponding to the user portrait features matched with the same first cluster feature representation into the same payment user cluster;

calculating a second feature difference between a service portrait feature corresponding to each payment service in a pre-collected payment service sample set and a plurality of second clustering feature representations according to the preset second clustering feature representations;

and determining the matching relation between the service portrait characteristics and the second clustering characteristic representations according to the second characteristic difference, and classifying the payment services corresponding to the service portrait characteristics which are matched with the same second clustering characteristic representation into the same payment service cluster.

2. The method of claim 1, further comprising:

establishing service heat distribution corresponding to a target payment user according to payment services operated by the target payment user within a preset time period, wherein the service heat distribution comprises an operation heat value corresponding to the payment services operated by the target payment user;

calculating according to a preset time heat relation function and the service heat distribution to obtain a heat characteristic distribution set corresponding to a payment service operated by the target payment user, and calculating to obtain a user portrait characteristic corresponding to the target payment user;

acquiring operation heat values of all payment services operated by the target payment user in the preset time period, and selecting a plurality of payment services as the payment services to be selected according to the operation heat values;

and acquiring a service tag matched with the payment service to be selected from a payment service tag set corresponding to the pre-collected payment service sample set according to the payment service to be selected, and acquiring a service portrait characteristic corresponding to a target payment service according to the service tag matched with the payment service to be selected.

3. The method of claim 2, further comprising:

according to the user portrait characteristics, acquiring payment users, of which the matching degree with the target payment user meets a first matching condition, in a payment user cluster corresponding to the target payment user as extended payment users corresponding to the target payment user;

according to the service portrait characteristics, obtaining a payment service of which the matching degree with the target payment service meets a second matching condition in a payment service cluster corresponding to the target payment service, taking the payment service as an extended payment service corresponding to the target payment service, and adding the extended payment service into the target payment service sequence;

4. The method as claimed in claim 3, wherein the obtaining, according to the user portrait features corresponding to the target payment user, the payment user whose matching degree with the target payment user satisfies a first matching condition in the payment user cluster corresponding to the target payment user as the extended payment user corresponding to the target payment user includes:

5. The method as claimed in claim 3, wherein the obtaining, according to the service representation feature corresponding to the target payment service, a payment service whose matching degree with the target payment service satisfies a second matching condition in a payment service cluster corresponding to the target payment service as an extended payment service corresponding to the target payment service includes:

6. The method of claim 3, wherein the calculating a matching probability value between the target payment user and each payment service in the sequence of target payment services and selecting a recommended payment service corresponding to the target payment user in the sequence of target payment services according to the matching probability value comprises:

7. The method of claim 6, wherein the calculating a matching probability value between the target payment user and each of the payment services to be recommended comprises:

8. A big data analytics server, comprising a machine-readable storage medium coupled to a processor, the machine-readable storage medium storing a program, instructions or code, and the processor being configured to execute the program, instructions or code to implement the method of any one of claims 1-7.