CN112699168B

CN112699168B - Service recommendation method and system based on Internet financial and big data

Info

Publication number: CN112699168B
Application number: CN202011601727.7A
Authority: CN
Inventors: 陈非
Original assignee: Oriental Fortune Information Co ltd
Current assignee: Oriental Fortune Information Co.,Ltd.
Priority date: 2020-12-30
Filing date: 2020-12-30
Publication date: 2021-09-07
Anticipated expiration: 2040-12-30
Also published as: CN112699168A; CN113590681A; CN113590680A

Abstract

The embodiment of the invention provides a service recommendation method and system based on Internet financial and big data. The first weighting coefficient which is larger than the second weighting coefficient of the second weighting characteristic is added into the first weighting characteristic, so that the recommendation display sequence of the payment service to be recommended is properly moved to a more important display position, the purpose of service recommendation is achieved, meanwhile, other important page payment services on a page are referred, and the recommendation mode is more in line with the actual service.

Description

Service recommendation method and system based on Internet financial and big data

Technical Field

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

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. For example, it is an important technical problem in the art how to mine and analyze the payment service data in the financial payment data to accurately recommend the payment service to the corresponding user.

Disclosure of Invention

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

the method comprises the steps of obtaining a pre-obtained 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, which are obtained by aiming at a large data user portrait of the target payment user;

acquiring a first weight characteristic of each payment service to be recommended 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 service recommendation server, wherein the first weight characteristic comprises a weight coefficient for determining the display priority of the payment service to be recommended, and the second weight characteristic comprises a weight coefficient for determining the display priority of the page payment service;

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

According to an implementation manner of the first aspect, displaying, on a display page of the payment service, the payment service to be recommended in the recommended payment service list 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 includes:

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;

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.

According to an implementation manner of the first aspect, 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 service recommendation server includes:

for each payment service to be recommended, acquiring 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;

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;

acquiring a second weight coefficient of the page payment service aiming at each page payment service, wherein the second weight coefficient is a weight value corresponding to the last time the page payment service is displayed;

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 of the payment services 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.

According to an implementation manner of the first aspect, 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 includes:

for 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;

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

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, including:

sequencing a first display priority parameter corresponding to each payment service to be recommended and a second display priority parameter corresponding to each page payment service from large to small, taking a sequencing number of each payment service to be recommended as a final display priority parameter of each payment service to be recommended according to a sequencing result, and taking a sequencing number of each page payment service as a final display priority parameter of each page payment service;

and 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.

According to an implementation manner of the first aspect, 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 on a display page of the payment service according to the final display priority includes:

for each payment service to be recommended, according to a service tag of the payment service to be recommended, inquiring a first target service sequence matched with the service tag from a plurality of preset service sequences, and adding the payment service to be recommended into the matched first target service sequence, wherein each service sequence comprises a plurality of payment services, and each service sequence corresponds to one display area on the display page;

after each payment service to be recommended is added to the corresponding matched first target service sequence, sequencing the payment services in each first target service sequence according to the descending order of the first display priority parameter and the second display priority parameter, and taking the sequencing number of each payment service in the first target service sequence as the final display priority parameter corresponding to each payment service;

and 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, wherein the display area 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.

detecting user operation aiming at any payment service on a display page of the payment service;

when the user operation is detected, taking the payment service operated by the user operation as a target payment service, and matching the target payment service with each payment service to be recommended in the payment service list to be recommended;

when the payment service list to be recommended has the payment service to be recommended matched with the target payment service, displaying the matched payment service to be recommended at a corresponding display position on a display page according to the matched first weight characteristic of the payment service to be recommended and the second weight characteristic of each payment service on the display page of the payment service; 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.

According to an implementation manner of the first aspect, displaying the matched payment service to be recommended at a corresponding display position on a display page according to the matched first weight feature of the payment service to be recommended and the second weight feature of each payment service on the display page of the payment service includes:

for the matched 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;

for each page payment service on the display page of the payment service, taking the product of a second weighting coefficient of the page payment service and a corresponding second weighting coefficient as the second display priority parameter;

sequencing a first display priority parameter corresponding to the matched payment service to be recommended and a second display priority parameter corresponding to each page payment service respectively according to a descending order, and taking the sequencing number of the matched payment service to be recommended as a final display priority parameter of the matched payment service to be recommended and the sequencing number of each page payment service as a final display priority parameter of each page payment service according to a sequencing result;

displaying the matched payment service to be recommended at a page position corresponding to the final display priority parameter of the matched payment service to be recommended on a display page of the payment service, 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.

according to the service tags of the target payment services, a second target service sequence matched with the service tags is inquired from a plurality of preset service sequences, and the matched payment services to be recommended are added into the matched second target service sequence, wherein each service sequence comprises a plurality of payment services, and each service sequence corresponds to one display area on a display page of the payment services;

sequencing the payment services in the second target service sequence according to the descending order of the first display priority parameter and the second display priority parameter, and taking the sequencing serial number of each payment service in the second target service sequence as a final display priority parameter corresponding to each payment service;

and 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 second target service sequence in the display area corresponding to the second target service sequence, wherein the display area 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.

In a second aspect, the present invention further provides an internet financial and big data based service recommendation system, including a service recommendation server and a plurality of payment terminals communicatively connected to the service recommendation server and corresponding to a plurality of payment users, respectively, where the service recommendation server includes a payment service recommendation device, a processor, and a machine-readable storage medium, the machine-readable storage medium is connected to the processor, and the machine-readable storage medium is used for storing a program, an instruction, or a code included in the payment service recommendation device, where the payment service recommendation device includes:

the service list acquisition module is used for acquiring a pre-obtained recommended payment service list aiming at a target payment user, and the recommended payment service list comprises a plurality of payment services to be recommended, which are obtained by aiming at a plurality of big data user figures of the target payment user;

a weight feature obtaining module, configured to obtain a first weight feature of each to-be-recommended payment service in the recommended payment service list and a second weight feature of each page payment service on a display page of the payment service provided by the service recommendation server, where the first weight feature includes a weight coefficient used to determine a display priority of the to-be-recommended payment service, and the second weight feature includes a weight coefficient used to determine a display priority of the page payment service; and

and the payment service recommendation module is used for 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 page payment service so as to realize recommendation of the payment service to be recommended.

According to an implementation manner of the second aspect, the payment service recommendation module is specifically configured to:

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

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, which specifically includes:

To sum up, compared with the prior art, the service recommendation method and system based on internet financial and big data provided by the embodiment of the invention can recommend and display the payment service to be recommended in a manner of comparing the first weight characteristics of the payment service to be recommended with the second weight characteristics corresponding to the payment services on each page, for the payment service to be recommended in the payment list of the recommendation service. 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.

And secondly, according to the target payment service operated by the user, the payment service to be recommended matched with the target payment service is searched in the recommended payment service list and is displayed on the display page, so that accurate service recommendation can be performed according to the operation of the user, and the recommendation success rate and the recommendation effect are better.

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 payment service recommendation system according to an embodiment of the present invention.

Fig. 2 is a schematic flowchart of a service recommendation method based on internet financial and big data according to an embodiment of the present invention.

Fig. 3 is a schematic step flow diagram of another implementation of step S23 shown in fig. 2.

Fig. 4 is another flowchart of a service recommendation method based on internet financial and big data according to an embodiment of the present invention.

Fig. 5 is a block schematic diagram of the service recommendation server in fig. 1.

Fig. 6 is a functional block diagram of the payment service recommendation apparatus in fig. 5.

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, in which like elements are denoted by like reference numerals. 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 foregoing background, embodiments of the present invention innovatively provide a service recommendation method and system based on internet financial and big data, 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 payment service recommendation system according to an embodiment of the present invention. The payment service recommendation system may include a service recommendation server 100 and a plurality of payment terminals 200. The service recommendation 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 service recommendation, which is independent from the server. The payment terminal 200 may be a personal computer, a smart phone, an intelligent wearable device, or the like. Various payment users may use various payment services provided by the payment terminal 200 using the internet financial platform, such as any payment-related service of purchasing e-commerce products, purchasing insurance, applying for loan, purchasing stocks, fund, online payment, service recharging, etc., to be considered as the payment service according to the embodiment of the present invention. The service recommendation server 100 may be respectively connected to each payment terminal 200 through a network, each payment terminal 200 may implement a corresponding payment service under the operation of a relevant user, and send the generated corresponding payment data to the service recommendation server 100, and the service recommendation 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 diagram of the service recommendation method based on internet financial and big data according to the embodiment of the present invention is shown. In this embodiment, the method is executed by the service recommendation server 100. The detailed steps of the method will be described and explained in detail below with reference to the accompanying drawings.

Step S21, obtaining a pre-obtained recommended payment service list aiming at the target payment user, wherein the recommended payment service list comprises a plurality of payment services to be recommended obtained aiming at a plurality of big data user figures of the target payment user.

Step S22, obtaining a first weight characteristic of each payment service to be recommended in the recommended payment service list, and a second weight characteristic of each page payment service on the display page of the payment service provided by the service recommendation server 100. 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 S23, 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 the present embodiment, in the step S21, the recommended payment service list may be obtained in advance by any feasible means, for example, by a manual setting by a user, or by analyzing big data. For example, a method obtained in such a manner that analysis is performed on big data can be described as follows.

For example, first, a payment user cluster obtained by performing cluster analysis on a large data 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 are obtained. Then, according to the user portrait characteristics corresponding to the target payment user, the payment users, of which the matching degree with the target payment user meets a first matching condition, are obtained from the payment user cluster corresponding to the target payment user and serve as the extended payment users corresponding to the target payment user. And then 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 as target payment services into a target payment service sequence, acquiring the 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 services, and adding the payment service as the expanded payment service corresponding to the target payment service into the target payment service sequence. And finally, 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 to obtain the recommended service payment list.

The expanded payment user can be determined by the following method:

firstly, determining payment users except for a 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;

then, 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 finally, 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 the first matching condition, and using the alternative payment users as the extended payment users corresponding to the target payment users.

Further, the extended payment service corresponding to the target payment service can be obtained through the following method:

firstly, 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;

then, 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 finally, 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.

Further, the above 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, a specific implementation manner is exemplarily described as follows:

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

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

and finally, 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.

Further, the above implementation manner of calculating the matching probability value between the target payment user and each payment service to be recommended is exemplarily described as follows:

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;

and 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. For example, a ratio of the first operation times corresponding to each payment service to the first total times 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 probability value is obtained by multiplying a first percentage corresponding to each payment service by a first weight, a second probability value is obtained by multiplying a second percentage by a second weight, and then the sum of the first probability value and the second probability value is used as the matching probability value of the target payment user and the payment service.

In detail, in this embodiment, in step S22, 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 S23, 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. It can be understood that, if the number of the payment services that can be displayed on the page is less than the sum of the number of the recommended payment services and the number of the payment services on the page, the payment services to be recommended and the page payment services are sequentially displayed on the page according to the sorting result in the order from small to large according to the sorting sequence number, and the payment services to be recommended with a large sorting sequence number may not be displayed or hidden temporarily due to the limitation of the page space, which is not specifically limited in this embodiment.

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, 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.

Alternatively, referring to fig. 3, in the step S23, 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 another alternative specific implementation scheme is exemplarily described as follows.

And step S31, detecting the user operation aiming at any payment service on the display page of the payment service.

Step S32, when the user operation is detected, taking the payment service operated by the user operation as a target payment service, and matching the target payment service with each payment service to be recommended in the list of payment services to be recommended. In this embodiment, the user operation may be any one of a single click, a double click, a service interface opening, a payment service completion, and the like for the target payment service, and is not limited specifically.

Step S33, when the payment service list to be recommended has the payment service to be recommended that matches the target payment service, displaying the matched payment service to be recommended at a corresponding display position on the display page according to the first weight characteristic of the matched payment service to be recommended and the second weight characteristic of each payment service on the display page of the payment service. In this embodiment, the display page of the payment service includes a plurality of display positions, and each display position has a position identifier corresponding to a final display priority parameter of one payment service.

In this embodiment, the time for displaying the matched payment service to be recommended may be after the operation of the user on the target payment service is completed or when a service interface corresponding to the target payment service is closed, and is not specifically limited herein. In addition, the payment service to be recommended that is matched with the target payment service may refer to a payment service to be recommended that is the same as the target payment service in service type, belongs to the same service sequence, or is the same as the target user group, and is not specifically limited herein.

Further, in the step S33, the matched payment service to be recommended is displayed at the corresponding display position on the display page according to the matched first weight characteristic of the payment service to be recommended and the second weight characteristic of each payment service on the display page of the payment service, and an alternative implementation manner may be described as follows.

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

Then, for each page payment service on the display page of the 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.

Then, on the basis, the first display priority parameter corresponding to the matched payment service to be recommended and the second display priority parameter corresponding to each page payment service can be ranked from large to small, the ranking number of the matched payment service to be recommended is used as the final display priority parameter of the matched payment service to be recommended according to the ranking result, and the ranking number of each page payment service is used as the final display priority parameter of each page payment service.

And finally, displaying the matched payment service to be recommended at a page position corresponding to the final display priority parameter of the matched payment service to be recommended on a display page of the payment service, and displaying each page payment service at a display position corresponding to the final display priority parameter of each page payment service. 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.

Alternatively, in the above step of displaying the matched payment service to be recommended at the corresponding display position on the display page according to the matched first weight characteristic of the payment service to be recommended and the second weight characteristic of each payment service on the display page of the payment service, another alternative implementation scheme may be exemplarily described as follows.

Firstly, according to the service label of the target payment service, a second target service sequence matched with the service label is inquired from a plurality of preset service sequences, and the matched payment service to be recommended is added into the matched second target service sequence. In this embodiment, each service sequence includes a plurality of payment services, and each service sequence corresponds to one display area on a display page of the payment services. It should be understood that, after the matched payment service to be recommended is added to the corresponding matched second target service sequence, the payment services in the second target service sequence include the added payment service to be recommended and the payment services existing in the second target service sequence.

Then, ordering the payment services in the second target service sequence according to the descending order of the first display priority parameter and the second display priority parameter, and taking the ordering sequence number of each payment service in the second target service sequence as a final display priority parameter corresponding to each payment service.

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 second target service sequence in the display area corresponding to the second 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 matched payment service to be recommended is a1, the corresponding first weighting coefficient is a1, and the corresponding first weighting coefficient is x 1. The second target service sequence added with a1 has four page payment services of C1, C2, C3 and C4 before, the corresponding second weighting coefficients are C1, C2, C3 and C4, and the corresponding second weighting coefficients are z1, z2, z3 and z 4. And calculating the arrangement sequence of the final display priority parameters corresponding to the payment services in the second target service sequence to be { c1 × z1, a1 × x1, c2 × z2, c3 × z3 and c4 × z4 }. Then, finally, in the display area corresponding to the second target service sequence, the final display order of each payment service is: c1, A1, C2, C3 and C4.

In summary, referring to the method shown in fig. 3, the payment service to be recommended that is matched with the target payment service can be found in the recommended payment service list according to the target payment service operated by the user and displayed on the display page, so that accurate service recommendation can be performed according to the operation of the user, and the recommendation success rate and the recommendation effect are better.

It should be noted that the service recommendation method shown in fig. 3 can also be regarded as a complete service recommendation method provided in another embodiment independently from step S23 in fig. 2. For example, based on a complete service recommendation method mainly shown in fig. 3, reference may be made to the flow of steps shown in fig. 4, which is exemplarily described as follows.

Step S41, obtaining a pre-obtained recommended payment service list aiming at the target payment user, wherein the recommended payment service list comprises a plurality of payment services to be recommended obtained aiming at the user portrait of the target payment user.

Step S42, detecting a user operation for any payment service on the display page of the payment service provided by the service recommendation server.

Step S43, when the user operation is detected, taking the payment service operated by the user operation as a target payment service, and matching the target payment service with each payment service to be recommended in the list of payment services to be recommended.

Step S44, when the payment service to be recommended matching the target payment service is in the payment service list to be recommended, displaying the matching payment service to be recommended on a display page of the payment service, so as to implement recommendation of the payment service to be recommended.

It should be understood that the specific implementation of the steps S41-S44 may refer to the same contents of the same or similar steps in fig. 2 and fig. 3, for example, the specific implementation may also be implemented by comparing the first weight characteristic and the second weight characteristic according to the embodiment corresponding to the above fig. 2 and fig. 3, and will not be described herein again. For example, in step S44, the payment service to be recommended that matches the payment service to be recommended and the first weight feature and the second weight feature that correspond to each page payment service on the display page may be obtained respectively to perform specific payment service recommendation on the matched payment service to be recommended. For the method for recommending payment services according to the first weight characteristic and the second weight characteristic, reference may be made to the associated contents of the specific embodiment shown in fig. 2 and fig. 3, and the associated contents are incorporated into the implementation manner corresponding to fig. 4, and the details of the related contents are not repeated here.

Further, referring to fig. 5, fig. 5 is a block diagram illustrating a service recommendation server 100 according to an embodiment of the present invention. In this embodiment, the service recommendation server 100 may include a payment service recommendation device 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 payment service recommendation apparatus, for example, instructions or codes corresponding to various software functional modules included in the payment service recommendation apparatus 110. The process 130 is configured to execute the program, instructions or codes stored in the machine-readable storage medium 120 to implement the above-mentioned internet financial and big data based service recommendation method.

In this embodiment, referring to fig. 6, the payment service recommendation apparatus 110 may include a service list obtaining module 111, a weight characteristic obtaining module 112, and a payment service recommendation module 113. The detailed description about each functional module described above is as follows.

The service list obtaining module 111 obtains a pre-obtained recommended payment service list for the target payment user, where the recommended payment service list includes a plurality of payment services to be recommended obtained by representing a plurality of big data users of the target payment user.

A weight feature obtaining module 112, configured to obtain a first weight feature of each to-be-recommended payment service in the recommended payment service list, and a second weight feature of each page payment service on a display page of the payment service provided by the service recommendation server, where the first weight feature includes a weight coefficient used for determining a display priority of the to-be-recommended payment service, and the second weight feature includes a weight coefficient used for determining a display priority of the page payment service.

And the payment service recommendation module 113 is configured to display 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 page payment service, so as to implement recommendation of the payment service to be recommended.

In detail, the weight feature obtaining module 112 is specifically configured to:

and 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. 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 reduced in sequence according to the arrangement sequence of the payment services to be recommended.

The payment service recommendation module 113 is specifically configured to:

Specifically, the payment service recommendation module 113 may rank, in descending order, a first display priority parameter corresponding to each payment service to be recommended and a second display priority parameter corresponding to each page payment service, according to a ranking result, using a ranking number of each payment service to be recommended as a final display priority parameter of each payment service to be recommended and using a ranking number of each page payment service as a 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. 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.

The payment service recommendation module 113 may be further configured to:

It should be noted that the service list obtaining module 111 may be configured to implement the step S21, and for more details of the service list obtaining module 111, reference may be made to the description related to the step S21, which is not described herein again. The weighting feature obtaining module 112 may be configured to implement the step S22, and for further details of the weighting feature obtaining module 112, reference may be made to the description related to the step S221, and details are not repeated here. The payment service recommending module 113 may be configured to implement the step S23, and for more details of the payment service recommending module 113, reference may be made to the description related to the step S23, which is not described herein again.

In this embodiment, the machine-readable storage medium 120 and the processor 130 may be located in the service recommendation server 100 and separately provided. However, it should be understood that the machine-readable storage medium 120 may also be separate from the service recommendation 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 service recommendation server 100 provided in the embodiment of the present invention is another implementation form of the above method embodiment, and the service recommendation server 100 can be used to execute each method step provided in the above method embodiment, the technical effect obtained by the service recommendation server may refer to the above method embodiment, and is not described herein again.

In summary, the service recommendation method and system based on internet financial and big data provided by the embodiments of the present invention can recommend and display the payment service to be recommended in a manner of comparing the first weight characteristics of the payment service to be recommended with the second weight characteristics corresponding to the payment services on each page in the recommendation service payment list for the payment service to be recommended in the recommendation service payment list. 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.

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.

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. A service recommendation method based on Internet financial and big data is applied to a service recommendation server, and the method comprises the following steps:

the method comprises the steps of obtaining a pre-obtained 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, which are obtained aiming at a big data user portrait of the target payment user;

displaying the payment services to be recommended in the recommended payment service list on a display page of the payment services according to the first weight characteristic of each payment service to be recommended and the second weight characteristic of each page payment service, so as to realize the recommendation of the payment services to be recommended;

the obtaining of the first weight characteristic of each payment service to be recommended in the recommended payment service list and the second weight characteristic of each page payment service on the display page of the payment service provided by the service recommendation server includes:

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 respectively are sequentially reduced according to the arrangement sequence of the payment services to be recommended;

the 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 page payment service includes:

2. The method of claim 1, wherein 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 comprises:

3. The method according to claim 1, wherein 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 on a display page of the payment service according to the final display priority comprises:

4. The method of claim 1, wherein displaying the to-be-recommended payment services in the recommended payment service list on the display page of the payment services according to the first weight characteristic of each to-be-recommended payment service and the second weight characteristic of each to-be-recommended payment service comprises:

5. The method of claim 4, wherein displaying the matched payment service to be recommended at a corresponding display position on the display page according to the matched first weight characteristic of the payment service to be recommended and the second weight characteristic of each payment service on the display page of the payment service comprises:

6. The method of claim 4, wherein displaying the matched payment service to be recommended at a corresponding display position on the display page according to the matched first weight characteristic of the payment service to be recommended and the second weight characteristic of each payment service on the display page of the payment service comprises:

7. A service recommendation system based on Internet financial and big data is characterized by comprising a service recommendation server and a plurality of payment terminals which are in communication connection with the service recommendation server and respectively correspond to a plurality of payment users, wherein the service recommendation server comprises a payment service recommendation device, a processor and a machine-readable storage medium, the machine-readable storage medium is connected with the processor, and the machine-readable storage medium is used for storing programs, instructions or codes included by the payment service recommendation device, and the payment service recommendation device comprises:

the payment service recommendation module is used for displaying the payment services to be recommended in the recommended payment service list on a display page of the payment services according to the first weight characteristic of each payment service to be recommended and the second weight characteristic of each page payment service so as to realize recommendation of the payment services to be recommended;

the weight feature obtaining module is specifically configured to:

the payment service recommendation module is specifically configured to:

and 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.

8. The Internet financing and big data based service recommendation system as claimed in claim 7,

the payment service recommendation module is specifically further configured to: