CN113191696A - Merchant recommendation method and device - Google Patents
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Abstract
The invention provides a merchant recommendation method and device, and relates to a big data technology, wherein the method comprises the following steps: collecting the click quantity and the transaction quantity of a merchant by a customer; determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant; collecting total click quantity and credit score of a merchant; determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant; determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant; and determining a merchant recommendation list according to the recommendation value of the merchant to the customer. The method comprises the steps of comprehensively considering dynamic information such as the click rate of a customer, transaction behaviors and the click rate of merchants, calculating the recommendation value of the merchants to the customer, sorting the merchants meeting the conditions from high to low according to the recommendation value, and preferentially returning the merchants used by the customer before or the merchants with high click rate to the mobile phone bank customer to realize accurate marketing of the merchants to the customer.
Description
Technical Field
The invention relates to the technical field of computer data processing, in particular to a merchant recommendation method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
The information of the mobile phone bank commercial tenants is configured through the background system to form a commercial tenant resource pool.
The number of merchants is large, and when the mobile banking requests the information of the merchants, the background system returns the information of the merchants meeting the conditions to the mobile banking user after filtering according to the version number of the mobile banking, the white list guest group and other conditions.
The current merchant filtering scheme does not consider dynamic information such as clicking and transaction behaviors of customers, the clicking amount of merchants and the like; and the returned merchant information is not ranked, resulting in the returned merchant possibly not being of interest to the user.
Therefore, how to provide a new solution, which can solve the above technical problems, is a technical problem to be solved in the art.
Disclosure of Invention
The embodiment of the invention provides a merchant recommending method, which comprehensively considers dynamic information such as click rate, transaction behavior and merchant click rate of a customer, calculates a recommending value of a merchant for the customer, sorts merchants meeting conditions from high to low according to the recommending value, and returns merchants used by the customer before or merchants with high click rate to a mobile banking customer preferentially to realize accurate marketing of the merchant for the customer, and comprises the following steps:
collecting the click quantity and the transaction quantity of a merchant by a customer;
determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant;
collecting total click quantity and credit score of a merchant;
determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant;
determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant;
and determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
An embodiment of the present invention further provides a merchant recommending apparatus, including:
the system comprises a click rate and transaction amount acquisition module, a transaction amount acquisition module and a data processing module, wherein the click rate and transaction amount acquisition module is used for acquiring the click rate and transaction amount of a merchant by a customer;
the conversion rate determining module of the customer to the commercial tenant is used for determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant;
the merchant total click rate and credit score acquisition module is used for acquiring the merchant total click rate and credit score;
the recommendation weight determination module of the commercial tenant is used for determining the recommendation weight of the commercial tenant according to the total click quantity and the credit score of the commercial tenant;
the merchant-to-customer recommendation value determination module is used for determining a merchant-to-customer recommendation value according to the merchant conversion rate of the customer and the merchant recommendation weight;
and the merchant recommendation list determining module is used for determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the merchant recommendation method.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for executing the merchant recommendation method is stored in the computer-readable storage medium.
The embodiment of the invention provides a merchant recommendation method and a merchant recommendation device, which comprise the following steps: firstly, acquiring the click quantity and the transaction quantity of a merchant by a customer; then determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant; then, acquiring the total click quantity and credit score of the merchant; continuously determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant; next, determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant; and finally, determining a merchant recommendation list according to the recommendation value of the merchant to the customer. The invention provides a method for filtering merchant information by comprehensively considering dynamic information such as click rate, transaction behavior and merchant click rate of a customer on the basis of an original merchant information filtering scheme using a mobile phone bank version number and white list customer group filtering, calculating a recommendation value of a merchant to the customer, sorting merchants meeting conditions from high to low according to the recommendation value, and preferentially returning the merchants used by the customer before or the merchants with high click rate to the mobile phone bank customer so as to realize accurate marketing of the merchant to the customer.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic diagram of a merchant recommendation method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a process of determining a recommendation weight of a merchant according to the merchant recommendation method in the embodiment of the present invention.
Fig. 3 is a schematic diagram of a process of determining a recommendation value of a merchant to a customer according to a merchant recommendation method in an embodiment of the present invention.
Fig. 4 is a schematic diagram of a process of displaying recommended merchants by a mobile banking APP of a merchant recommendation method according to an embodiment of the present invention.
Fig. 5 is a schematic diagram of a computer device for operating a merchant recommendation method implemented by the present invention.
Fig. 6 is a schematic diagram of a merchant recommending apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
The invention relates to big data technology. Fig. 1 is a schematic diagram of a merchant recommendation method according to an embodiment of the present invention, and as shown in fig. 1, an embodiment of the present invention provides a merchant recommendation method, which calculates a recommendation value of a merchant to a customer by comprehensively considering dynamic information such as a click rate, a transaction behavior, and a merchant click rate of a customer, sorts merchants meeting a condition from high to low according to the recommendation value, and returns merchants used by the customer before or merchants with a high click rate to a mobile banking customer preferentially, so as to implement accurate marketing of the merchant to the customer, where the method includes:
step 101: collecting the click quantity and the transaction quantity of a merchant by a customer;
step 102: determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant;
step 103: collecting total click quantity and credit score of a merchant;
step 104: determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant;
step 105: determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant;
step 106: and determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
The merchant recommendation method provided by the embodiment of the invention comprises the following steps: firstly, acquiring the click quantity and the transaction quantity of a merchant by a customer; then determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant; then, acquiring the total click quantity and credit score of the merchant; continuously determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant; next, determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant; and finally, determining a merchant recommendation list according to the recommendation value of the merchant to the customer. The invention provides a method for filtering merchant information by comprehensively considering dynamic information such as click rate, transaction behavior and merchant click rate of a customer on the basis of an original merchant information filtering scheme using a mobile phone bank version number and white list customer group filtering, calculating a recommendation value of a merchant to the customer, sorting merchants meeting conditions from high to low according to the recommendation value, and preferentially returning the merchants used by the customer before or the merchants with high click rate to the mobile phone bank customer so as to realize accurate marketing of the merchant to the customer.
When the merchant recommendation method provided by the embodiment of the present invention is implemented specifically, in an embodiment, the method may include:
collecting the click quantity and the transaction quantity of a merchant by a customer;
determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant;
collecting total click quantity and credit score of a merchant;
determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant;
determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant;
and determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
The embodiment of the invention mainly carries out data interaction between the background system and the mobile phone bank APP.
When the merchant recommendation method provided by the embodiment of the present invention is implemented specifically, in an embodiment, the collecting click rate and transaction amount of the merchant by the customer includes:
developing a data acquisition program, embedding the data acquisition program into the mobile phone bank APP, acquiring the click rate and the transaction amount of each merchant of a customer, uploading the click rate and the transaction amount to a background system in real time, and storing data uploaded by the mobile phone bank APP to a database through the background system.
In the embodiment, in order to collect the click rate and the transaction amount of the customer to the merchant, a data collection program is developed, a mobile phone bank APP is embedded, and the click rate C (C) of the customer to the merchant is collected1,C2，...,Ci...,Cn) Customer-to-merchant transaction amount T (T)1,T2，...,Ti...,Tn) And uploading the data to a background system in real time, and storing the data uploaded by the APP of the mobile phone bank to a database through the background system.
The background system extracts the click rate and the transaction amount of the customer to the commercial tenant through the database, and the conversion rate of the customer to the commercial tenant is obtained through calculation.
When the merchant recommendation method provided by the embodiment of the present invention is implemented specifically, in an embodiment, the conversion rate of the customer to the merchant is determined according to the following manner:
Fi＝Ti/Ci
wherein, FiConversion rate for customer to ith merchant, conversion rate F includes (F)1,F2，...,Fi...,Fn) N is the number of merchants; t isiTransaction amount for the customer to the ith merchant, transaction amount T comprising (T)1,T2，...,Ti...,Tn) N is the number of merchants; ciThe click rate C of the ith merchant for the customer comprises (C)1,C2，...,Ci...,Cn) And n is the number of merchants.
While the foregoing expressions for determining customer-to-merchant conversion are provided by way of example, those skilled in the art will appreciate that the above equations may be modified and other parameters or data may be added as needed, or other specific equations may be provided, and such modifications are intended to fall within the scope of the present invention.
In the embodiment, the click rate C (C) of the customer to each merchant is collected and recorded through the above1,C2，...,Ci...,Cn) Transaction amount T (T)1,T2，...,Ti...,Tn) Calculating the conversion rate F (F) of the customer to the merchant i according to the click quantity and the transaction quantity data1,F2，...,Fi...,Fn) In which F isi＝Ti/CiAnd n is the number of merchants. The greater the conversion, the more frequent the customer uses the merchant.
When the merchant recommendation method provided by the embodiment of the present invention is specifically implemented, in an embodiment, the acquiring of the total click rate and the credit score of the merchant includes:
the method comprises the steps of utilizing a data acquisition program embedded into an APP of the mobile phone bank to acquire the total click quantity and credit score of each merchant, uploading the total click quantity and credit score to a background system in real time, and storing data uploaded by the APP of the mobile phone bank to a database through the background system.
In the embodiment, when the total click quantity and the credit score of the merchants are collected, a data collection program which is developed and embedded into a mobile phone bank APP is used, and the total click quantity M (M) of each merchant is collected by the data collection program embedded into the mobile phone bank APP1,M2，...,Mi...,Mn) Credit rating for each merchant S (S)1,S2，...,Si...,Sn) Wherein M isi∈[Mmin,Mmax]，Si∈[Smin,Smax]And uploading the data to a background system in real time, and storing the data uploaded by the APP of the mobile phone bank to a database through the background system.
Fig. 2 is a schematic diagram of a process of determining a recommendation weight of a merchant according to a merchant recommendation method in an embodiment of the present invention, and as shown in fig. 2, when a merchant recommendation method provided in an embodiment of the present invention is implemented specifically, in an embodiment, determining a recommendation weight of a merchant according to a total click rate and a credit score of the merchant includes:
step 201: connecting a background system with a database, and extracting the total click rate and credit score of the merchant;
step 202: normalizing the total click rate and the credit score of the merchants, regularly acquiring the click rate of the merchant with the maximum click rate from all the merchants according to preset time, and regularly acquiring the credit score of the merchant with the highest credit score from all the merchants according to the preset time;
step 203: determining the click rate weight of each merchant according to the click rate of the merchant with the maximum click rate and by combining the click rate of each merchant;
step 204: determining credit scoring weight of each merchant according to the credit score of the merchant with the highest credit score and by combining the credit score of each merchant;
step 205: and respectively adding the click weight of each merchant and the credit scoring weight of each merchant to determine the recommendation weight of each merchant, and storing the recommendation weight in the database.
In an embodiment, the main process of calculating the recommendation weight of the merchant includes:
firstly, the background system is connected with a database to extract the total click rate M (M) of the commercial tenant1,M2，...,Mi...,Mn) And merchant credit score S (S)1,S2，...,Si...,Sn) (ii) a Wherein M isi∈[Mmin,Mmax]，Si∈[Smin,Smax]；
Then, the total click rate and the credit score of the merchants are normalized, and the click rate M of the merchant with the largest click rate in all merchants is periodically obtained according to the preset time lengthmaxRegularly acquiring credit score S of the merchant with the highest credit score from all merchants according to preset time lengthmax；
Then according to the click rate of the merchant with the maximum click rate, determining the click rate weight of each merchant by combining the click rate of each merchant; for example, merchant i has a click rate weight of Mi/Mmax。
Next, according to the credit score of the merchant with the highest credit score, determining the credit score weight of each merchant by combining the credit score of each merchant; for example, merchant i has a credit score weight of Si/Smax。
Finally, respectively weighting the click rate of each merchant by Mi/MmaxAnd each merchant credit scoring weight Si/SmaxAdding to determine the recommended weight R (R) of each merchant1,R2，...,Ri...,Rn) And storing the data in a database.
In one embodiment, the recommended weight for each merchant is determined as follows:
Ri＝Mi/Mmax+Si/Smax
wherein R isiA recommendation weight for the ith merchant, the recommendation weight R comprising (R)1,R2，...,Ri...,Rn) N is the number of merchants; mi/MmaxThe click rate weight of each merchant; si/SmaxCredit for each merchantScoring the weight;
the aforementioned expressions for determining the recommended weight of each business are only used for illustration, and those skilled in the art will understand that the above formulas may be modified in some forms and other parameters or data may be added as needed, or other specific formulas may be provided, and these modifications are all within the scope of the present invention.
Fig. 3 is a schematic diagram of a process of determining a recommended value of a merchant to a customer in a merchant recommendation method according to an embodiment of the present invention, and as shown in fig. 3, when a merchant recommendation method provided in an embodiment of the present invention is specifically implemented, in an embodiment, determining a recommended value of a merchant to a customer according to a conversion rate of a customer to a merchant and a recommendation weight of a merchant includes:
step 301: extracting the conversion rate of the customer to the commercial tenant and the recommendation weight of the commercial tenant through a background system;
step 302: and multiplying the conversion rate of the customer to the commercial tenant with the recommendation weight of the commercial tenant, and calculating to obtain the recommendation value of each commercial tenant to the customer.
In an embodiment, the main process of calculating the recommendation value of the merchant to the customer includes: firstly, the conversion rate F of the customer to the commercial tenant is extracted through a background systemiAnd a merchant's recommendation weight Ri(ii) a Then multiplying the conversion rate of the customer to the commercial tenant with the recommendation weight of the commercial tenant, and calculating to obtain a recommendation value F of each commercial tenant to the customeri*RiWhere denotes multiplication. The larger the recommended weight, the more likely the representative merchant is to be a trending merchant; the recommendation value of the merchant to the customer is in direct proportion to the conversion rate and the recommendation weight.
When the merchant recommendation method provided by the embodiment of the present invention is implemented specifically, in an embodiment, determining a merchant recommendation list according to a recommendation value of a merchant to a customer includes:
and sequencing the recommendation values of each merchant to the customer in the order from high to low to determine a merchant recommendation list.
In the embodiment, the recommended values of all merchants to the current customers are calculated, and after sorting is performed from high to low, the merchant information meeting the conditions is returned to the mobile banking customer.
Fig. 4 is a schematic diagram of a process of displaying and recommending merchants by a mobile banking APP of a merchant recommendation method according to an embodiment of the present invention, and as shown in fig. 4, when the merchant recommendation method according to the embodiment of the present invention is specifically implemented, in an embodiment, the method further includes:
step 401: returning the merchant recommendation list to the mobile phone bank APP through the background system;
step 402: and loading the merchant recommendation list through the APP of the mobile phone bank, and displaying the merchants according to the recommendation value sequence.
In the embodiment, in order to display the merchant recommendation list returned value of the mobile banking APP, the main process includes: firstly, returning a merchant recommendation list to a mobile phone bank APP through a background system;
and then loading a merchant recommendation list through the APP of the mobile phone bank, and displaying the merchants according to the recommendation value sequence. And the mobile phone bank compiles codes, loads the merchant information returned by the background system and displays the merchant information to the customer.
According to the embodiment of the invention, the conversion rate F of the customer to all the merchants is calculated by collecting and recording the click rate and transaction amount data of the customer to each merchant. And collecting and recording the total click rate and the credit score of each merchant, and carrying out normalization processing to obtain the click rate weight and the credit score weight of each merchant. The two are added as the recommendation weight W of the merchant. And the product of the conversion rate F of the customer to the merchant and the merchant weight W is used as a recommended value of the merchant to the customer, and the recommended value is sorted from top to bottom and then returned to the customer.
The merchant recommendation method provided by the embodiment of the invention is briefly described below with reference to specific scenarios:
the invention provides a method for calculating the recommendation value of a merchant to a customer by comprehensively considering dynamic information such as the click and transaction behaviors of the customer, the click amount of the merchant and the like on the basis of the original merchant information filtering scheme, and returning the merchant meeting the conditions to a mobile phone bank customer after sorting the merchants from high to low according to the recommendation value, thereby realizing the accurate marketing of the merchant to the customer.
The main process for implementing the embodiment of the invention is as follows: and developing a data acquisition program, embedding the data acquisition program into an APP (application) of the mobile phone bank, and acquiring the click quantity of the customer to the merchant, the transaction quantity, the click quantity of the merchant and the merchant grading data.
The background system stores the data through a database, and writes codes to calculate the recommendation value of each merchant to the customer, and the specific process is as follows:
1) calculating the conversion rate of the customer to the merchant: collecting and recording the click rate C (C) of the customer to each merchant1,C2，...,Ci...,Cn) Transaction amount T (T)1,T2，...,Ti...,Tn) Calculating the conversion rate F (F) of the customer to the merchant i according to the click quantity and the transaction quantity data1,F2，...,Fi...,Fn) In which F isi＝Ti/CiAnd n is the number of merchants. The greater the conversion, the more frequent the customer uses the merchant.
2) Acquiring click quantity and credit scoring information of merchants: collecting and recording total click rate M (M) of each merchant1,M2，...,Mi...,Mn) Credit score S (S)1,S2，...,Si...,Sn) In which S isi∈[Smin,Smax]。
3) Calculating the click rate weight and the credit scoring weight of the merchant:
and carrying out normalization processing on the total click quantity of the commercial tenant and the customer score to obtain the click quantity weight and the credit score weight of the commercial tenant. The click rate of the merchant with the maximum click rate in all merchants is regularly obtained and recorded as MmaxIf the click rate weight of the merchant i is Mi/MmaxThe credit score has a weight of Si/Smax。
4) Calculating the recommended value of the merchant to the customer: the recommendation weight of merchant i to customer is defined as R (R)1,R2，...,Ri...,Rn) Wherein R isi＝Mi/Mmax+Si/SmaxRi, the product of the conversion rate of the customer to the merchant i and the recommendation weight of the merchant i to the customer is recorded as a recommendation value F of the merchant i to the customeri*Ri. The larger the recommendation weight is, the larger the representationThe more likely the merchant is a trending merchant; the recommendation value of the merchant to the customer is in direct proportion to the conversion rate and the recommendation weight.
5) And returning a result: and calculating the recommended values of all the merchants to the current customers, sorting from high to low, and returning the merchant information meeting the conditions to the mobile banking customers.
The embodiment of the invention also provides a modular example of the merchant recommendation method, which comprises the following steps:
the system comprises a data acquisition module, a data storage module, an algorithm realization module and a merchant display module:
1) a data acquisition module: and compiling a data acquisition program, embedding the data acquisition program into a mobile phone bank APP, and acquiring click and transaction data of a mobile phone bank customer, click data of a merchant and credit scoring information.
2) A data storage module: the mobile phone bank uploads the collected data to the background system in real time, and the background system stores the data uploaded by the mobile phone bank in the database.
3) An algorithm implementation module: calculating the conversion rate of the customer to the commercial tenant, the click rate weight and the credit scoring weight of the commercial tenant, and the recommendation weight and the recommendation value of the commercial tenant to the customer; merchants are ranked from high to low.
4) The merchant display module: and the mobile phone bank compiles codes, loads the merchant information returned by the background system and displays the merchant information to the customer.
To realize the above modularized example, the following procedures are included:
1) and the mobile phone bank collects the customer behavior and the merchant click rate data and uploads the data to the background system.
2) The background system stores data collected by the mobile phone bank.
3) When a customer requests the merchant data, the background system calculates the recommendation value of each merchant to the customer according to the data stored in the database, sorts the recommendation values from high to low, and then returns the merchant information meeting the conditions.
4) And the mobile phone bank loads the merchant information and displays the merchant information to the customer.
When the technical staff implements the merchant recommendation method provided by the embodiment of the present invention, the contents to be developed include:
1) a mobile banking data acquisition method and program.
2) And (4) a recommendation value algorithm of the three-party merchant to the customer.
In development, the key points mainly include:
1) a method for acquiring data of customer behaviors of a mobile phone bank and click quantity of a merchant.
2) And (4) recommending a value algorithm to the customer by the merchant of the mobile phone bank.
The invention can realize more accurate merchant-to-customer marketing: on the basis of using the version number of the mobile phone bank and the white list guest group for filtering, the behavior data of the customers and the click rate data of the merchants are comprehensively considered, the recommendation value of the merchants to the customers is calculated, and the recommendation value is sorted from high to low and then returned to the customers. Merchants that the customer has used before, or merchants with high clicks, will preferably be returned to the customer.
Fig. 5 is a schematic diagram of a computer device for operating a merchant recommendation method implemented by the present invention, and as shown in fig. 5, an embodiment of the present invention further provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the merchant recommendation method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program for implementing the merchant recommendation method is stored in the computer-readable storage medium.
The embodiment of the invention also provides a merchant recommending device, which is described in the following embodiment. Because the principle of the device for solving the problems is similar to that of a merchant recommendation method, the implementation of the device can refer to the implementation of the merchant recommendation method, and repeated parts are not described again.
Fig. 6 is a schematic diagram of a merchant recommending apparatus according to an embodiment of the present invention, and as shown in fig. 6, an embodiment of the present invention further provides a merchant recommending apparatus, which may include:
the click rate and transaction amount acquisition module 601 is used for acquiring the click rate and transaction amount of the customer to the merchant;
a conversion rate determination module 602 for determining a conversion rate of the customer to the merchant according to the click rate and the transaction amount of the customer to the merchant;
a total click rate and credit score acquisition module 603 for acquiring total click rate and credit score of the merchant;
the recommendation weight determination module 604 of the merchant is configured to determine the recommendation weight of the merchant according to the total click amount and the credit score of the merchant;
a merchant-to-customer recommendation value determining module 605, configured to determine a merchant-to-customer recommendation value according to the merchant conversion rate of the customer and the merchant recommendation weight;
the merchant recommendation list determining module 606 is configured to determine a merchant recommendation list according to a recommendation value of a merchant to a customer.
When the merchant recommending device provided by the embodiment of the present invention is implemented specifically, in an embodiment, the click volume and transaction volume collecting module is specifically configured to:
developing a data acquisition program, embedding the data acquisition program into the mobile phone bank APP, acquiring the click rate and the transaction amount of each merchant of a customer, uploading the click rate and the transaction amount to a background system in real time, and storing data uploaded by the mobile phone bank APP to a database through the background system.
In an embodiment of the invention, when the merchant recommendation apparatus provided in the embodiment of the present invention is implemented specifically, the conversion rate determination module of the customer to the merchant is configured to determine the conversion rate of the customer to the merchant according to the following manner:
Fi＝Ti/Ci
wherein, FiConversion rate for customer to ith merchant, conversion rate F includes (F)1,F2，...,Fi...,Fn) N is the number of merchants; t isiTransaction amount for the customer to the ith merchant, transaction amount T comprising (T)1,T2，...,Ti...,Tn) N is the number of merchants; ciThe click rate C of the ith merchant for the customer comprises (C)1,C2，...,Ci...,Cn) And n is the number of merchants.
When the merchant recommending device provided by the embodiment of the present invention is implemented specifically, in an embodiment, the merchant total click rate and credit score collecting module is specifically configured to:
the method comprises the steps of utilizing a data acquisition program embedded into an APP of the mobile phone bank to acquire the total click quantity and credit score of each merchant, uploading the total click quantity and credit score to a background system in real time, and storing data uploaded by the APP of the mobile phone bank to a database through the background system.
In an embodiment of the invention, when the merchant recommendation apparatus provided in the embodiment of the present invention is implemented specifically, the merchant recommendation weight determining module is specifically configured to:
connecting a background system with a database, and extracting the total click rate and credit score of the merchant;
normalizing the total click rate and the credit score of the merchants, regularly acquiring the click rate of the merchant with the maximum click rate from all the merchants according to preset time, and regularly acquiring the credit score of the merchant with the highest credit score from all the merchants according to the preset time;
determining the click rate weight of each merchant according to the click rate of the merchant with the maximum click rate and by combining the click rate of each merchant;
determining credit scoring weight of each merchant according to the credit score of the merchant with the highest credit score and by combining the credit score of each merchant;
and respectively adding the click weight of each merchant and the credit scoring weight of each merchant to determine the recommendation weight of each merchant, and storing the recommendation weight in the database.
In an embodiment of the invention, when the merchant recommendation apparatus provided in the embodiment of the present invention is implemented specifically, the module for determining a recommendation value of a merchant to a customer is specifically configured to:
extracting the conversion rate of the customer to the commercial tenant and the recommendation weight of the commercial tenant through a background system;
and multiplying the conversion rate of the customer to the commercial tenant with the recommendation weight of the commercial tenant, and calculating to obtain the recommendation value of each commercial tenant to the customer.
In an embodiment of the invention, when the merchant recommendation apparatus provided in the embodiment of the present invention is implemented specifically, the merchant recommendation list determining module is specifically configured to:
and sequencing the recommendation values of each merchant to the customer in the order from high to low to determine a merchant recommendation list.
In an embodiment of the invention, when the merchant recommendation apparatus provided in the embodiment of the present invention is implemented specifically, the merchant recommendation list determining module is further configured to:
returning the merchant recommendation list to the mobile phone bank APP through the background system;
and loading the merchant recommendation list through the APP of the mobile phone bank, and displaying the merchants according to the recommendation value sequence.
To sum up, a merchant recommendation method and apparatus provided by the embodiments of the present invention include: firstly, acquiring the click quantity and the transaction quantity of a merchant by a customer; then determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant; then, acquiring the total click quantity and credit score of the merchant; continuously determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant; next, determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant; and finally, determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
The invention provides a method for filtering merchant information by comprehensively considering dynamic information such as click rate, transaction behavior and merchant click rate of a customer on the basis of an original merchant information filtering scheme using a mobile phone bank version number and white list customer group filtering, calculating a recommendation value of a merchant to the customer, sorting merchants meeting conditions from high to low according to the recommendation value, and preferentially returning the merchants used by the customer before or the merchants with high click rate to the mobile phone bank customer so as to realize accurate marketing of the merchant to the customer.
According to the embodiment of the invention, the conversion rate F of the customer to all the merchants is calculated by collecting and recording the click rate and transaction amount data of the customer to each merchant. And collecting and recording the total click rate and the credit score of each merchant, and carrying out normalization processing to obtain the click rate weight and the credit score weight of each merchant. The two are added as the recommendation weight W of the merchant. And the product of the conversion rate F of the customer to the merchant and the merchant weight W is used as a recommended value of the merchant to the customer, and the recommended value is sorted from top to bottom and then returned to the customer.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (18)
1. A merchant recommendation method, comprising:
collecting the click quantity and the transaction quantity of a merchant by a customer;
determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant;
collecting total click quantity and credit score of a merchant;
determining the recommendation weight of the merchant according to the total click quantity and the credit score of the merchant;
determining a recommendation value of the merchant to the customer according to the conversion rate of the customer to the merchant and the recommendation weight of the merchant;
and determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
2. The method of claim 1, wherein collecting the amount of clicks and transactions made by the customer to the merchant comprises:
developing a data acquisition program, embedding the data acquisition program into the mobile phone bank APP, acquiring the click rate and the transaction amount of each merchant of a customer, uploading the click rate and the transaction amount to a background system in real time, and storing data uploaded by the mobile phone bank APP to a database through the background system.
3. The method of claim 1, wherein the customer to merchant conversion is determined as follows:
Fi＝Ti/Ci
wherein, FiConversion rate for customer to ith merchant, conversion rate F includes (F)1,F2，...,Fi...,Fn) N is the number of merchants; t isiTransaction amount for the customer to the ith merchant, transaction amount T comprising (T)1,T2，...,Ti...,Tn) N is the number of merchants; ciThe click rate C of the ith merchant for the customer comprises (C)1,C2，...,Ci...,Cn) And n is the number of merchants.
4. The method of claim 2, wherein collecting merchant total clicks and credit scores comprises:
the method comprises the steps of utilizing a data acquisition program embedded into an APP of the mobile phone bank to acquire the total click quantity and credit score of each merchant, uploading the total click quantity and credit score to a background system in real time, and storing data uploaded by the APP of the mobile phone bank to a database through the background system.
5. The method of claim 1, wherein determining the recommendation weight for the merchant based on the merchant total clicks and the credit score comprises:
connecting a background system with a database, and extracting the total click rate and credit score of the merchant;
normalizing the total click rate and the credit score of the merchants, regularly acquiring the click rate of the merchant with the maximum click rate from all the merchants according to preset time, and regularly acquiring the credit score of the merchant with the highest credit score from all the merchants according to the preset time;
determining the click rate weight of each merchant according to the click rate of the merchant with the maximum click rate and by combining the click rate of each merchant;
determining credit scoring weight of each merchant according to the credit score of the merchant with the highest credit score and by combining the credit score of each merchant;
and respectively adding the click weight of each merchant and the credit scoring weight of each merchant to determine the recommendation weight of each merchant, and storing the recommendation weight in the database.
6. The method of claim 1, wherein determining the merchant-to-customer recommendation value based on the customer-to-merchant conversion rate and the merchant recommendation weight comprises:
extracting the conversion rate of the customer to the commercial tenant and the recommendation weight of the commercial tenant through a background system;
and multiplying the conversion rate of the customer to the commercial tenant with the recommendation weight of the commercial tenant, and calculating to obtain the recommendation value of each commercial tenant to the customer.
7. The method of claim 6, wherein determining a merchant recommendation list based on the merchant's recommendation value to the customer comprises:
and sequencing the recommendation values of each merchant to the customer in the order from high to low to determine a merchant recommendation list.
8. The method of claim 7, further comprising:
returning the merchant recommendation list to the mobile phone bank APP through the background system;
and loading the merchant recommendation list through the APP of the mobile phone bank, and displaying the merchants according to the recommendation value sequence.
9. A merchant recommendation device, comprising:
the system comprises a click rate and transaction amount acquisition module, a transaction amount acquisition module and a data processing module, wherein the click rate and transaction amount acquisition module is used for acquiring the click rate and transaction amount of a merchant by a customer;
the conversion rate determining module of the customer to the commercial tenant is used for determining the conversion rate of the customer to the commercial tenant according to the click quantity and the transaction quantity of the customer to the commercial tenant;
the merchant total click rate and credit score acquisition module is used for acquiring the merchant total click rate and credit score;
the recommendation weight determination module of the commercial tenant is used for determining the recommendation weight of the commercial tenant according to the total click quantity and the credit score of the commercial tenant;
the merchant-to-customer recommendation value determination module is used for determining a merchant-to-customer recommendation value according to the merchant conversion rate of the customer and the merchant recommendation weight;
and the merchant recommendation list determining module is used for determining a merchant recommendation list according to the recommendation value of the merchant to the customer.
10. The apparatus of claim 9, wherein the click volume and transaction volume collection module is specifically configured to:
developing a data acquisition program, embedding the data acquisition program into the mobile phone bank APP, acquiring the click rate and the transaction amount of each merchant of a customer, uploading the click rate and the transaction amount to a background system in real time, and storing data uploaded by the mobile phone bank APP to a database through the background system.
11. The apparatus of claim 9, wherein the customer-to-merchant conversion determination module is configured to determine the customer-to-merchant conversion as follows:
Fi＝Ti/Ci
wherein, FiConversion rate for customer to ith merchant, conversion rate F includes (F)1,F2，...,Fi...,Fn) N is the number of merchants; t isiTransaction amount for the customer to the ith merchant, transaction amount T comprising (T)1,T2，...,Ti...,Tn) N is the number of merchants; ciThe click rate C of the ith merchant for the customer comprises (C)1,C2，...,Ci...,Cn) And n is the number of merchants.
12. The apparatus of claim 10, wherein the merchant gross click and credit score collection module is specifically configured to:
the method comprises the steps of utilizing a data acquisition program embedded into an APP of the mobile phone bank to acquire the total click quantity and credit score of each merchant, uploading the total click quantity and credit score to a background system in real time, and storing data uploaded by the APP of the mobile phone bank to a database through the background system.
13. The apparatus of claim 9, wherein the merchant recommendation weight determination module is specifically configured to:
connecting a background system with a database, and extracting the total click rate and credit score of the merchant;
normalizing the total click rate and the credit score of the merchants, regularly acquiring the click rate of the merchant with the maximum click rate from all the merchants according to preset time, and regularly acquiring the credit score of the merchant with the highest credit score from all the merchants according to the preset time;
determining the click rate weight of each merchant according to the click rate of the merchant with the maximum click rate and by combining the click rate of each merchant;
determining credit scoring weight of each merchant according to the credit score of the merchant with the highest credit score and by combining the credit score of each merchant;
and respectively adding the click weight of each merchant and the credit scoring weight of each merchant to determine the recommendation weight of each merchant, and storing the recommendation weight in the database.
14. The apparatus of claim 9, wherein the merchant to customer recommendation determination module is specifically configured to:
extracting the conversion rate of the customer to the commercial tenant and the recommendation weight of the commercial tenant through a background system;
and multiplying the conversion rate of the customer to the commercial tenant with the recommendation weight of the commercial tenant, and calculating to obtain the recommendation value of each commercial tenant to the customer.
15. The apparatus of claim 14, wherein the merchant recommendation list determination module is specifically configured to:
and sequencing the recommendation values of each merchant to the customer in the order from high to low to determine a merchant recommendation list.
16. The apparatus of claim 15, wherein the merchant recommendation list determination module is further configured to:
returning the merchant recommendation list to the mobile phone bank APP through the background system;
and loading the merchant recommendation list through the APP of the mobile phone bank, and displaying the merchants according to the recommendation value sequence.
17. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 8 when executing the computer program.
18. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing a method according to any one of claims 1 to 8.
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