CN112148995A - Product recommendation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to an intelligent recommendation technology, and discloses a product recommendation method, which comprises the following steps: determining first characteristics of each user, and grouping the users based on the first characteristics to obtain a plurality of user groups; calculating a product permeability array corresponding to each user group in a plurality of user groups; determining a target user group corresponding to a user to be recommended; acquiring a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended; and when the target attribute value is smaller than a preset threshold value, recommending a target product for the user to be recommended based on the product permeability array corresponding to the target user group. The invention also provides a product recommendation device, electronic equipment and a readable storage medium. The invention improves the product recommendation success rate.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a product recommendation method and device, electronic equipment and a readable storage medium.
Background
With the development of science and technology, product recommendation is more and more widely applied in people's lives, for example, article pushing, commodity information recommendation and the like, and currently, recommendation is generally performed according to relevant information of a user, such as historical browsing information and purchasing information, so that the recommendation success rate is low. Therefore, a product recommendation method is needed to improve the recommendation success rate.
Disclosure of Invention
In view of the above, there is a need to provide a product recommendation method, aiming at improving the recommendation success rate.
The product recommendation method provided by the invention comprises the following steps:
acquiring first historical data of each user in a database, determining first characteristics of each user based on first characteristic factors and the first historical data, and grouping each user based on the first characteristics to obtain a plurality of user groups;
calculating a product permeability array corresponding to each of the plurality of user groups based on the first historical data;
analyzing a product recommendation request sent by a user based on a client, acquiring an identifier of a user to be recommended carried by the product recommendation request, acquiring second historical data of the user to be recommended from the database based on the identifier, and determining a target user group corresponding to the user to be recommended;
acquiring a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended;
and when the target attribute value is smaller than a preset threshold value, recommending a target product for the user to be recommended based on the product permeability array corresponding to the target user group.
Optionally, the grouping the users based on the first feature to obtain a plurality of user groups includes:
grouping each user by adopting a K-means clustering algorithm based on the first characteristic, wherein K is respectively a natural number in a preset numerical range, and one value of K corresponds to one grouping result to obtain a plurality of grouping results;
determining a central user of each user group corresponding to each grouping result in the multiple grouping results, and respectively calculating a contour coefficient corresponding to each grouping result based on a first characteristic of the central user;
and taking the grouping result of which the contour coefficient is closest to a preset numerical value as a target grouping result.
Optionally, the determining the central user of each user group corresponding to each grouping result in the multiple grouping results includes:
and calculating the average value of the first characteristics of all users in each user group corresponding to each grouping result, and taking the user with the minimum absolute value of the difference between the first characteristics and the average value in each user group as the central user of each user group.
Optionally, the calculation formula of the contour coefficient is:
wherein S ispqRepresenting the profile coefficients corresponding to the qth user group in the pth grouping result,
representing the average distance of the first characteristic of the central user of the qth group of users in the pth grouping result to the first characteristics of the other users of the same group of users,
representing the minimum value of the average distance of the first features of the central users of the qth group of users to the first features of the other groups of users in the pth grouping result, SpRepresents the p-th packet junctionAnd m represents the total number of user groups in the p-th grouping result according to the corresponding contour coefficient.
Optionally, after the second feature is input to the attribute analysis model to obtain the target attribute value of the user to be recommended, the method further includes:
if the target attribute value is larger than a preset threshold value, calculating index values of a plurality of preset indexes based on the second historical data, and recommending a target product for the user to be recommended based on a specified index when the index value corresponding to a specified index in the plurality of preset indexes is larger than an index threshold value.
Optionally, the calculating, based on the first historical data, a product permeability set corresponding to each of the plurality of user groups includes:
determining products corresponding to each user in each user group based on the first historical data;
calculating user proportion corresponding to various products in each user group;
and determining a product permeability group corresponding to each user group based on the user proportion.
Optionally, after obtaining the second historical data of the user to be recommended from the database based on the identifier, the method further includes:
and if the second historical data of the user to be recommended cannot be acquired from the database, recommending a preset product list to the user to be recommended.
In order to solve the above problems, the present invention also provides a product recommendation apparatus, comprising:
the grouping module is used for acquiring first historical data of each user in a database, determining first characteristics of each user based on first characteristic factors and the first historical data, and grouping each user based on the first characteristics to obtain a plurality of user groups;
a calculation module, configured to calculate, based on the first historical data, a product permeability array corresponding to each of the plurality of user groups;
the analysis module is used for analyzing a product recommendation request sent by a user based on a client, acquiring an identifier of a user to be recommended carried by the product recommendation request, acquiring second historical data of the user to be recommended from the database based on the identifier, and determining a target user group corresponding to the user to be recommended;
the determining module is used for acquiring a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended;
and the recommending module is used for recommending the target product for the user to be recommended based on the product permeability array corresponding to the target user group when the target attribute value is smaller than a preset threshold value.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a product recommendation program executable by the at least one processor, the product recommendation program being executable by the at least one processor to enable the at least one processor to perform the product recommendation method described above.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having a product recommendation program stored thereon, the product recommendation program being executable by one or more processors to implement the above product recommendation method.
Compared with the prior art, the method has the advantages that the users are grouped according to the first characteristic to obtain a plurality of user groups, the product permeability array corresponding to each user group is calculated based on the first historical data, the preference degree of different user groups to different products is analyzed, and accurate recommendation can be facilitated; then, second historical data of the user to be recommended are obtained, a target user group corresponding to the user to be recommended is determined, second characteristics of the user to be recommended are determined according to the second characteristic factors and the second historical data, the second characteristics are input into an attribute analysis model to obtain a target attribute value (namely, a loss rate) of the user to be recommended, when the target attribute value is smaller than a preset threshold value, a target product is recommended for the user to be recommended according to a product permeability array corresponding to the target user group, and in the step, product recommendation is performed on the basis of the product permeability array under the condition that the user loss rate is low, so that the product recommendation success rate is higher. Therefore, the invention improves the product recommendation success rate.
Drawings
Fig. 1 is a schematic flowchart of a product recommendation method according to an embodiment of the present invention;
FIG. 2 is a block diagram of a product recommendation device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device implementing a product recommendation method according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the description relating to "first", "second", etc. in the present invention is for descriptive purposes only and is not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
The invention provides a product recommendation method. Referring to fig. 1, a flowchart of a product recommendation method according to an embodiment of the present invention is shown. The method may be performed by an electronic device, which may be implemented by software and/or hardware.
In this embodiment, the product recommendation method includes:
s1, acquiring first historical data of each user in the database, determining first characteristics of each user based on first characteristic factors and the first historical data, and grouping each user based on the first characteristics to obtain a plurality of user groups.
In this embodiment, a description is given by taking a product recommended by a bank client as an example, the first historical data includes basic information and asset information of a user, and the first characteristic factors include a gender, an age, a school calendar, an account age, a card holding level, a client level, whether the user is a member, whether a risk assessment is completed, whether three-party payment is bound, and the like of the user in the basic information, an AUM (total asset market value) in the asset information, a maximum historical AUM, a PPC value (number of currently held products), an AUM monthly-daily average value, an AUM yearly daily average value, and the like. The first characteristic is obtained by splicing characteristic values corresponding to all characteristics in the basic information and the asset information.
The grouping of the users based on the first characteristic to obtain a plurality of user groups comprises:
a1, grouping the users by adopting a K-means clustering algorithm based on the first characteristic, wherein K is respectively a natural number in a preset numerical range, and one value of K corresponds to one grouping result to obtain a plurality of grouping results;
k represents the number of user groups, and in this embodiment, K is any natural number from 3 to 10, and 8 grouping results, including 3 user groups, 4 user groups, … … user groups, 9 user groups, and 10 user groups, can be obtained.
The user grouping process is illustrated with K ═ 3: taking the first characteristics of any 3 users as three initial clustering centers, then calculating the distance of the first characteristics between the remaining users and each clustering center, allocating each user to the closest clustering, allocating one user each, recalculating the clustering center of the cluster according to the existing users in the clustering, and repeating the steps until all the users are grouped.
A2, determining a central user of each user group corresponding to each grouping result in the multiple grouping results, and respectively calculating a contour coefficient corresponding to each grouping result based on a first characteristic of the central user;
the existing method for calculating the outline coefficient has higher complexity, wherein the calculation formula of the outline coefficient corresponding to a user is as follows:
wherein S isijRepresenting the contour coefficient corresponding to the jth user in the ith grouping result,
representing the average distance of the first feature of the jth user in the ith grouping result to the first features of other users in the same user group,
and the minimum value of the average distance from the first feature of the jth user to the first features of other user groups in the ith grouping result is represented.
The calculation formula of the contour coefficient corresponding to the grouping result is as follows:
wherein S isiRepresenting the profile coefficient, S, corresponding to the ith grouping resultijAnd representing the profile coefficient corresponding to the jth user in the ith grouping result, and n represents the total number of the users.
The contour coefficient is an evaluation mode of the grouping result, and reflects the cohesion and the separation of the clustering method. If the cohesion of the same cluster is higher and the separation degree of different clusters is higher, the clustering effect is better, and S isijThe closer to 1 represents
The smaller the clustering, the better the clustering.
It can be known from the above formula that when calculating the contour coefficient corresponding to each user, the distances between the contour coefficient and the first characteristic values of other users are calculated respectively, and finally the distances of all users are averaged, with the time complexity of n2. In order to reduce the time complexity, the invention provides a simplified contour coefficient calculation method. In the clustering process, the centroid of each cluster is known due to the adoption of the K-means algorithm. The centroid is the most central point of each cluster to which other points within the cluster are close, and thus the centroid can approximately represent the entire cluster. When the contour coefficients are calculated, only the distance between the centroids is calculated, namely the formula for calculating the contour coefficients is unchanged, but the parameter meanings are different, and the calculation amount is greatly reduced.
The determining the center user (i.e. the centroid) of each user group corresponding to each grouping result in the plurality of grouping results includes:
and calculating the average value of the first characteristics of all users in each user group corresponding to each grouping result, and taking the user with the minimum absolute value of the difference between the first characteristics and the average value in each user group as the central user of each user group.
In other embodiments, the central user of each user group may be determined in other manners, and the determination manner of the central user is not limited in the present invention.
The calculation formula of the contour coefficient in the embodiment is as follows:
wherein S ispqRepresenting the profile coefficients corresponding to the qth user group in the pth grouping result,
representing the average distance of the first characteristic of the central user of the qth group of users in the pth grouping result to the first characteristics of the other users of the same group of users,
representing the minimum value of the average distance of the first features of the central users of the qth group of users to the first features of the other groups of users in the pth grouping result, SpAnd representing the profile coefficient corresponding to the p-th grouping result, and m represents the total number of the user groups in the p-th grouping result.
As can be seen, the invention will calculate the complexity of the contour coefficient from n2The grouping efficiency is greatly improved by the method and the device, which are simplified to mn, wherein n is the total number of users, and m is the number of user groups.
And A3, taking the grouping result of the contour coefficient closest to the preset value as the target grouping result.
In this embodiment, the preset value is 1, and the grouping result with the contour coefficient closest to 1 is used as the target grouping result.
And S2, calculating a product permeability array corresponding to each user group in the plurality of user groups based on the first historical data.
The calculating a set of product permeability values corresponding to respective ones of the plurality of user groups based on the first historical data comprises:
b1, determining products corresponding to each user in each user group based on the first historical data;
b2, calculating user proportion corresponding to each product in each user group;
and B3, determining a product permeability group corresponding to each user group based on the user proportion.
In this embodiment, the first historical data further includes product purchase information of the user, and the product permeability in the product permeability group corresponding to a certain user group is a percentage of the number of people who purchase each product in the user group to the total number of users in the user group.
For example, the user accounts corresponding to the products in the user groups are shown in table 1 below:
| period of life | On a regular basis | Financing | Fund gold | Safety device | …… | Letter support | |
| User group 1 | 20％ | 5％ | 50％ | 2％ | 6％ | …… | 2％ |
| User group 2 | 30％ | 1％ | 1％ | 62％ | 1％ | …… | 1％ |
| User group 3 | 13％ | 20％ | 1％ | 2％ | 52％ | …… | 2％ |
| User group 4 | 9％ | 78％ | 2％ | 5％ | 2％ | …… | 1％ |
TABLE 1
As can be seen from Table 1 above, the product permeability groups corresponding to the user group 1 are { 20%, 5%, 50%, 2%, 6%, … …, 2% }, and the preference degrees of different user groups for different products can be analyzed according to the product permeability groups, and can be used for product recommendation.
S3, analyzing a product recommendation request sent by a user based on a client, acquiring an identifier of a user to be recommended carried by the product recommendation request, acquiring second historical data of the user to be recommended from the database based on the identifier, and determining a target user group corresponding to the user to be recommended;
if the historical data of the user to be recommended can be obtained from the database, the user to be recommended is indicated to be a stock user, the target user group information corresponding to the user to be recommended can be obtained based on the user identification, if the target user group cannot be determined, the absolute value of the difference value of the first characteristics of the user to be recommended and the central user of each user group is calculated, and the user group to which the central user belongs corresponding to the minimum absolute value of the difference value is taken as the target user group of the user to be recommended.
In this embodiment, the method further includes:
and if the second historical data of the user to be recommended cannot be acquired from the database, recommending a preset product list to the user to be recommended.
If the historical data cannot be acquired, the user to be recommended is a new user, and data information is not generated yet, and at the moment, a pre-configured product list aiming at the new user is recommended to the new user.
S4, obtaining a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended.
In this embodiment, the attribute analysis model is used to analyze the churn probability of the user, and the attribute analysis model is a random forest model.
The second feature factors include volatility feature, trend feature, product hobby feature and activity feature, and the second feature of the user is constructed by using time slices, for example, the data of the previous 6 months and the data of the previous 3 months are screened from the second historical data to determine the feature values corresponding to the feature factors of the user.
The characteristic values corresponding to the volatility characteristics comprise: asset range of approximately 3 months (difference between asset maximum and asset minimum), asset standard deviation of approximately 3 months, asset range of approximately 6 months, and asset standard deviation of approximately 6 months; the characteristic values corresponding to the trend characteristics comprise: an asset growth rate of approximately 3 months, an asset quantity growth rate of approximately 3 months, an asset growth rate of approximately 6 months, and a quantity growth rate of approximately 6 months; the characteristic value corresponding to the product preference characteristic comprises: the system comprises a life ratio, a regular ratio, a financing ratio, a fund ratio, a resource and management ratio and an insurance ratio; the characteristic values corresponding to the activity characteristics comprise: account age, average dynamic account transaction times of nearly 3 months, average dynamic account transaction times of nearly 6 months, and the like.
And splicing the characteristic values corresponding to the volatility characteristic, the trend characteristic, the product hobby characteristic and the activity characteristic to obtain a second characteristic, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value (namely loss probability) of the user to be recommended.
And S5, recommending the target product for the user to be recommended based on the product permeability group corresponding to the target user group when the target attribute value is smaller than the preset threshold value.
When the target attribute value (namely the loss probability) is smaller than a preset threshold value, the user loss probability is low, at the moment, a product permeability array corresponding to a target user group to which the user to be recommended belongs is obtained, and a plurality of products with the product permeability close to the front are recommended to the user to be recommended as target products.
In this embodiment, after the second feature is input to the attribute analysis model to obtain the target attribute value of the user to be recommended, the method further includes:
if the target attribute value is larger than a preset threshold value, calculating index values of a plurality of preset indexes based on the second historical data, and recommending a target product for the user to be recommended based on a specified index when the index value corresponding to a specified index in the plurality of preset indexes is larger than an index threshold value.
When the target attribute value (namely, the churn probability) is greater than a preset threshold value, it indicates that the churn probability of the user is high, the preset index is an index which is preset and has a large influence on the churn rate, the churn reason of the user can be determined based on the preset index, and a suitable product is recommended, for example, the asset standard deviation is a preset index, when the asset standard deviation is greater than the index threshold value, it indicates that the asset variation range of the user to be recommended is large, and in order to reduce the asset standard deviation, a financial product with a long period, such as a large volume deposit list, can be recommended to the user to be recommended.
According to the product recommendation method, firstly, the users are grouped according to the first characteristic to obtain a plurality of user groups, the product permeability data group corresponding to each user group is calculated based on the first historical data, the preference degree of different user groups to different products is analyzed, and accurate recommendation can be facilitated; then, second historical data of the user to be recommended are obtained, a target user group corresponding to the user to be recommended is determined, second characteristics of the user to be recommended are determined according to the second characteristic factors and the second historical data, the second characteristics are input into an attribute analysis model to obtain a target attribute value (namely, a loss rate) of the user to be recommended, when the target attribute value is smaller than a preset threshold value, a target product is recommended for the user to be recommended according to a product permeability array corresponding to the target user group, and in the step, product recommendation is performed on the basis of the product permeability array under the condition that the user loss rate is low, so that the product recommendation success rate is higher. Therefore, the invention improves the product recommendation success rate.
Fig. 2 is a schematic block diagram of a product recommendation device according to an embodiment of the present invention.
The product recommendation device 100 of the present invention may be installed in an electronic device. Depending on the implemented functionality, the product recommendation device 100 may include a grouping module 110, a calculation module 120, a parsing module 130, a determination module 140, and a recommendation module 150. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the grouping module 110 is configured to obtain first historical data of each user in a database, determine first characteristics of each user based on first characteristic factors and the first historical data, and group each user based on the first characteristics to obtain a plurality of user groups.
In this embodiment, a description is given by taking a product recommended by a bank client as an example, the first historical data includes basic information and asset information of a user, and the first characteristic factors include a gender, an age, a school calendar, an account age, a card holding level, a client level, whether the user is a member, whether a risk assessment is completed, whether three-party payment is bound, and the like of the user in the basic information, an AUM (total asset market value) in the asset information, a maximum historical AUM, a PPC value (number of currently held products), an AUM monthly-daily average value, an AUM yearly daily average value, and the like. The first characteristic is obtained by splicing characteristic values corresponding to all characteristics in the basic information and the asset information.
The grouping of the users based on the first characteristic to obtain a plurality of user groups comprises:
a1, grouping the users by adopting a K-means clustering algorithm based on the first characteristic, wherein K is respectively a natural number in a preset numerical range, and one value of K corresponds to one grouping result to obtain a plurality of grouping results;
k represents the number of user groups, and in this embodiment, K is any natural number from 3 to 10, and 8 grouping results, including 3 user groups, 4 user groups, … … user groups, 9 user groups, and 10 user groups, can be obtained.
The user grouping process is illustrated with K ═ 3: taking the first characteristics of any 3 users as three initial clustering centers, then calculating the distance of the first characteristics between the remaining users and each clustering center, allocating each user to the closest clustering, allocating one user each, recalculating the clustering center of the cluster according to the existing users in the clustering, and repeating the steps until all the users are grouped.
A2, determining a central user of each user group corresponding to each grouping result in the multiple grouping results, and respectively calculating a contour coefficient corresponding to each grouping result based on a first characteristic of the central user;
the existing method for calculating the outline coefficient has higher complexity, wherein the calculation formula of the outline coefficient corresponding to a user is as follows:
wherein S isijRepresenting the contour coefficient corresponding to the jth user in the ith grouping result,
representing the average distance of the first feature of the jth user in the ith grouping result to the first features of other users in the same user group,
and the minimum value of the average distance from the first feature of the jth user to the first features of other user groups in the ith grouping result is represented.
The calculation formula of the contour coefficient corresponding to the grouping result is as follows:
wherein S isiRepresenting the profile coefficient, S, corresponding to the ith grouping resultijAnd representing the profile coefficient corresponding to the jth user in the ith grouping result, and n represents the total number of the users.
The contour coefficient is an evaluation mode of the grouping result, and reflects the cohesion and the separation of the clustering method. If the cohesion of the same cluster is higher and the separation degree of different clusters is higher, the clustering effect is better, and S isijThe closer to 1 represents
The smaller the clustering, the better the clustering.
It can be known from the above formula that when calculating the contour coefficient corresponding to each user, the distances between the contour coefficient and the first characteristic values of other users are calculated respectively, and finally the distances of all users are averaged, with the time complexity of n2. In order to reduce the time complexity, the invention provides a simplified contour coefficient calculation method. In the clustering process, the centroid of each cluster is known due to the adoption of the K-means algorithm. The centroid is the most central point of each cluster to which other points within the cluster are close, and thus the centroid can approximately represent the entire cluster. When the contour coefficients are calculated, only the distance between the centroids is calculated, namely the formula for calculating the contour coefficients is unchanged, but the parameter meanings are different, and the calculation amount is greatly reduced.
The determining the center user (i.e. the centroid) of each user group corresponding to each grouping result in the plurality of grouping results includes:
and calculating the average value of the first characteristics of all users in each user group corresponding to each grouping result, and taking the user with the minimum absolute value of the difference between the first characteristics and the average value in each user group as the central user of each user group.
In other embodiments, the central user of each user group may be determined in other manners, and the determination manner of the central user is not limited in the present invention.
The calculation formula of the contour coefficient in the embodiment is as follows:
wherein S ispqRepresenting the profile coefficients corresponding to the qth user group in the pth grouping result,
representing the average distance of the first characteristic of the central user of the qth group of users in the pth grouping result to the first characteristics of the other users of the same group of users,
denotes the p-th speciesMinimum value of the mean distance of the first features of the central users of the qth group of users to the first features of the other groups of users in the grouping result, SpAnd representing the profile coefficient corresponding to the p-th grouping result, and m represents the total number of the user groups in the p-th grouping result.
As can be seen, the invention will calculate the complexity of the contour coefficient from n2The grouping efficiency is greatly improved by the method and the device, which are simplified to mn, wherein n is the total number of users, and m is the number of user groups.
And A3, taking the grouping result of the contour coefficient closest to the preset value as the target grouping result.
In this embodiment, the preset value is 1, and the grouping result with the contour coefficient closest to 1 is used as the target grouping result.
A calculating module 120, configured to calculate, based on the first historical data, a product permeability array corresponding to each of the plurality of user groups.
The calculating a set of product permeability values corresponding to respective ones of the plurality of user groups based on the first historical data comprises:
b1, determining products corresponding to each user in each user group based on the first historical data;
b2, calculating user proportion corresponding to each product in each user group;
and B3, determining a product permeability group corresponding to each user group based on the user proportion.
In this embodiment, the first historical data further includes product purchase information of the user, and the product permeability in the product permeability group corresponding to a certain user group is a percentage of the number of people who purchase each product in the user group to the total number of users in the user group.
For example, the user accounts corresponding to the products in the user groups are shown in table 1 below:
| period of life | On a regular basis | Financing | Fund gold | Safety device | …… | Letter support | |
| User group 1 | 20％ | 5％ | 50％ | 2％ | 6％ | …… | 2％ |
| User group 2 | 30％ | 1％ | 1％ | 62％ | 1％ | …… | 1％ |
| User group 3 | 13％ | 20％ | 1％ | 2％ | 52％ | …… | 2％ |
| User group 4 | 9％ | 78％ | 2％ | 5％ | 2％ | …… | 1％ |
TABLE 1
As can be seen from Table 1 above, the product permeability groups corresponding to the user group 1 are { 20%, 5%, 50%, 2%, 6%, … …, 2% }, and the preference degrees of different user groups for different products can be analyzed according to the product permeability groups, and can be used for product recommendation.
The analysis module 130 is configured to analyze a product recommendation request sent by a user based on a client, acquire an identifier of a user to be recommended, which is carried by the product recommendation request, acquire second historical data of the user to be recommended from the database based on the identifier, and determine a target user group corresponding to the user to be recommended;
if the historical data of the user to be recommended can be obtained from the database, the user to be recommended is indicated to be a stock user, the target user group information corresponding to the user to be recommended can be obtained based on the user identification, if the target user group cannot be determined, the absolute value of the difference value of the first characteristics of the user to be recommended and the central user of each user group is calculated, and the user group to which the central user belongs corresponding to the minimum absolute value of the difference value is taken as the target user group of the user to be recommended.
In this embodiment, the parsing module 130 is further configured to:
and if the second historical data of the user to be recommended cannot be acquired from the database, recommending a preset product list to the user to be recommended.
If the historical data cannot be acquired, the user to be recommended is a new user, and data information is not generated yet, and at the moment, a pre-configured product list aiming at the new user is recommended to the new user.
The determining module 140 is configured to obtain a second feature factor corresponding to the product recommendation request, determine a second feature of the user to be recommended based on the second feature factor and the second historical data, and input the second feature into an attribute analysis model to obtain a target attribute value of the user to be recommended.
In this embodiment, the attribute analysis model is used to analyze the churn probability of the user, and the attribute analysis model is a random forest model.
The second feature factors include volatility feature, trend feature, product hobby feature and activity feature, and the second feature of the user is constructed by using time slices, for example, the data of the previous 6 months and the data of the previous 3 months are screened from the second historical data to determine the feature values corresponding to the feature factors of the user.
The characteristic values corresponding to the volatility characteristics comprise: asset range of approximately 3 months (difference between asset maximum and asset minimum), asset standard deviation of approximately 3 months, asset range of approximately 6 months, and asset standard deviation of approximately 6 months; the characteristic values corresponding to the trend characteristics comprise: an asset growth rate of approximately 3 months, an asset quantity growth rate of approximately 3 months, an asset growth rate of approximately 6 months, and a quantity growth rate of approximately 6 months; the characteristic value corresponding to the product preference characteristic comprises: the system comprises a life ratio, a regular ratio, a financing ratio, a fund ratio, a resource and management ratio and an insurance ratio; the characteristic values corresponding to the activity characteristics comprise: account age, average dynamic account transaction times of nearly 3 months, average dynamic account transaction times of nearly 6 months, and the like.
And splicing the characteristic values corresponding to the volatility characteristic, the trend characteristic, the product hobby characteristic and the activity characteristic to obtain a second characteristic, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value (namely loss probability) of the user to be recommended.
And the recommending module 150 is configured to recommend the target product to the user to be recommended based on the product permeability array corresponding to the target user group when the target attribute value is smaller than a preset threshold value.
When the target attribute value (namely the loss probability) is smaller than a preset threshold value, the user loss probability is low, at the moment, a product permeability array corresponding to a target user group to which the user to be recommended belongs is obtained, and a plurality of products with the product permeability close to the front are recommended to the user to be recommended as target products.
In this embodiment, after the second feature is input to the attribute analysis model to obtain the target attribute value of the user to be recommended, the recommending module 150 is further configured to:
if the target attribute value is larger than a preset threshold value, calculating index values of a plurality of preset indexes based on the second historical data, and recommending a target product for the user to be recommended based on a specified index when the index value corresponding to a specified index in the plurality of preset indexes is larger than an index threshold value.
When the target attribute value (namely, the churn probability) is greater than a preset threshold value, it indicates that the churn probability of the user is high, the preset index is an index which is preset and has a large influence on the churn rate, the churn reason of the user can be determined based on the preset index, and a suitable product is recommended, for example, the asset standard deviation is a preset index, when the asset standard deviation is greater than the index threshold value, it indicates that the asset variation range of the user to be recommended is large, and in order to reduce the asset standard deviation, a financial product with a long period, such as a large volume deposit list, can be recommended to the user to be recommended.
Fig. 3 is a schematic structural diagram of an electronic device for implementing a product recommendation method according to an embodiment of the present invention.
The electronic device 1 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a command set or stored in advance. The electronic device 1 may be a computer, or may be a single network server, a server group composed of a plurality of network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing, where cloud computing is one of distributed computing and is a super virtual computer composed of a group of loosely coupled computers.
In the present embodiment, the electronic device 1 includes, but is not limited to, a memory 11, a processor 12, and a network interface 13, which are communicatively connected to each other through a system bus, wherein the memory 11 stores a product recommendation program 10, and the product recommendation program 10 is executable by the processor 12. While FIG. 3 shows only the electronic device 1 with the components 11-13 and the product recommendation program 10, those skilled in the art will appreciate that the configuration shown in FIG. 3 does not constitute a limitation of the electronic device 1, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
The storage 11 includes a memory and at least one type of readable storage medium. The memory provides cache for the operation of the electronic equipment 1; the readable storage medium may be a non-volatile storage medium such as flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. The readable storage medium may also be a volatile storage medium. In some embodiments, the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1; in other embodiments, the storage medium may also be an external storage device of the electronic device 1, such as a plug-in hard disk provided on the electronic device 1, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. In this embodiment, the readable storage medium of the memory 11 is generally used for storing an operating system and various application software installed in the electronic device 1, for example, codes of the product recommendation program 10 in an embodiment of the present invention. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is generally configured to control the overall operation of the electronic device 1, such as performing control and processing related to data interaction or communication with other devices. In this embodiment, the processor 12 is configured to run the program codes stored in the memory 11 or process data, such as running the product recommendation program 10.
The network interface 13 may comprise a wireless network interface or a wired network interface, and the network interface 13 is used for establishing a communication connection between the electronic device 1 and a client (not shown).
Optionally, the electronic device 1 may further include a user interface, the user interface may include a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may further include a standard wired interface and a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The product recommendation program 10 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 12, may implement:
acquiring first historical data of each user in a database, determining first characteristics of each user based on first characteristic factors and the first historical data, and grouping each user based on the first characteristics to obtain a plurality of user groups;
calculating a product permeability array corresponding to each of the plurality of user groups based on the first historical data;
analyzing a product recommendation request sent by a user based on a client, acquiring an identifier of a user to be recommended carried by the product recommendation request, acquiring second historical data of the user to be recommended from the database based on the identifier, and determining a target user group corresponding to the user to be recommended;
acquiring a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended;
and when the target attribute value is smaller than a preset threshold value, recommending a target product for the user to be recommended based on the product permeability array corresponding to the target user group.
Specifically, the processor 12 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the product recommendation program 10, which is not described herein again. It is emphasized that, in order to further ensure the privacy and security of the first and second history data, the first and second history data may also be stored in a node of a block chain.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or non-volatile. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The computer-readable storage medium stores a product recommendation program 10, where the product recommendation program 10 may be executed by one or more processors, and a specific implementation of the computer-readable storage medium of the present invention is substantially the same as that in the embodiments of the product recommendation method, and is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for recommending products, the method comprising:
acquiring first historical data of each user in a database, determining first characteristics of each user based on first characteristic factors and the first historical data, and grouping each user based on the first characteristics to obtain a plurality of user groups;
calculating a product permeability array corresponding to each of the plurality of user groups based on the first historical data;
analyzing a product recommendation request sent by a user based on a client, acquiring an identifier of a user to be recommended carried by the product recommendation request, acquiring second historical data of the user to be recommended from the database based on the identifier, and determining a target user group corresponding to the user to be recommended;
acquiring a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended;
and when the target attribute value is smaller than a preset threshold value, recommending a target product for the user to be recommended based on the product permeability array corresponding to the target user group.
2. The product recommendation method of claim 1, wherein said grouping users based on said first characteristic to obtain a plurality of user groups comprises:
grouping each user by adopting a K-means clustering algorithm based on the first characteristic, wherein K is respectively a natural number in a preset numerical range, and one value of K corresponds to one grouping result to obtain a plurality of grouping results;
determining a central user of each user group corresponding to each grouping result in the multiple grouping results, and respectively calculating a contour coefficient corresponding to each grouping result based on a first characteristic of the central user;
and taking the grouping result of which the contour coefficient is closest to a preset numerical value as a target grouping result.
3. The product recommendation method of claim 2, wherein said determining a central user of a respective user group corresponding to each grouping result of said plurality of grouping results comprises:
and calculating the average value of the first characteristics of all users in each user group corresponding to each grouping result, and taking the user with the minimum absolute value of the difference between the first characteristics and the average value in each user group as the central user of each user group.
4. The product recommendation method of claim 2, wherein the profile coefficient is calculated by:
wherein S ispqRepresenting the profile coefficients corresponding to the qth user group in the pth grouping result,
representing the average distance of the first characteristic of the central user of the qth group of users in the pth grouping result to the first characteristics of the other users of the same group of users,
representing the minimum value of the average distance of the first features of the central users of the qth group of users to the first features of the other groups of users in the pth grouping result, SpAnd representing the profile coefficient corresponding to the p-th grouping result, and m represents the total number of the user groups in the p-th grouping result.
5. The product recommendation method of claim 1, wherein after the second feature input attribute analysis model is applied to obtain the target attribute value of the user to be recommended, the method further comprises:
if the target attribute value is larger than a preset threshold value, calculating index values of a plurality of preset indexes based on the second historical data, and recommending a target product for the user to be recommended based on a specified index when the index value corresponding to a specified index in the plurality of preset indexes is larger than an index threshold value.
6. The product recommendation method of claim 1, wherein said calculating a set of product permeability numbers for each of the plurality of user groups based on the first historical data comprises:
determining products corresponding to each user in each user group based on the first historical data;
calculating user proportion corresponding to various products in each user group;
and determining a product permeability group corresponding to each user group based on the user proportion.
7. The product recommendation method of claim 1, wherein after obtaining the second historical data of the user to be recommended from the database based on the identification, the method further comprises:
and if the second historical data of the user to be recommended cannot be acquired from the database, recommending a preset product list to the user to be recommended.
8. A product recommendation device, the device comprising:
the grouping module is used for acquiring first historical data of each user in a database, determining first characteristics of each user based on first characteristic factors and the first historical data, and grouping each user based on the first characteristics to obtain a plurality of user groups;
a calculation module, configured to calculate, based on the first historical data, a product permeability array corresponding to each of the plurality of user groups;
the analysis module is used for analyzing a product recommendation request sent by a user based on a client, acquiring an identifier of a user to be recommended carried by the product recommendation request, acquiring second historical data of the user to be recommended from the database based on the identifier, and determining a target user group corresponding to the user to be recommended;
the determining module is used for acquiring a second characteristic factor corresponding to the product recommendation request, determining a second characteristic of the user to be recommended based on the second characteristic factor and the second historical data, and inputting the second characteristic into an attribute analysis model to obtain a target attribute value of the user to be recommended;
and the recommending module is used for recommending the target product for the user to be recommended based on the product permeability array corresponding to the target user group when the target attribute value is smaller than a preset threshold value.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a product recommendation program executable by the at least one processor to enable the at least one processor to perform the product recommendation method of any of claims 1-7.
10. A computer-readable storage medium having a product recommendation program stored thereon, the product recommendation program being executable by one or more processors to implement the product recommendation method of any one of claims 1-7.
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| CN113158053A (en) * | 2021-04-25 | 2021-07-23 | 平安科技（深圳）有限公司 | Service product recommendation method and device, computer equipment and storage medium |
| CN113407857A (en) * | 2021-08-19 | 2021-09-17 | 明品云(北京)数据科技有限公司 | Insurance recommendation method, system, device and medium |
| CN113515703A (en) * | 2021-07-09 | 2021-10-19 | 深圳壹账通智能科技有限公司 | Information recommendation method and device, electronic equipment and readable storage medium |
| CN113627653A (en) * | 2021-07-14 | 2021-11-09 | 深圳索信达数据技术有限公司 | Method and device for determining activity prediction strategy of mobile banking user |
| WO2022089190A1 (en) * | 2020-11-02 | 2022-05-05 | 深圳壹账通智能科技有限公司 | Product recommendation method and apparatus, and electronic device and readable storage medium |
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| CN106919647B (en) * | 2017-01-17 | 2020-10-30 | 浙江工业大学 | Clustering-based network structure similarity recommendation method |
| CN108492194A (en) * | 2018-03-06 | 2018-09-04 | 平安科技（深圳）有限公司 | Products Show method, apparatus and storage medium |
| CN109447728A (en) * | 2018-09-07 | 2019-03-08 | 平安科技（深圳）有限公司 | Financial product recommended method, device, computer equipment and storage medium |
| CN110825723B (en) * | 2019-10-09 | 2023-04-25 | 上海电力大学 | Resident user classification method based on electricity load analysis |
| CN112148995A (en) * | 2020-11-02 | 2020-12-29 | 深圳壹账通智能科技有限公司 | Product recommendation method and device, electronic equipment and readable storage medium |
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| WO2022089190A1 (en) * | 2020-11-02 | 2022-05-05 | 深圳壹账通智能科技有限公司 | Product recommendation method and apparatus, and electronic device and readable storage medium |
| CN113158053A (en) * | 2021-04-25 | 2021-07-23 | 平安科技（深圳）有限公司 | Service product recommendation method and device, computer equipment and storage medium |
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| CN113515703A (en) * | 2021-07-09 | 2021-10-19 | 深圳壹账通智能科技有限公司 | Information recommendation method and device, electronic equipment and readable storage medium |
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