CN113362106A - Underwear type recommendation method and device based on big data mining - Google Patents

Underwear type recommendation method and device based on big data mining Download PDF

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CN113362106A
CN113362106A CN202110612893.5A CN202110612893A CN113362106A CN 113362106 A CN113362106 A CN 113362106A CN 202110612893 A CN202110612893 A CN 202110612893A CN 113362106 A CN113362106 A CN 113362106A
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underwear
user
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measurement data
current user
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周滢滢
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Atog Health Technology Beijing Co ltd
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Abstract

The invention provides an underwear type recommendation method and device based on big data mining, wherein the method comprises the following steps: acquiring chest measurement data of a current user; determining recommended mold parameters corresponding to the chest measurement data of the current user based on the mapping relation between the chest measurement data and the underwear mold parameters, wherein the mapping relation is determined based on the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data; and recommending the underwear type to the current user based on the recommended mold parameters of the current user. According to the method and the device provided by the invention, the recommended mold parameters corresponding to the chest measurement data of the current user are determined by mining the mapping relation, and then the underwear type is recommended, the underwear with a fitted shape can be obtained without repeated try-on, and the time cost for choosing the underwear is reduced. The introduction and the reference of historical data abandon the subjectivity of manually recommending the underwear type, and further improve the accuracy and the recommendation efficiency of recommending the underwear type.

Description

Underwear type recommendation method and device based on big data mining
Technical Field
The invention relates to the technical field of computers, in particular to an underwear type recommendation method and device based on big data mining.
Background
The function of the undergarment is to provide external support to the breasts and increase the comfort of the wearer. The wearer typically needs to purchase underwear that fits his body from various types of underwear that are commercially available.
However, most of the underwear sold in the market are made by uniform plate making, only the sizes of the underwear are different, the underwear which is suitable for purchasing needs to be repeatedly tried on the spot, and a lot of time is consumed for purchasing and trying on the underwear on the spot, and even then the underwear which is completely suitable cannot be found.
Disclosure of Invention
The invention provides an underwear form recommending method and device based on big data mining, which are used for solving the problems that a large amount of time is consumed for choosing underwear and the underwear form chosen cannot be attached to a wearer.
The invention provides an underwear type recommendation method based on big data mining, which comprises the following steps:
acquiring chest measurement data of a current user;
determining recommended mold parameters corresponding to the chest measurement data of the current user based on a mapping relation between the chest measurement data and underwear mold parameters, wherein the mapping relation is determined based on the chest measurement data of historical users and the underwear mold parameters matched with the chest measurement data;
and recommending the underwear type to the current user based on the recommended mold parameters of the current user.
According to the underwear type recommendation method based on big data mining provided by the invention, the recommended mold parameter corresponding to the chest measurement data of the current user is determined based on the mapping relation between the chest measurement data and the underwear mold parameter, and the method comprises the following steps:
determining recommended mold parameters corresponding to chest measurement data of the current user based on a mapping relation corresponding to the user type to which the current user belongs;
the mapping relation corresponding to the user type of the current user is determined based on chest measurement data of historical users of the same user type as the current user and underwear mold parameters matched with the chest measurement data.
According to the underwear type recommendation method based on big data mining provided by the invention, the method for determining the type of the user to which the current user belongs comprises the following steps:
acquiring user information of the current user, wherein the user information comprises at least one of user age, fertility status, lactation status and wearing habits;
and determining the user type of the current user based on the user information of the current user.
According to the underwear type recommendation method provided by the invention, the determining of the user type of the current user based on the user information of the current user comprises the following steps:
encoding the user information of the current user to obtain the portrait characteristics of the current user;
calculating the similarity between the portrait characteristics of the current user and the type characteristics of each candidate user type, and taking the candidate user type corresponding to the type characteristic with the highest similarity as the user type of the current user;
the type feature of each candidate user type is determined based on each user cluster obtained by carrying out unsupervised clustering on the portrait features of each sample user.
According to the underwear type recommendation method based on big data mining provided by the invention, the determination method of the mapping relation comprises the following steps:
based on the chest measurement data of the historical user and the underwear die parameters matched with the chest measurement data, performing association mining on each chest measurement data and each underwear die parameter to obtain the associated underwear die parameters of each chest measurement data;
selecting a mapping data set of each chest measurement data and the related underwear die parameters from the chest measurement data of the historical user and the underwear die parameters matched with the chest measurement data;
and establishing the mapping relation based on the mapping data set of each chest measurement data and the related underwear mould parameters.
According to the underwear type recommendation method based on big data mining provided by the invention, the acquisition of chest measurement data of a current user comprises the following steps:
and if the recommended mold parameters of the current user are not inquired or a parameter updating request of the current user is received, acquiring chest measurement data of the current user.
According to the underwear type recommendation method based on big data mining provided by the invention, the underwear type is recommended to the current user, and then the method further comprises the following steps:
receiving feedback information of the current user;
based on the feedback information, adjusting the recommended mold parameters of the current user;
updating the mapping relationship based on the chest measurement data of the current user and the adjusted recommended mold parameters.
The invention provides an underwear type recommendation device based on big data mining, which comprises:
the data acquisition unit is used for acquiring chest measurement data of a current user;
the mapping unit is used for determining recommended mold parameters corresponding to the chest measurement data of the current user based on the mapping relation between the chest measurement data and the underwear mold parameters, and the mapping relation is determined based on the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data;
and the recommending unit is used for recommending the underwear type to the current user based on the recommended mold parameters of the current user.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the underwear type recommendation method based on big data mining.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when being executed by a processor, realizes the steps of the big data mining based underwear type recommendation method according to any one of the above.
According to the underwear type recommendation method and device based on big data mining, provided by the invention, the recommended mold parameters corresponding to the chest measurement data of the current user are determined by mining the mapping relation obtained by the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data, so that the underwear type recommendation is carried out, the user can obtain underwear fitting with the shape of the user without repeatedly trying on the underwear, and the time cost for the user to select and purchase underwear is greatly reduced. In addition, the introduction and the reference of historical data abandon the subjectivity of a platemaker for manually recommending the underwear type, ensure the reliability and the objectivity of the recommended type and contribute to further improving the accuracy and the recommendation efficiency of the underwear type recommendation.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of an underwear type recommendation method provided by the invention;
FIG. 2 is a schematic flow chart of a mapping relationship determination method provided by the present invention;
FIG. 3 is a schematic representation of chest measurement data provided by the present invention;
FIG. 4 is a second schematic diagram of chest measurement data provided by the present invention;
FIG. 5 is a third schematic diagram of thoracic measurements provided by the present invention;
FIG. 6 is a fourth schematic diagram of chest measurement data provided by the present invention;
FIG. 7 is a schematic structural diagram of an underwear type recommendation device provided by the invention;
fig. 8 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Most of the underwear sold in the market are made in a unified way, only the sizes of the underwear are different, the underwear which is suitable for being purchased needs to be repeatedly tried on the spot, and a large amount of time is consumed for both purchasing the underwear on the spot and trying on the underwear, but even so, the underwear which is completely suitable cannot be found. In order to solve the problem, the embodiment of the invention provides an underwear type recommendation method. Fig. 1 is a schematic flow chart of an underwear type recommendation method provided by the present invention, as shown in fig. 1, the method includes:
step 110, chest measurement data of the current user is acquired.
The current user referred to herein is the user who needs to make underwear style recommendations. The chest measurement data of the user is obtained by measurement, and specifically, the chest measurement data may be input by a professional measuring person after the user arrives at a store, or may be input by the user after the user measures the body, and this is not particularly limited in the embodiment of the present invention.
Further, the breast measurement data may include at least one of left and right breast inner radius vertical data, left and right breast inner radius fit data, left and right breast outer radius vertical data, left and right breast outer radius fit data, left and right breast upper radius vertical data, left and right breast upper radius fit data, left and right breast lower radius vertical data, left and right breast lower radius fit data, left and right breast height data, double breast nipple interval data, left and right breast lockstep distance, left and right breast shoulder distance, lower breast circumference data, and upper breast circumference data.
And 120, determining a recommended mold parameter corresponding to the chest measurement data of the current user based on the mapping relation between the chest measurement data and the underwear mold parameter, wherein the mapping relation is determined based on the chest measurement data of the historical user and the underwear mold parameter matched with the chest measurement data.
Specifically, in consideration of the fact that different people have great differences in body types, compared with the scheme that the traditional underwear is made into a plate in a unified mode and only distinguished on the size, the underwear is split into the multiple components based on the concept of one person and one plate, and the corresponding die parameters are set for the components, so that the underwear fits the body types of the users on a finer granularity, and the reasonability and comfortableness of the recommended underwear type are guaranteed. The underwear components comprise cups, steel supports, hearts, rear ratios, earrings, oxter pieces, shoulder straps and the like, correspondingly, the underwear die parameters comprise cup parameters, steel support parameters, hearts parameters, rear ratios, earring parameters, oxter piece parameters, shoulder strap parameters and the like, wherein the cup parameters can comprise sizes, softnesses, thicknesses, materials, shapes and the like of the cups, the steel support parameters can comprise sizes, softnesses, materials and the like of the steel supports, the heart parameters can comprise sizes, widths and the like of the hearts, the rear ratios can comprise sizes, shapes and the like of the rear ratios, the earrings and the oxter piece parameters can be the presence or absence of the earrings and the oxter pieces and the like, and the shoulder strap parameters can be the widths, the lengths, the lifting and pulling point positions of the shoulder straps and the like.
After the chest measurement data of the current user are obtained, the chest measurement data of the current user can be mapped to the corresponding underwear mold parameters based on the mapping relation between the chest measurement data and the underwear mold parameters which are obtained in advance, so that the underwear mold parameters which can be recommended to the current user, namely the recommended mold parameters, are obtained.
The mapping relation between the chest measurement data and the underwear mold parameters is obtained by performing correlation mining on the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data. Before step 120 is executed, a mapping relationship between the chest measurement data and the underwear mold parameters may be obtained, and the specific obtaining manner includes: the method comprises the steps of firstly collecting chest measurement data of a historical user and underwear mold parameters matched with the chest measurement data, wherein the chest measurement data of the historical user can be obtained by measuring the chest measurement data in a store or obtained by measuring the chest measurement data of the historical user, the underwear mold parameters matched with the historical user can be determined through ordering records of the historical user, and information of all components of underwear finally selected by the historical user can be arranged into the underwear mold parameters matched with the underwear mold parameters. On the basis, the mapping relation between the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data can be mined, and the mapping relation is used as the mapping relation between the chest measurement data and the underwear mold parameters.
Furthermore, the mining of the mapping relation can be that the chest measurement data of the historical user and the underwear mold parameters adapted to the chest measurement data are used as training samples of the neural network model, the neural network model is trained, the trained neural network model is used as the embodiment of the mapping relation, and the chest measurement data of the current user are correspondingly input into the trained neural network model, so that the recommended mold parameters output by the neural network model can be obtained; the mining of the mapping relationship may also be to perform regression analysis based on the chest measurement data of the historical user and the underwear mold parameters adapted thereto, and use a function obtained by the regression analysis as the mapping relationship, which is not specifically limited in the embodiment of the present invention.
And step 130, recommending the underwear type to the current user based on the recommended mold parameters of the current user.
Specifically, after the recommended mold parameters are obtained, the underwear style obtained based on the combination of the recommended mold parameters may be recommended to the current user, so that the current user can determine whether to customize the underwear style based on the recommended underwear style, or the current user can propose a modification suggestion based on the recommended underwear style. And after the user confirms, customizing the underwear based on the recommended underwear type.
Further, the recommending of the underwear type to the current user may be to splice all underwear components corresponding to the recommended mold parameters into a set of solid underwear for the current user to try on, or to form an underwear three-dimensional model corresponding to the type based on the recommended mold parameters, and to match the underwear three-dimensional model with the human body three-dimensional model of the current user to simulate a wearing effect for reference by the current user, which is not specifically limited in the embodiment of the present invention.
According to the method provided by the embodiment of the invention, the recommended mold parameters corresponding to the chest measurement data of the current user are determined by mining the mapping relation obtained by the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data, and then the underwear type is recommended, so that the underwear fitting with the body of the user can be obtained without repeated try-on of the user, and the time cost for the user to buy the underwear is greatly reduced. In addition, the introduction and the reference of historical data abandon the subjectivity of a platemaker for manually recommending the underwear type, ensure the reliability and the objectivity of the recommended type and contribute to further improving the accuracy and the recommendation efficiency of the underwear type recommendation.
Based on the above embodiment, step 120 includes:
determining recommended mold parameters corresponding to chest measurement data of the current user based on a mapping relation corresponding to the user type to which the current user belongs; the mapping relation corresponding to the user type of the current user is determined based on chest measurement data of historical users belonging to the same user type as the current user and underwear mold parameters matched with the chest measurement data.
Specifically, different users may have different requirements for the function and wearing experience of underwear, for example, the preferences of the users themselves may be different, some users prefer the underwear of the body-shaping adjustment type, some users pursue the comfort of the underwear, prefer the underwear without steel rings, and for example, whether the users have a child or not and whether the users breast feed or not can affect the chest state of the users, thereby affecting the functional requirements of the users for the underwear.
In consideration of the situation, the users can be classified in advance, and the corresponding mapping relation is set for different user types, so that more suitable underwear styles can be recommended for different types of users in a more targeted manner. The user type referred to herein may be divided according to user preference, such as preference of comfortable users, preference of body-shaping users, preference of balance users, or may be divided according to user body status, such as lactation users, pregnancy users, and the like, which is not specifically limited in this embodiment of the present invention.
Correspondingly, the mapping relation of different user types is constructed only based on the chest measurement data of each historical user under the corresponding user type and the underwear mold parameters matched with the chest measurement data, so that the obtained mapping relation can fully reflect the preference of the corresponding user type and can more easily meet the requirements of the corresponding user type.
Based on any of the above embodiments, the method for determining the user type to which the current user belongs includes:
acquiring user information of a current user, wherein the user information comprises at least one of user age, fertility status, lactation status and wearing habits;
and determining the user type of the current user based on the user information of the current user.
Specifically, the user information includes information related to the chest condition and/or underwear function requirement of the user, wherein the age of the user may be a specific age of the user or an age group to which the user belongs, the birth status may be whether the user has been born or not, or the number of times of the user has been born, the birth status may be whether the user has been born or not, whether the user is currently in the lactation period or not, or the time for finishing the lactation period, and the wearing habits may include the size of underwear which is frequently worn, whether the user often wears steel-ring-free underwear, whether the user often wears body-shaping underwear, like the width of shoulder straps, like the width of row buckles, like the degree of tightness, like the thickness of cups, and the like.
By comprehensively considering the user information, the chest condition of the user and the corresponding underwear function requirements can be comprehensively known, so that the user classification is realized. Here, the specific classification manner may be to collect user information of a large number of sample users in advance, perform user classification on the part of sample users manually to obtain a sample user type of each sample user, and then train a user classification model using the user information of the sample user and the sample user type as training samples to realize classification of each subsequent user, or may also set a classification rule for each item of user information, and perform user classification based on the classification rule, which is not specifically limited in the embodiment of the present invention.
Based on any of the above embodiments, the determining the user type of the current user based on the user information of the current user includes:
encoding user information of a current user to obtain portrait characteristics of the current user;
calculating the similarity between the portrait characteristics of the current user and the type characteristics of each candidate user type, and taking the candidate user type corresponding to the type characteristic with the highest similarity as the user type of the current user;
the type feature of each candidate user type is determined based on each user cluster obtained by carrying out unsupervised clustering on the portrait features of each sample user.
Specifically, the user information includes information of each dimension, and user classification directly performed from the dimensions of age group, birth or lactation, and the like will generate a large number of user types, some of the user types may be relatively close to each other, and the large number of user types will cause a sudden increase in calculation amount when the mapping relationship is obtained.
Aiming at the problem, the portrait characteristics of a large number of sample users can be subjected to unsupervised clustering, so that a plurality of user types which are relatively reliable and have distinguished shapes are obtained, and the calculation amount is effectively reduced while the accurate classification of the users is ensured. The portrait features are obtained by coding user information of the application user, and the unsupervised clustering can be realized by a K-means algorithm, a DBSCAN algorithm and other clustering algorithms.
After the portrait features of the sample user are clustered, a plurality of clusters can be obtained, each cluster corresponds to one user type, and the mean value of all portrait features in a single cluster can be used as the clustering center of the cluster, namely the type feature corresponding to the user type.
On the basis, aiming at the current user, the user information of the current user can be coded to serve as the portrait characteristics of the current user, and the similarity between the portrait characteristics and the type characteristics of each candidate user type is calculated, so that the candidate user type with the highest similarity is selected to serve as the user type of the current user. Here, the similarity between the image feature and the type feature may be calculated by a common similarity calculation method such as euclidean distance and cosine similarity.
According to the method provided by the embodiment of the invention, each candidate user type is determined through unsupervised clustering, so that the reliability of user type division is ensured while massive calculation caused by fine-grained classification is avoided; on the basis, the type of the current user is divided through similarity calculation, so that the accuracy of user classification is guaranteed, and the pertinence and the reliability of underwear type recommendation are improved.
Based on any of the above embodiments, fig. 2 is a schematic flow chart of a method for determining a mapping relationship provided by the present invention, and as shown in fig. 2, the method for determining a mapping relationship includes:
and step 210, performing association mining on each chest measurement data and each underwear mold parameter based on the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data to obtain the associated underwear mold parameters of each chest measurement data.
The association mining referred to herein is a data mining approach that can mine implicit relationships between objects from large-scale data. Specifically, in the embodiment of the invention, the association mining is used for mining the implicit association relationship between each chest measurement data and each underwear mold parameter from the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data, so as to obtain the associated underwear mold parameters of each chest measurement data. Here, for any chest measurement data, the associated undergarment mold parameters, i.e., the undergarment mold parameters that have an implicit relationship with the chest measurement data, for example, the associated undergarment mold parameters for the chest measurement data "breast height" include "cup thickness".
Further, the algorithm used for association mining herein may be Apriori algorithm, FP-growth algorithm, or the like.
Step 220, selecting a mapping data set of each chest measurement data and the related underwear mold parameters from the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data.
Step 230, a mapping relationship is established based on the mapping data sets of the chest measurement data and the associated underwear mold parameters.
Specifically, after obtaining the associated undergarment mold parameters of each chest measurement data, for any chest measurement data and one associated undergarment mold parameter corresponding thereto, a data pair of the chest measurement data and the associated undergarment mold parameter can be selected from a large amount of historical data and used as a mapping data set of the chest measurement data and the associated undergarment mold parameter. On the basis, the mapping relation between the two words can be established through the modes of neural network training, regression analysis and the like.
And finally, integrating the mapping relation between all the chest measurement data and the related underwear mold parameters thereof to serve as the mapping relation between the chest measurement data and the underwear mold parameters.
For example, for the breast measurement data "breast height" and its associated underwear mold parameter "cup thickness", the breast height of each historical user and its purchased cup thickness may be selected from the breast measurement data of the historical user and its adapted underwear mold parameter to construct a mapping data set as the breast measurement data "breast height" and its associated underwear mold parameter "cup thickness". For the mapping data sets of the breast height and the cup thickness, the mapping relationship between the breast height and the cup thickness can be obtained, for example, the corresponding cup is a thick cup when the breast height is larger, and the corresponding cup is a thin cup when the breast height is larger.
Based on any of the above embodiments, step 110 includes:
and if the recommended mold parameters of the current user are not inquired or a parameter updating request of the current user is received, acquiring chest measurement data of the current user.
Specifically, when recommending the underwear type to the current user, it may be firstly queried whether the recommended mold parameters of the user are stored. The recommended mold parameters specifically queried here may be mold parameters of underwear previously purchased by the current user, or may be recommended mold parameters previously determined according to chest measurement data of the current user. If the recommended mold parameters of the current user are obtained through query, the chest measurement data of the current user do not need to be obtained again, and the underwear type can be directly recommended to the user based on the recommended mold parameters obtained through query, so that unnecessary workload brought to the system by repeated calculation is avoided. And if the recommended mold parameters of the current user cannot be inquired, obtaining chest measurement data of the current user, and further determining the recommended mold parameters to recommend the underwear type based on the chest measurement data and the mapping relation.
In addition, there is another situation that the user does not store his chest measurement data, or the chest measurement data stored originally needs to be changed due to an error in the previous measurement, a change in the user's body type, and the like, and at this time, the user may initiate a parameter update request. The parameter update request is used to trigger the update of the chest parameter of the corresponding user, and the parameter update request may be only used to trigger the update of the parameter, or may carry the chest measurement data that needs to be updated. After receiving the parameter updating request, the system updates and acquires chest measurement data of the current user, and determines the recommended mold parameters again based on the updated chest measurement data, so that the underwear type recommended to the user can be matched with the actual situation of the user.
Based on any of the above embodiments, step 130 further includes:
receiving feedback information of a current user;
based on the feedback information, adjusting the recommended mold parameters of the current user;
and updating the mapping relation based on the chest measurement data of the current user and the adjusted recommended mold parameters.
Specifically, the feedback information is a feedback suggestion given by the current user for the recommended underwear form, the current user may return the feedback information after the fitting is completed, or the feedback information may return according to the simulated wearing effect of the recommended underwear form, which is not specifically limited in the embodiment of the present invention.
The feedback information carries the problems of the recommended underwear model indicated by the current user and/or adjustment suggestions aiming at the underwear model, such as 'chest pressing' and 'shoulder belt growing', and for example 'changing into a soft cup', and after the feedback information is received, the recommended mold parameters can be adjusted according to the problems or suggestions contained in the feedback information, so that the underwear model formed by the adjusted recommended mold parameters can better meet the requirements of the current user.
On the basis, the chest measurement data of the current user and the adjusted recommended mold parameters can be used as a group of chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data, and the chest measurement data and the underwear mold parameters are applied to updating of the mapping relation between the chest measurement data and the underwear mold parameters, so that the mapping relation can be updated in real time in the underwear type recommending process, and the recommended underwear type can be more fit with the body type of the user.
Based on any of the above embodiments, fig. 3, 4, 5, and 6 are schematic diagrams of measured data of the breast provided by the present invention, and the dashed lines in the diagrams represent contours of the breast. Fig. 3 and 4 reflect right and left milk inner radius vertical data, right and left milk inner radius fit data, right and left milk outer radius vertical data, right and left milk outer radius fit data, right and left milk upper radius vertical data, right and left milk upper radius fit data, right and left milk lower radius vertical data, right and left milk lower radius fit data, where a1 is milk inner radius vertical, a2 is milk outer radius vertical, a3 is milk upper radius vertical, a4 is milk lower radius vertical, b1 is milk inner radius fit, b2 is milk outer radius fit, b3 is milk upper radius fit, and b4 is milk lower radius fit. For example, the left and right milk inner radius vertical data may include a measurement of the left milk inner radius vertical a1 and a measurement of the right milk inner radius vertical a1, the left and right milk inner radius hugging data may include a measurement of the left milk inner radius hugging b1 and a measurement of the right milk inner radius hugging b1, the difference in the measurements of vertical and hugging may be seen in fig. 4. Fig. 5 shows left and right breast height data, fig. 6 shows data of the distance between the nipples of both breasts, the distance between the breast centers of the left and right breasts, and the distance between the breast shoulders of the left and right breasts, wherein the distance d1 between the breasts and the breast center is measured, the distance d2 between the breast centers is the linear distance from the clavicle groove to the breast point, the distance d3 between the breast point and the breast shoulder is vertical, the breast height h is the breast height, and the tape is placed horizontally and is vertical to the body. The upper chest circumference data is the measurement result of the fact that the body is inclined forward by 45 degrees, the circumference is measured horizontally for one circle, and the tape passes through the breast point; the lower chest circumference data is the measurement result of raising head, straightening chest, measuring horizontal circumference for a circle and passing through the lower breast root point by a tape.
Based on any embodiment, fig. 7 is a schematic structural diagram of an underwear type recommendation device provided by the invention, as shown in fig. 7, the device comprises:
a data acquisition unit 310 for acquiring chest measurement data of a current user;
the mapping unit 320 is configured to determine a recommended mold parameter corresponding to the chest measurement data of the current user based on a mapping relationship between the chest measurement data and an underwear mold parameter, where the mapping relationship is determined based on the chest measurement data of the historical user and the underwear mold parameter adapted to the historical user;
a recommending unit 330, configured to recommend the underwear type to the current user based on the recommended mold parameter of the current user.
According to the device provided by the embodiment of the invention, the recommended mold parameters corresponding to the chest measurement data of the current user are determined by mining the mapping relation obtained by the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data, so that the underwear type is recommended, the underwear fitting with the body of the user can be obtained without repeated try-on of the user, and the time cost for the user to buy the underwear is greatly reduced. In addition, the introduction and the reference of historical data abandon the subjectivity of a platemaker for manually recommending the underwear type, ensure the reliability and the objectivity of the recommended type and contribute to further improving the accuracy and the recommendation efficiency of the underwear type recommendation.
Based on any of the above embodiments, the mapping unit 320 is configured to:
determining recommended mold parameters corresponding to chest measurement data of the current user based on a mapping relation corresponding to the user type to which the current user belongs;
the mapping relation corresponding to the user type of the current user is determined based on chest measurement data of historical users of the same user type as the current user and underwear mold parameters matched with the chest measurement data.
Based on any embodiment above, the apparatus further comprises:
the user classifying unit is used for acquiring user information of the current user, wherein the user information comprises at least one of user age, fertility status, lactation status and wearing habits; and determining the user type of the current user based on the user information of the current user.
Based on any of the above embodiments, the user classifying unit is configured to:
encoding the user information of the current user to obtain the portrait characteristics of the current user;
calculating the similarity between the portrait characteristics of the current user and the type characteristics of each candidate user type, and taking the candidate user type corresponding to the type characteristic with the highest similarity as the user type of the current user;
the type feature of each candidate user type is determined based on each user cluster obtained by carrying out unsupervised clustering on the portrait features of each sample user.
Based on any embodiment above, the apparatus further comprises:
the mapping unit determining unit is used for performing association mining on the chest measurement data and the underwear die parameters based on the chest measurement data of the historical user and the underwear die parameters matched with the chest measurement data to obtain the associated underwear die parameters of the chest measurement data;
selecting a mapping data set of each chest measurement data and the related underwear die parameters from the chest measurement data of the historical user and the underwear die parameters matched with the chest measurement data;
and establishing the mapping relation based on the mapping data set of each chest measurement data and the related underwear mould parameters.
Based on any of the above embodiments, the data obtaining unit 310 is configured to:
and if the recommended mold parameters of the current user are not inquired or a parameter updating request of the current user is received, acquiring chest measurement data of the current user.
Based on any embodiment above, the apparatus further comprises:
a feedback adjusting unit, configured to receive feedback information of the current user;
based on the feedback information, adjusting the recommended mold parameters of the current user;
updating the mapping relationship based on the chest measurement data of the current user and the adjusted recommended mold parameters.
Fig. 8 illustrates a physical structure diagram of an electronic device, and as shown in fig. 8, the electronic device may include: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform an underwear type recommendation method comprising: acquiring chest measurement data of a current user; determining recommended mold parameters corresponding to the chest measurement data of the current user based on a mapping relation between the chest measurement data and underwear mold parameters, wherein the mapping relation is determined based on the chest measurement data of historical users and the underwear mold parameters matched with the chest measurement data; and recommending the underwear type to the current user based on the recommended mold parameters of the current user.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform the method for recommending the type of underwear provided by the above methods, the method comprising: acquiring chest measurement data of a current user; determining recommended mold parameters corresponding to the chest measurement data of the current user based on a mapping relation between the chest measurement data and underwear mold parameters, wherein the mapping relation is determined based on the chest measurement data of historical users and the underwear mold parameters matched with the chest measurement data; and recommending the underwear type to the current user based on the recommended mold parameters of the current user.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the underwear type recommendation method provided above, the method comprising: acquiring chest measurement data of a current user; determining recommended mold parameters corresponding to the chest measurement data of the current user based on a mapping relation between the chest measurement data and underwear mold parameters, wherein the mapping relation is determined based on the chest measurement data of historical users and the underwear mold parameters matched with the chest measurement data; and recommending the underwear type to the current user based on the recommended mold parameters of the current user.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. An underwear type recommendation method based on big data mining is characterized by comprising the following steps:
acquiring chest measurement data of a current user, wherein the chest measurement data comprises at least one of left and right breast inner radius vertical data, left and right breast inner radius close data, left and right breast outer radius vertical data, left and right breast outer radius close data, left and right breast upper radius vertical data, left and right breast upper radius close data, left and right breast lower radius vertical data, left and right breast lower radius close data, left and right breast height data, double breast nipple interval data, left and right breast lock center distance, left and right breast shoulder distance, lower chest circumference data and upper chest circumference data;
determining recommended mold parameters corresponding to the chest measurement data of the current user based on a mapping relation between the chest measurement data and underwear mold parameters, wherein the mapping relation is determined based on the chest measurement data of historical users and the underwear mold parameters matched with the chest measurement data;
and recommending the underwear type to the current user based on the recommended mold parameters of the current user.
2. The method for recommending underwear types based on big data mining according to claim 1, wherein the determining the recommended mold parameters corresponding to the chest measurement data of the current user based on the mapping relationship between the chest measurement data and the underwear mold parameters comprises:
determining recommended mold parameters corresponding to chest measurement data of the current user based on a mapping relation corresponding to the user type to which the current user belongs;
the mapping relation corresponding to the user type of the current user is determined based on chest measurement data of historical users of the same user type as the current user and underwear mold parameters matched with the chest measurement data.
3. The big data mining-based underwear type recommendation method according to claim 2, wherein the method for determining the user type to which the current user belongs comprises the following steps:
acquiring user information of the current user, wherein the user information comprises at least one of user age, fertility status, lactation status and wearing habits;
and determining the user type of the current user based on the user information of the current user.
4. The big data mining-based underwear type recommendation method according to claim 3, wherein the determining the user type of the current user based on the user information of the current user comprises:
encoding the user information of the current user to obtain the portrait characteristics of the current user;
calculating the similarity between the portrait characteristics of the current user and the type characteristics of each candidate user type, and taking the candidate user type corresponding to the type characteristic with the highest similarity as the user type of the current user;
the type feature of each candidate user type is determined based on each user cluster obtained by carrying out unsupervised clustering on the portrait features of each sample user.
5. The big data mining-based underwear type recommendation method according to any one of claims 1-4, wherein the mapping relation determination method comprises the following steps:
based on the chest measurement data of the historical user and the underwear die parameters matched with the chest measurement data, performing association mining on each chest measurement data and each underwear die parameter to obtain the associated underwear die parameters of each chest measurement data;
selecting a mapping data set of each chest measurement data and the related underwear die parameters from the chest measurement data of the historical user and the underwear die parameters matched with the chest measurement data;
and establishing the mapping relation based on the mapping data set of each chest measurement data and the related underwear mould parameters.
6. The big data mining based underwear type recommendation method according to any one of claims 1-4, wherein the obtaining chest measurement data of a current user comprises:
and if the recommended mold parameters of the current user are not inquired or a parameter updating request of the current user is received, acquiring chest measurement data of the current user.
7. The big data mining-based underwear type recommendation method according to any one of claims 1-4, wherein the recommending the underwear type to the current user is further followed by:
receiving feedback information of the current user;
based on the feedback information, adjusting the recommended mold parameters of the current user;
updating the mapping relationship based on the chest measurement data of the current user and the adjusted recommended mold parameters.
8. An underwear type recommendation device based on big data mining, comprising:
the data acquisition unit is used for acquiring chest measurement data of a current user, wherein the chest measurement data comprises at least one of left and right breast inner radius vertical data, left and right breast inner radius clinging data, left and right breast outer radius vertical data, left and right breast outer radius clinging data, left and right breast upper radius vertical data, left and right breast upper radius clinging data, left and right breast lower radius vertical data, left and right breast lower radius clinging data, left and right breast height data, double breast nipple interval data, left and right breast lock heart distance, left and right breast shoulder distance, lower chest circumference data and upper chest circumference data;
the mapping unit is used for determining recommended mold parameters corresponding to the chest measurement data of the current user based on the mapping relation between the chest measurement data and the underwear mold parameters, and the mapping relation is determined based on the chest measurement data of the historical user and the underwear mold parameters matched with the chest measurement data;
and the recommending unit is used for recommending the underwear type to the current user based on the recommended mold parameters of the current user.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the big data mining based underwear version recommendation method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the big data mining based underwear type recommendation method according to any one of claims 1 to 7.
CN202110612893.5A 2021-06-02 2021-06-02 Underwear type recommendation method and device based on big data mining Pending CN113362106A (en)

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