CN117172832B - Intelligent recommendation method for collagen peptide health products based on machine learning - Google Patents
Intelligent recommendation method for collagen peptide health products based on machine learning Download PDFInfo
- Publication number
- CN117172832B CN117172832B CN202311450551.3A CN202311450551A CN117172832B CN 117172832 B CN117172832 B CN 117172832B CN 202311450551 A CN202311450551 A CN 202311450551A CN 117172832 B CN117172832 B CN 117172832B
- Authority
- CN
- China
- Prior art keywords
- target user
- target
- user
- product
- health
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000036541 health Effects 0.000 title claims abstract description 277
- 108090000765 processed proteins & peptides Proteins 0.000 title claims abstract description 112
- 102000008186 Collagen Human genes 0.000 title claims abstract description 111
- 108010035532 Collagen Proteins 0.000 title claims abstract description 111
- 229920001436 collagen Polymers 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000010801 machine learning Methods 0.000 title claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 10
- 230000000875 corresponding effect Effects 0.000 claims description 314
- 230000008520 organization Effects 0.000 claims description 20
- 230000002596 correlated effect Effects 0.000 claims description 9
- 239000013598 vector Substances 0.000 claims description 8
- 230000006870 function Effects 0.000 claims description 7
- 238000010586 diagram Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 3
- 239000000047 product Substances 0.000 description 282
- 241001465754 Metazoa Species 0.000 description 7
- 238000002372 labelling Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000000694 effects Effects 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 241000894007 species Species 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005429 filling process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the technical field of electric digital data processing, in particular to an intelligent collagen peptide health care product recommendation method based on machine learning, which comprises the following steps: acquiring user information corresponding to each historical user in a user to be recommended and a historical user set; forming a target user group by using target users with the same occupation in all target users; analyzing and processing the working intensity of the occupation of each target user group; determining a physical strain index corresponding to each target user; determining the usage dissimilarity of the health products between every two target users; clustering all target users according to the body strain index, the professional work intensity and the dissimilarity of health care product use; and recommending the collagen peptide health products to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs. According to the invention, through data processing on all user information, the accuracy of recommending the collagen peptide health care products to the user is improved.
Description
Technical Field
The invention relates to the technical field of electric digital data processing, in particular to an intelligent recommendation method for collagen peptide health products based on machine learning.
Background
The peptide health product is a skin care health product. When a human body constructs cells by oneself, collagen peptide is often required to be used as a raw material, and researches show that collagen peptide below 1000 daltons can be absorbed by the human body with high efficiency, so that the human body can use exogenous collagen peptide health care products to supplement the collagen peptide. Because of the variety of the collagen peptide health products, in order to facilitate the selection of the collagen peptide health products by users, the recommendation of the collagen peptide health products by users can be often performed based on machine learning. Currently, when recommending an item, the following methods are generally adopted: and recommending the articles to the user according to the article use information of the user.
However, when the collagen peptide health product recommendation is performed on the user according to the use information of the collagen peptide health product of the user, the following technical problems often exist:
when a user wants to try the type of collagen peptide health products which the user has not used, the user is often difficult to accurately recommend the collagen peptide health products based on the use information of the collagen peptide health products of the user, so that the accuracy of recommending the collagen peptide health products of the user is poor.
Disclosure of Invention
The summary of the invention is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. The summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In order to solve the technical problem of poor accuracy of recommending the collagen peptide health care products for users, the invention provides an intelligent recommending method of the collagen peptide health care products based on machine learning.
The invention provides an intelligent recommendation method of a collagen peptide health product based on machine learning, which comprises the following steps:
Acquiring user information corresponding to each historical user in a user to be recommended and a historical user set;
the users to be recommended and each history user are target users, and target users with the same occupation in all target users form a target user group;
According to the user information corresponding to all the target users in each target user group, carrying out working intensity analysis processing on the occupation of each target user group to obtain the professional working intensity corresponding to each target user group;
Determining a physical strain index corresponding to each target user according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs;
Determining the dissimilarity of health care product usage between every two target users according to health care product usage data contained in user information corresponding to all the target users;
clustering all target users according to the body strain index corresponding to all target users, the professional work intensity corresponding to all target user groups and the dissimilarity degree of all health care product usage, so as to obtain target cluster clusters;
and recommending the collagen peptide health products to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs.
Optionally, the analyzing the working intensity of the occupation to which each target user group belongs according to the user information corresponding to all the target users in each target user group to obtain the working intensity of the occupation corresponding to each target user group includes:
Determining an excess income proportion corresponding to each target user according to income data and minimum income standard of residence included in user information corresponding to each target user in the target user group;
Classifying target users in the target user group according to excess income proportions corresponding to all the target users in the target user group to obtain a preset number of reference categories;
Taking age data contained in user information corresponding to a target user in each reference category as an abscissa, and taking excess income proportion corresponding to the target user in the reference category as an ordinate, and making a scatter diagram corresponding to the reference category;
Performing straight line fitting on the scatter diagram corresponding to each reference category, determining a fitting straight line corresponding to each reference category, and obtaining a fitting straight line set corresponding to the target user group;
And determining the professional work intensity corresponding to the target user group according to the fitting straight line set.
Optionally, the determining, according to the income data and the minimum residential income standard included in the user information corresponding to each target user in the target user group, the excess income proportion corresponding to each target user includes:
And determining the ratio of the difference between the income data included in the user information corresponding to the target user and the minimum income standard of the residential area in the minimum income standard of the residential area included in the user information corresponding to the target user as the excess income ratio corresponding to the target user.
Optionally, the formula corresponding to the professional work intensity corresponding to the target user group is:
; wherein,/> is the professional work intensity corresponding to the i-th target user group; i is the sequence number of the target user group; the number of fitting lines in the fitting line set corresponding to the ith target user group is/; t and r are sequence numbers of fitting straight lines in the fitting straight line set corresponding to the ith target user group; Is the slope of the t fitting straight line in the fitting straight line set corresponding to the i target user group; the value of/() is the slope of the r-th fitting line in the fitting line set corresponding to the i-th target user group; the absolute value function is taken by the/> ; the/> is the intercept of the t-th fitting line in the fitting line set corresponding to the i-th target user group; the value of is the intercept of the r-th fitting line in the fitting line set corresponding to the i-th target user group; and/> is the mean of all the fit line intercepts in the fit line set corresponding to the ith target user group.
Optionally, the determining the physical strain index corresponding to each target user according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs includes:
And determining the physical strain index corresponding to each target user according to the age data, the excess income proportion corresponding to each target user and the professional work intensity and the fitting straight line set corresponding to the target user group to which each target user belongs, which are included in the user information corresponding to each target user.
Optionally, the formula corresponding to the physical strain index corresponding to the target user is:
; wherein,/> is the physical strain index corresponding to the jth target user in the ith target user group; i is the sequence number of the target user group; j is the sequence number of the target user in the ith target user group; the/> is the excess income proportion corresponding to the j-th target user in the i-th target user group; the/> is the professional work intensity corresponding to the i-th target user group; the value of the slope of all the fitting lines in the fitting line set corresponding to the ith target user group is denoted by/(); the/> is age data included in the user information corresponding to the j-th target user in the i-th target user group; the value of/() is the average value of all fitting straight line intercepts in the fitting straight line set corresponding to the ith target user group; the/> is the average value of the occupational work intensity corresponding to all the target user groups; the/> is the slope of a fitted straight line to which the excess income proportion corresponding to the jth target user in the ith target user group belongs; and/> is the intercept of the fitted line to which the excess revenue ratio corresponding to the jth target user in the ith target user group belongs.
Optionally, the determining the dissimilarity of health care product usage between every two target users according to the health care product usage data included in the user information corresponding to all the target users includes:
Determining a purchased health product information set which is corresponding to each target user and is included in health product use data corresponding to each target user, wherein the purchased health product information in the purchased health product information set comprises the following components: the product expenditure weight, the product source and the product organization are formed, and the information of one purchased health product corresponds to one collagen peptide health product;
And determining the dissimilarity of health care product use between every two target users according to the product expenditure weight, the product source and the product organization composition included in all the purchased health care product information in the purchased health care product information set corresponding to every two target users.
Optionally, the formula corresponding to the dissimilarity of the health products between the two target users is:
; wherein,/> is the dissimilarity in health care product usage between the h target user and the m target user; h and m are sequence numbers of the target users; the number of the purchased health product information in the purchased health product information set corresponding to the h target user is indicated by ';/>'; the number of the purchased health product information in the purchased health product information set corresponding to the mth target user is/; the/> is an absolute function; a is the serial number of the purchased health product information in the purchased health product information set corresponding to the h target user; b is the serial number of the purchased health product information in the purchased health product information set corresponding to the mth target user; the/> is the product expenditure weight included in the a-th purchased health product information in the purchased health product information set corresponding to the h-th target user; the/> is the product expenditure weight included in the b-th purchased health product information in the purchased health product information set corresponding to the m-th target user; setting/> as a first preset value when the product organization composition included in the a-th purchased health product information is the same as the product organization composition included in the b-th purchased health product information; setting/> to a second preset value when the product organization composition included in the a-th purchased health product information is different from the product organization composition included in the b-th purchased health product information; the first preset value is smaller than the second preset value; the value range of the grade of the product source is [1, Q ]; q represents the highest grade of the grades to which the source of the product belongs; setting/> as q-1 when the lowest grade of the product source included in the a-th purchased health product information and the product source included in the b-th purchased health product information is the q-th grade; the value range of q is [1, Q ]; when the source of the product included in the a-th purchased health product information is different from the source of the product included in the b-th purchased health product information in all grades, setting/> as Q.
Optionally, clustering all target users according to the physical strain index corresponding to all target users, the professional work intensity corresponding to all target user groups and the dissimilarity of all health products, to obtain a target cluster, including:
Correcting the excess income proportion and the physical strain index corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs to obtain the corrected income proportion and the corrected strain degree corresponding to each target user and the corrected work intensity corresponding to the target user group to which the target user belongs respectively, wherein the excess income proportion and the corrected income proportion are positively correlated, the physical strain index and the corrected strain degree are positively correlated, and the professional work intensity and the corrected work intensity are positively correlated;
Combining the corrected income proportion and the corrected strain degree corresponding to each target user, the corrected working intensity corresponding to the target user group to which the target user belongs, and age data and sex data included in the corresponding user information into professional physiological feature vectors corresponding to each target user;
Determining the square sum of the dissimilarity between the Euclidean distance between the occupational physiological feature vectors corresponding to each two target users and the health care product usage dissimilarity between the two target users as the target dissimilarity feature between the two target users;
And carrying out hierarchical clustering on all target users according to the target dissimilar characteristics among all target users, and taking each cluster obtained as a target cluster.
Optionally, the recommending the collagen peptide health product to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs includes:
the collagen peptide health products corresponding to the purchased health product information included in the user information corresponding to all the target users in the target cluster with the smallest belonged to the user to be recommended are determined to be candidate health products;
And screening collagen peptide health products which are not purchased by the user to be recommended from all candidate health products, taking the collagen peptide health products as target health products, and recommending the target health products to the user to be recommended.
The invention has the following beneficial effects:
According to the intelligent recommendation method for the collagen peptide health products based on machine learning, through data processing on all user information, the technical problem that the accuracy of recommending the collagen peptide health products to users is poor is solved, and the accuracy of recommending the collagen peptide health products to users is improved. Firstly, user information corresponding to the user to be recommended is acquired, so that preference of the user to be recommended for using the collagen peptide health product can be conveniently analyzed later, and the user to be recommended can be conveniently recommended for recommending the collagen peptide health product later. Secondly, user information corresponding to the historical users is acquired, so that the health care product use similarity condition between the users to be recommended and the historical users can be conveniently analyzed later, the collagen peptide health care product recommendation can be conveniently carried out on the users to be recommended according to the user information corresponding to the historical users with higher similarity in a follow-up auxiliary mode, and the unused collagen peptide health care product types of the users to be recommended can be recommended more accurately to a certain extent. Then, because professional requirements corresponding to different professional types are often different, the working intensity of the target users is often different, so that physical exercise conditions corresponding to users of different professional types are often different, and the types of required collagen peptide health care products are often different. Then, since one target user group corresponds to one occupation, based on the user information corresponding to all the target users in the target user group, the occupation work intensity of the occupation to which the target user group belongs can be quantified. Continuing, since the self situation of the target user and the professional work intensity of the occupation are often related to the hard and tired situation of the target user, the physical strain index corresponding to the target user can be quantified based on the user information corresponding to the target user and the professional work intensity corresponding to the target user group to which the target user belongs. And then, comprehensively considering the health care product use data included in the user information corresponding to all the target users, and quantifying the use dissimilarity of the health care products between every two target users, wherein the smaller the value is, the closer the health care product use preference of the two target users is. And then, based on the body strain index corresponding to all the target users, the professional working intensity corresponding to all the target user groups and the use dissimilarity of all the health products, clustering all the target users, so that the target users with similar health product use preference, body strain condition and working intensity can be divided into the same target cluster as far as possible. Finally, because the target users in the target cluster to which the user to be recommended belongs are often target users similar to the health care product use preference, the physical strain condition and the working strength of the user to be recommended, compared with the method for recommending the collagen peptide health care product by only considering the user information corresponding to the user to be recommended, the method and the device for recommending the collagen peptide health care product based on the user information corresponding to the target users in the target cluster to which the user to be recommended belongs, comprehensively consider the user information corresponding to the user to be recommended and the user information corresponding to the health care product use preference, the physical strain condition and the working strength of the user to be recommended, the method and the device can reasonably recommend the collagen peptide health care product to the user to be recommended, so that the accuracy of recommending the collagen peptide health care product to the user to be recommended is improved. Secondly, compared with the method for recommending the collagen peptide health products based on the content by manually labeling different types of collagen peptide health products with fixed efficacy, the method can avoid the problem of poor accuracy of recommending the collagen peptide health products for users to be recommended due to the fact that the manual labeling is wrong due to various types of the collagen peptide health products to a certain extent.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of the intelligent recommendation method of the collagen peptide health care product based on machine learning.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description is given below of the specific implementation, structure, features and effects of the technical solution according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides an intelligent recommendation method of a collagen peptide health product based on machine learning, which comprises the following steps:
Acquiring user information corresponding to each historical user in a user to be recommended and a historical user set;
Recording the users to be recommended and each history user as target users, and forming a target user group by using target users with the same occupation in all target users;
According to the user information corresponding to all the target users in each target user group, carrying out working intensity analysis processing on the occupation of each target user group to obtain the professional working intensity corresponding to each target user group;
Determining a physical strain index corresponding to each target user according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs;
Determining the dissimilarity of health care product usage between every two target users according to health care product usage data contained in user information corresponding to all the target users;
clustering all target users according to the body strain index corresponding to all target users, the professional work intensity corresponding to all target user groups and the dissimilarity degree of all health care product usage, so as to obtain target cluster clusters;
And recommending the collagen peptide health products to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs.
The following detailed development of each step is performed:
Referring to fig. 1, a flow of some embodiments of a machine learning based collagen peptide health care product intelligent recommendation method according to the present invention is shown. The intelligent collagen peptide health product recommending method based on machine learning comprises the following steps:
step S1, user information corresponding to each historical user in a user to be recommended and a historical user set is obtained.
In some embodiments, user information corresponding to each historical user in the set of users to be recommended and historical users may be obtained.
The user to be recommended can be a user to be recommended for collagen peptide health products. The collagen peptide is a small molecular extract of animal tissues, belongs to a health-care product, and can be recorded as a collagen peptide health-care product. The historical users in the historical user collection can be users recorded with information related to the collagen peptide health care products. The user to be recommended and each history user are recorded as target users, and the user information corresponding to the target users can be information related to the collagen peptide health products of the target users. The user information may include: revenue data, minimum residential revenue criteria, age data, gender data, occupation data, and health care usage data. Revenue data may be data characterizing revenue. For example, the revenue data may be the monthly revenue of the target user. The residence minimum revenue criteria may be a month minimum payroll criteria for the residence where the target user is located. For example, the residence minimum revenue criterion may be the month minimum payroll criterion for the city in which the target user is located. The age data may be the age of the target user. The gender data may be the gender of the target user. The occupation data may be the occupation of the target user. The health product use data can be recorded data of the condition that the target user uses the collagen peptide health product. The health product usage data may include a collection of purchased health product information. The purchased health product information in the purchased health product information set may be information of the target user purchasing each collagen peptide health product. One piece of information of the purchased health care product can correspond to one collagen peptide health care product. The purchased healthcare product information in the purchased healthcare product information set may include: product expense weight, product source and product organization. The product expenditure weight included in the purchased healthcare product information may be a total amount spent by the target user purchasing the collagen peptide healthcare products corresponding to the purchased healthcare product information, and a ratio of the total amount spent by the target user purchasing all the collagen peptide healthcare products. The purchased health product information can comprise animal sources of the collagen peptide health products corresponding to the purchased health product information, and can represent which animal the collagen peptide health products are extracted from. The product tissue composition included in the purchased health product information may be an animal tissue composition of the collagen peptide health product corresponding to the purchased health product information, and may indicate that the collagen peptide health product is extracted from the animal tissue.
It should be noted that, the user information corresponding to the user to be recommended is obtained, so that the preference of the user to be recommended for using the collagen peptide health product can be conveniently analyzed later, and the user to be recommended can be conveniently recommended for recommending the collagen peptide health product later. Secondly, user information corresponding to the historical users is acquired, so that the health care product use similarity condition between the users to be recommended and the historical users can be conveniently analyzed later, the collagen peptide health care product recommendation can be conveniently carried out on the users to be recommended according to the user information corresponding to the historical users with higher similarity in a follow-up auxiliary mode, and the unused collagen peptide health care product types of the users to be recommended can be recommended more accurately to a certain extent.
As an example, the user information corresponding to the user to be recommended and each history user may be filled according to the autonomous willingness of the user to be recommended and each history user, and whether to be anonymous may be selected in the filling process.
And S2, recording the users to be recommended and each history user as target users, and forming a target user group by using target users with the same occupation in all target users.
In some embodiments, the user to be recommended and each history user may be remembered as target users, and target users with the same occupation in all target users may be formed into a target user group.
It should be noted that, because professional requirements corresponding to different professional types are often different, the working intensity of the target users is often different, so that physical exercise conditions corresponding to users of different professional types are often different, and the types of required collagen peptide health care products are often different, therefore, the target users with the same profession in all target users form a target user group, and the professional working intensity of each profession can be conveniently analyzed later.
As an example, target users whose professional data included in the user information is the same may be divided into the same target user group.
And S3, according to the user information corresponding to all the target users in each target user group, analyzing and processing the working intensity of the occupation to which each target user group belongs, and obtaining the working intensity of the occupation corresponding to each target user group.
In some embodiments, according to user information corresponding to all target users in each target user group, a job intensity analysis process may be performed on a job to which each target user group belongs, so as to obtain a job intensity corresponding to each target user group.
The occupation to which the target user group belongs may be the occupation of the target user in the target user group.
It should be noted that, since one target user group corresponds to one occupation, based on the user information corresponding to all the target users in the target user group, the occupation work intensity of the occupation to which the target user group belongs can be quantified.
As an example, this step may include the steps of:
and a first step of determining an excess income proportion corresponding to each target user according to income data and minimum residential income standard included in the user information corresponding to each target user in the target user group.
For example, the formula for determining the ratio of the sum of the income data included in the user information corresponding to the target user and the minimum income criterion of the residence, which is the ratio of the sum of the income data included in the user information corresponding to the target user and the minimum income criterion of the residence, may be:
; wherein,/> is the excess revenue ratio corresponding to the jth target user in the ith target user group. i is the sequence number of the target user group. j is the sequence number of the target user in the ith target user group. And/> is revenue data included in the user information corresponding to the j-th target user in the i-th target user group. And/> is the minimum residential revenue standard included in the user information corresponding to the j-th target user in the i-th target user group.
It should be noted that, when is larger, it is often explained that the income of the jth target user in the ith target user group is relatively higher, and it is often explained that the jth target user in the ith target user group is more likely to belong to a high-income crowd at the residence. Because the demand of the people with different incomes for the acceptable price and the like of the collagen peptide health care products is often different, the demand of the people with similar incomes for the acceptable price and the like of the collagen peptide health care products is often the same, and secondly, the excess income proportion is often related to the working type and is often related to the working strength, so that the physical condition of people is often influenced, the demand of the people with similar excess income proportion for the collagen peptide health care products is often the same, the excess income proportion corresponding to target users is quantified, the follow-up assistance can be facilitated according to the target users with similar excess income proportion corresponding to the users to be recommended, and more suitable collagen peptide health care products are recommended for the users to be recommended.
And secondly, classifying the target users in the target user group according to the excess income proportion corresponding to all the target users in the target user group to obtain a preset number of reference categories.
The preset number may be a preset number. For example, the preset number may be 10.
For example, all target users in the target user group are ordered according to the order of the excess income proportion corresponding to the target users from large to small, the obtained sequence is used as a target user sequence corresponding to the target user group, the target user sequence is equally divided into a preset number of subsequences, and the target users in each subsequence are used as target users in a reference category.
And thirdly, taking age data included in user information corresponding to the target user in each reference category as an abscissa, and taking excess income proportion corresponding to the target user in the reference category as an ordinate to make a scatter diagram corresponding to the reference category.
And fourthly, performing straight line fitting on the scatter diagram corresponding to each reference category, and determining a fitting straight line corresponding to each reference category to obtain a fitting straight line set corresponding to the target user group.
The number of the fitting straight lines in the fitting straight line set corresponding to the target user group may be a preset number.
Fifthly, determining a formula corresponding to the professional work intensity corresponding to the target user group according to the fitting straight line set, wherein the formula can be as follows:
; where,/> is the professional work intensity corresponding to the i-th target user group. i is the sequence number of the target user group. And/> is the number of fitting lines in the set of fitting lines corresponding to the ith target user group. And t and r are sequence numbers of fitting straight lines in the fitting straight line set corresponding to the ith target user group. Is the slope of the t-th fitting line in the fitting line set corresponding to the i-th target user group. And/() is the slope of the r-th fitting line in the fitting line set corresponding to the i-th target user group. And/> takes the absolute function. And/> is the intercept of the t-th fitting line in the set of fitting lines corresponding to the i-th target user group. And/> is the intercept of the r-th fitting line in the set of fitting lines corresponding to the i-th target user group. And/> is the mean of all the fit line intercepts in the fit line set corresponding to the ith target user group.
It should be noted that the slope of the fitted line may often characterize the rate of revenue increase, and the intercept of the fitted line may often characterize the initial revenue. When is larger, it is often stated that the revenue distribution for the profession to which the i-th target user group belongs may be more uneven. When/> is larger, the occupation of the ith target user group tends to be more obvious as the income gap is enlarged with the age, and the income distribution tends to be more uneven. When/> is larger, it is often stated that the revenue distribution of the occupation to which the i-th target user group belongs is relatively uneven. Therefore, when/> is larger, the more uneven the revenue distribution of the profession to which the i-th target user group belongs is often indicated, and the higher the sinking cost of the profession to which the i-th target user group belongs is often indicated. Generally, whether the income distribution of the profession is uniform and stable is often related to the working intensity, and often influences the working attitude and the effort degree of people, and often influences the physical condition of people, so that the demands of people on the collagen peptide health care product are influenced, and the demands of target users with similar professional working intensity on the collagen peptide health care product are often similar. Therefore, the professional work intensity is quantified, and the more appropriate collagen peptide health care product can be conveniently recommended to the user to be recommended in a follow-up auxiliary manner according to the target user which is similar to the professional work intensity of the work to which the user to be recommended belongs.
And S4, determining the body strain index corresponding to each target user according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs.
In some embodiments, the physical strain index corresponding to each target user may be determined according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs.
It should be noted that, since the self situation of the target user and the professional work intensity of the occupation are often related to the hard and tired situation of the target user, the physical strain index corresponding to the target user can be quantified based on the user information corresponding to the target user and the professional work intensity corresponding to the target user group to which the target user belongs.
As an example, according to age data included in the user information corresponding to each target user, an excess income proportion corresponding to the target user, and a professional work intensity and a fitting straight line set corresponding to the target user group to which the target user belongs, a formula corresponding to the physical strain index corresponding to each target user may be determined as follows:
; wherein,/> is the physical strain index corresponding to the jth target user in the ith target user group. i is the sequence number of the target user group. j is the sequence number of the target user in the ith target user group. And/> is the excess revenue ratio corresponding to the jth target user in the ith target user group. And/> is the professional work intensity corresponding to the i-th target user group. And/() is the average value of the slopes of all the fit lines in the fit line set corresponding to the ith target user group. And/> is age data included in the user information corresponding to the j-th target user in the i-th target user group. And/> is the mean of all the fit line intercepts in the fit line set corresponding to the ith target user group. And/> is the average of the professional work intensity corresponding to all the target user groups. And/() is the slope of the fitted straight line to which the excess income proportion corresponding to the jth target user in the ith target user group belongs. And/> is the intercept of the fitted line to which the excess revenue ratio corresponding to the jth target user in the ith target user group belongs.
It should be noted that, since the excess income proportion is often related to the work type and is often related to the work intensity, so that the physical condition of people is often affected, and the professional work intensity is often affected by the work attitude and effort of people, so that the physical condition of people is often affected, so can represent the physical condition corresponding to the jth target user. Often,/> can represent the average excess income level of the profession of the jth target user in the age group of the jth target user, and/> can represent the average level of all professional work intensity, so can represent the average physical condition of the crowd of the profession of the jth target user in the age group of the jth target user. The/> is mainly used for correction . Thus,/> can characterize the physical condition and physical strain of the jth target user. Generally, the physical strain often affects the demands of people on collagen peptide health care products, and the demands of target users with similar physical strain often are similar.
And S5, determining the dissimilarity of the health care product usage between every two target users according to the health care product usage data contained in the user information corresponding to all the target users.
In some embodiments, the dissimilarity of health care product usage between every two target users may be determined according to health care product usage data included in user information corresponding to all target users.
It should be noted that, comprehensively considering the health care product usage data included in the user information corresponding to all the target users, the dissimilarity of health care product usage between every two target users can be quantified, and the smaller the value, the closer the health care product usage preferences of the two target users are often described.
As an example, this step may include the steps of:
The method comprises the steps of firstly, determining a purchased health product information set which is included in health product use data and is included in user information corresponding to each target user as the purchased health product information set corresponding to each target user.
Secondly, according to the product expenditure weight, the product source and the product organization composition contained in all purchased health product information in the purchased health product information set corresponding to each two target users, the formula corresponding to the dissimilarity of health product use between each two target users can be determined as follows:
; wherein,/> is the dissimilarity in health care product usage between the h-th target user and the m-th target user. h and m are the sequence numbers of the target users. And/> ./> is the number of the purchased health care product information in the purchased health care product information set corresponding to the h target user. And/> is the number of the purchased health product information in the purchased health product information set corresponding to the mth target user. And/> is an absolute function. a is the serial number of the purchased health product information in the purchased health product information set corresponding to the h target user. b is the serial number of the purchased health product information in the purchased health product information set corresponding to the mth target user. And/> is the product expenditure weight included in the a-th purchased health product information in the purchased health product information set corresponding to the h-th target user. And/> is the product expenditure weight included in the b-th purchased health product information in the purchased health product information set corresponding to the m-th target user. When the product organization composition included in the a-th purchased health product information is the same as the product organization composition included in the b-th purchased health product information, setting/> to be a first preset value. When the composition of the product organization included in the a-th purchased health product information is different from the composition of the product organization included in the b-th purchased health product information, setting/> to be a second preset value. The first preset value is smaller than the second preset value. For example, the first preset value may be 0 and the second preset value may be 2. The grade of the product source is in the range of [1, Q ]. Q represents the highest grade of the grades to which the source of the product belongs. Because the product source is the animal source of the collagen peptide health care product, namely the animal, the grade of the product source can be 7, and the product source can be sequentially from low to high: "species", "genus", "family", "order", "class", "phylum" and "kingdom", where Q may be 7, may represent "kingdom" in the hierarchy. When the lowest grade of the product source included in the a-th purchased health product information and the product source included in the b-th purchased health product information is the q-th grade, setting/> to be q-1. If the source of the product included in the a-th purchased health product information is different from the source of the product included in the b-th purchased health product information in the "species" and is the same in the "genus", "family", "order", "class", "door" and "kingdom", then/> may be set to 1 at this time. The value range of q is [1, Q ]. When the source of the product included in the a-th purchased health product information is different from the source of the product included in the b-th purchased health product information in all grades, setting/> as Q.
When is smaller, it is often indicated that the product expense weight of the h target user for the collagen peptide health product corresponding to the a-th purchased health product information is more similar to the product expense weight of the m target user for the collagen peptide health product corresponding to the b-th purchased health product information, and it is often indicated that the favorability of the h target user for the collagen peptide health product corresponding to the a-th purchased health product information is more similar to the favorability of the m target user for the collagen peptide health product corresponding to the b-th purchased health product information. When/> is smaller, it is often indicated that the source of the product included in the information of the a-th purchased health product is closer to the source of the product included in the information of the b-th purchased health product. When is smaller, it is often indicated that the composition of the product organization included in the information of the a-th purchased health product is more similar to the composition of the product organization included in the information of the b-th purchased health product. Therefore, when/> 、/> and/> are smaller, it is often indicated that the preference degree of the h target user and the m target user for the collagen peptide health care products composed of similar product sources and product tissues is more similar. Therefore, when/> is smaller, it is often indicated that the favoring degree of the h target user and the m target user on the raw material composition of the collagen peptide health product is more similar.
And S6, clustering all the target users according to the body strain index corresponding to all the target users, the professional work intensity corresponding to all the target user groups and the dissimilarity of all the health care products, and obtaining a target cluster.
In some embodiments, the target clusters may be obtained by clustering all target users according to the physical strain indexes corresponding to all target users, the professional work intensities corresponding to all target user groups, and the dissimilarity of use of all health products.
It should be noted that, based on the physical strain indexes corresponding to all the target users, the professional working intensities corresponding to all the target user groups, and the dissimilarity of use of all the health products, clustering all the target users can divide the target users with similar health product use preference, physical strain condition, and working intensity into the same target cluster as much as possible.
As an example, this step may include the steps of:
the first step, correcting the excess income proportion and the physical strain index corresponding to each target user and the professional working intensity corresponding to the target user group to which the target user belongs to obtain the corrected income proportion and the corrected strain degree corresponding to each target user and the corrected working intensity corresponding to the target user group to which the target user belongs respectively.
Wherein the excess revenue ratio may be positively correlated with the corrected revenue ratio. The physical strain indicator may be positively correlated with the degree of corrected strain. The professional work intensity may be positively correlated with the corrected work intensity.
For example, the formulas corresponding to the corrected income proportion and the corrected strain degree of the target user and the corrected working intensity of the target user group to which the target user belongs may be:
;/>;/> ; wherein is the corrected revenue ratio corresponding to the jth target user in the ith target user group. i is the sequence number of the target user group. j is the sequence number of the target user in the ith target user group. And/> is a base 10 logarithmic function. And/> is the excess revenue ratio corresponding to the jth target user in the ith target user group. And/> is the corrected strain level corresponding to the jth target user in the ith target user group. And/> is the physical strain index corresponding to the j-th target user in the i-th target user group. Is the corrected working strength corresponding to the ith target user group. And/> is the professional work intensity corresponding to the i-th target user group.
It should be noted that, when is larger, it is often explained that the income of the jth target user in the ith target user group is relatively higher. Thus, when/> is larger, it is often indicated that the higher the income of the jth target user in the ith target user group is, the more likely the jth target user in the ith target user group is to belong to a high-income crowd at the residence. When is larger, the revenue distribution of the profession to which the ith target user group belongs is relatively uneven, the sinking cost of the profession to which the ith target user group belongs is relatively high, and therefore when/> is larger, the revenue distribution of the profession to which the ith target user group belongs is relatively uneven, the sinking cost of the profession to which the ith target user group belongs is relatively high. The physical condition and physical strain condition of the jth target user can be characterized. Thus/> can characterize the physical condition and physical strain of the jth target user.
And secondly, combining the corrected income proportion and the corrected strain degree corresponding to each target user, the corrected working intensity corresponding to the target user group to which the target user belongs, and age data and sex data included in the corresponding user information into professional physiological feature vectors corresponding to each target user.
For example, the professional physiological feature vector corresponding to the jth target user in the ith target user group may be { ,/>,/>,/>,/> }, where/> is the corrected revenue ratio corresponding to the jth target user in the ith target user group; the/> is the corrected working strength corresponding to the i-th target user group; the/> is the corrected strain degree corresponding to the jth target user in the ith target user group; the/> is the gender data included in the user information corresponding to the j-th target user in the i-th target user group; and/> is age data included in the user information corresponding to the j-th target user in the i-th target user group.
And thirdly, determining the square sum of the Euclidean distance between occupational physiological feature vectors corresponding to each two target users and the dissimilarity degree of the health care product usage between the two target users as the target dissimilarity feature between the two target users.
Fourth, hierarchical clustering is carried out on all target users according to target dissimilar characteristics among all target users, and each cluster is obtained and is used as a target cluster.
For example, the target dissimilar characteristics among the target users can be used as a measure in the clustering process, hierarchical clustering is performed on all the target users, and each obtained cluster is used as a target cluster.
Optionally, product expenditure weights, product sources and product organizations included in all purchased health product information in the purchased health product information sets corresponding to all target users can be used as input, all target users are clustered through a hierarchical clustering algorithm, in the clustering process, target dissimilar characteristics between two target users can be calculated according to the product expenditure weights, the product sources and the product organizations included in all purchased health product information in the purchased health product information sets corresponding to every two target users, and the sum of the calculated Euclidean distance between occupational physiological characteristic vectors corresponding to every two target users and the target dissimilar characteristics between the two target users can be used as a measure in the clustering process; and outputting a tree structure which is hierarchical clustering, and taking each cluster obtained at the moment as a target cluster.
And S7, recommending the collagen peptide health products to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs.
In some embodiments, the collagen peptide health product recommendation of the user to be recommended may be performed according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs, so that the collagen peptide health product recommendation of the user to be recommended may be implemented.
As an example, this step may include the steps of:
firstly, determining collagen peptide health products corresponding to each purchased health product information included in user information corresponding to all target users in the target cluster with the smallest belonged to the user to be recommended as candidate health products.
And secondly, screening collagen peptide health products which are not purchased by the user to be recommended from all candidate health products, taking the collagen peptide health products as target health products, and recommending the target health products to the user to be recommended.
For example, the recommendation of the target health product to the user to be recommended may be achieved by sending the name corresponding to the target health product to the target terminal. The target terminal may be a mobile phone.
Optionally, if more collagen peptide health products are to be recommended to the user to be recommended, the recommending of the collagen peptide health products to the user to be recommended may be performed according to user information corresponding to all target users in a target cluster of a larger hierarchy to which the user to be recommended belongs, which may specifically be: the target cluster of a larger hierarchy, to which the user to be recommended belongs, is used as the smallest target cluster to which the user to be recommended belongs, and the first step to the second step, which are included as examples in the step S7, are executed, so that more collagen peptide health products can be recommended to the user to be recommended.
In summary, because the target users in the target cluster to which the user to be recommended belongs are often target users similar to the health care product use preference, the physical strain condition and the working strength of the user to be recommended, compared with the method for recommending the collagen peptide health care product by only considering the user information corresponding to the user to be recommended, the method for recommending the collagen peptide health care product by the user to be recommended based on the user information corresponding to the target users in the target cluster to which the user to be recommended belongs comprehensively considers the user information corresponding to the user to be recommended and the user information corresponding to the health care product use preference, the physical strain condition and the working strength of the user to be recommended, the method for recommending the collagen peptide health care product by the user to be recommended can reasonably recommended, and accordingly accuracy of recommending the collagen peptide health care product by the user to be recommended is improved. Secondly, compared with the method for recommending the collagen peptide health products based on the content by manually labeling different types of collagen peptide health products with fixed efficacy, the method can avoid the problem of poor accuracy of recommending the collagen peptide health products for users to be recommended due to the fact that the manual labeling is wrong due to various types of the collagen peptide health products to a certain extent.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention and are intended to be included within the scope of the invention.
Claims (5)
1. The intelligent collagen peptide health product recommending method based on machine learning is characterized by comprising the following steps of:
Acquiring user information corresponding to each historical user in a user to be recommended and a historical user set;
the users to be recommended and each history user are target users, and target users with the same occupation in all target users form a target user group;
According to the user information corresponding to all the target users in each target user group, carrying out working intensity analysis processing on the occupation of each target user group to obtain the professional working intensity corresponding to each target user group;
Determining a physical strain index corresponding to each target user according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs;
Determining the dissimilarity of health care product usage between every two target users according to health care product usage data contained in user information corresponding to all the target users;
clustering all target users according to the body strain index corresponding to all target users, the professional work intensity corresponding to all target user groups and the dissimilarity degree of all health care product usage, so as to obtain target cluster clusters;
recommending the collagen peptide health products to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs;
the step of analyzing and processing the working intensity of the occupation of each target user group according to the user information corresponding to all the target users in each target user group to obtain the working intensity of the occupation corresponding to each target user group comprises the following steps:
Determining an excess income proportion corresponding to each target user according to income data and minimum income standard of residence included in user information corresponding to each target user in the target user group;
Classifying target users in the target user group according to excess income proportions corresponding to all the target users in the target user group to obtain a preset number of reference categories;
Taking age data contained in user information corresponding to a target user in each reference category as an abscissa, and taking excess income proportion corresponding to the target user in the reference category as an ordinate, and making a scatter diagram corresponding to the reference category;
Performing straight line fitting on the scatter diagram corresponding to each reference category, determining a fitting straight line corresponding to each reference category, and obtaining a fitting straight line set corresponding to the target user group;
determining the occupational work intensity corresponding to the target user group according to the fitting straight line set;
The determining the physical strain index corresponding to each target user according to the user information corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs comprises the following steps:
Determining the physical strain index corresponding to each target user according to age data, the excess income proportion corresponding to the user information corresponding to each target user, the professional work intensity corresponding to the target user group to which the target user belongs and the fitting straight line set;
The determining the dissimilarity of health care product usage between every two target users according to the health care product usage data included in the user information corresponding to all the target users comprises the following steps:
Determining a purchased health product information set which is corresponding to each target user and is included in health product use data corresponding to each target user, wherein the purchased health product information in the purchased health product information set comprises the following components: the product expenditure weight, the product source and the product organization are formed, and the information of one purchased health product corresponds to one collagen peptide health product;
Determining the dissimilarity of health care product usage between every two target users according to the product expenditure weight, product source and product organization included in all purchased health care product information in the purchased health care product information set corresponding to every two target users;
Clustering all target users according to the body strain index corresponding to all target users, the professional work intensity corresponding to all target user groups and the dissimilarity of all health care products, so as to obtain target cluster, wherein the clustering comprises the following steps:
Correcting the excess income proportion and the physical strain index corresponding to each target user and the professional work intensity corresponding to the target user group to which the target user belongs to obtain the corrected income proportion and the corrected strain degree corresponding to each target user and the corrected work intensity corresponding to the target user group to which the target user belongs respectively, wherein the excess income proportion and the corrected income proportion are positively correlated, the physical strain index and the corrected strain degree are positively correlated, and the professional work intensity and the corrected work intensity are positively correlated;
Combining the corrected income proportion and the corrected strain degree corresponding to each target user, the corrected working intensity corresponding to the target user group to which the target user belongs, and age data and sex data included in the corresponding user information into professional physiological feature vectors corresponding to each target user;
Determining the square sum of the dissimilarity between the Euclidean distance between the occupational physiological feature vectors corresponding to each two target users and the health care product usage dissimilarity between the two target users as the target dissimilarity feature between the two target users;
Hierarchical clustering is carried out on all target users according to target dissimilar characteristics among all target users, and each cluster is obtained and is used as a target cluster;
The recommending the collagen peptide health product to the user to be recommended according to the user information corresponding to the target user in the target cluster to which the user to be recommended belongs comprises the following steps:
the collagen peptide health products corresponding to the purchased health product information included in the user information corresponding to all the target users in the target cluster with the smallest belonged to the user to be recommended are determined to be candidate health products;
And screening collagen peptide health products which are not purchased by the user to be recommended from all candidate health products, taking the collagen peptide health products as target health products, and recommending the target health products to the user to be recommended.
2. The intelligent recommendation method for collagen peptide health products based on machine learning according to claim 1, wherein the determining the excess income ratio corresponding to each target user according to the income data and the minimum income standard of the residence included in the user information corresponding to each target user in the target user group comprises:
And determining the ratio of the difference between the income data included in the user information corresponding to the target user and the minimum income standard of the residential area in the minimum income standard of the residential area included in the user information corresponding to the target user as the excess income ratio corresponding to the target user.
3. The intelligent recommendation method for collagen peptide health products based on machine learning according to claim 1, wherein the formula corresponding to the professional work intensity corresponding to the target user group is:
; wherein,/> is the professional work intensity corresponding to the i-th target user group; i is the sequence number of the target user group; the number of fitting lines in the fitting line set corresponding to the ith target user group is/; t and r are sequence numbers of fitting straight lines in the fitting straight line set corresponding to the ith target user group; the value of/() is the slope of the t-th fitting line in the fitting line set corresponding to the i-th target user group; the value of/() is the slope of the r-th fitting line in the fitting line set corresponding to the i-th target user group; the absolute value function is taken by the/> ; the/> is the intercept of the t-th fitting line in the fitting line set corresponding to the i-th target user group; the value of is the intercept of the r-th fitting line in the fitting line set corresponding to the i-th target user group; and/> is the mean of all the fit line intercepts in the fit line set corresponding to the ith target user group.
4. The intelligent recommendation method for collagen peptide health products based on machine learning according to claim 1, wherein the formula corresponding to the physical strain index corresponding to the target user is:
; wherein,/> is the physical strain index corresponding to the jth target user in the ith target user group; i is the sequence number of the target user group; j is the sequence number of the target user in the ith target user group; the/> is the excess income proportion corresponding to the j-th target user in the i-th target user group; the/> is the professional work intensity corresponding to the i-th target user group; the value of the slope of all the fitting lines in the fitting line set corresponding to the ith target user group is denoted by/(); the/> is age data included in the user information corresponding to the j-th target user in the i-th target user group; Is the average value of all fitting straight line intercept in the fitting straight line set corresponding to the ith target user group; the/> is the average value of the occupational work intensity corresponding to all the target user groups; the/> is the slope of a fitted straight line to which the excess income proportion corresponding to the jth target user in the ith target user group belongs; and/> is the intercept of the fitted line to which the excess revenue ratio corresponding to the jth target user in the ith target user group belongs.
5. The intelligent recommendation method for collagen peptide health products based on machine learning according to claim 1, wherein the formula corresponding to the dissimilarity of health products used by two target users is:
; wherein is the dissimilarity in health care product usage between the h target user and the m target user; h and m are sequence numbers of the target users; the number of the purchased health product information in the purchased health product information set corresponding to the h target user is indicated by ';/>'; the number of the purchased health product information in the purchased health product information set corresponding to the mth target user is/; the/> is an absolute function; a is the serial number of the purchased health product information in the purchased health product information set corresponding to the h target user; b is the serial number of the purchased health product information in the purchased health product information set corresponding to the mth target user; the/> is the product expenditure weight included in the a-th purchased health product information in the purchased health product information set corresponding to the h-th target user; the/> is the product expenditure weight included in the b-th purchased health product information in the purchased health product information set corresponding to the m-th target user; setting/> as a first preset value when the product organization composition included in the a-th purchased health product information is the same as the product organization composition included in the b-th purchased health product information; setting/> to a second preset value when the product organization composition included in the a-th purchased health product information is different from the product organization composition included in the b-th purchased health product information; the first preset value is smaller than the second preset value; the value range of the grade of the product source is [1, Q ]; q represents the highest grade of the grades to which the source of the product belongs; setting/> as q-1 when the lowest grade of the product source included in the a-th purchased health product information and the product source included in the b-th purchased health product information is the q-th grade; the value range of q is [1, Q ]; when the source of the product included in the a-th purchased health product information is different from the source of the product included in the b-th purchased health product information in all grades, setting/> as Q.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311450551.3A CN117172832B (en) | 2023-11-03 | 2023-11-03 | Intelligent recommendation method for collagen peptide health products based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311450551.3A CN117172832B (en) | 2023-11-03 | 2023-11-03 | Intelligent recommendation method for collagen peptide health products based on machine learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117172832A CN117172832A (en) | 2023-12-05 |
| CN117172832B true CN117172832B (en) | 2024-04-16 |
Family
ID=88932129
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311450551.3A Active CN117172832B (en) | 2023-11-03 | 2023-11-03 | Intelligent recommendation method for collagen peptide health products based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117172832B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117972221B (en) * | 2024-04-02 | 2024-06-11 | 济宁市金桥煤矿 | Intelligent recommendation method and system for coal mine practical training courses |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013257793A (en) * | 2012-06-13 | 2013-12-26 | Nippon Telegr & Teleph Corp <Ntt> | Commodity diversification recommendation device, and method and program for the same |
| CN112233772A (en) * | 2020-10-13 | 2021-01-15 | 杭州电子科技大学 | Healthy recipe recommendation system based on machine learning |
| CN112884547A (en) * | 2021-02-03 | 2021-06-01 | 绿瘦健康产业集团有限公司 | Intelligent commodity recommendation method, device, medium and terminal equipment |
| CN116911985A (en) * | 2023-07-24 | 2023-10-20 | 中国银行股份有限公司 | Product recommendation method, device, equipment and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11410110B2 (en) * | 2019-08-06 | 2022-08-09 | International Business Machines Corporation | Alert generation based on a cognitive state and a physical state |
-
2023
- 2023-11-03 CN CN202311450551.3A patent/CN117172832B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013257793A (en) * | 2012-06-13 | 2013-12-26 | Nippon Telegr & Teleph Corp <Ntt> | Commodity diversification recommendation device, and method and program for the same |
| CN112233772A (en) * | 2020-10-13 | 2021-01-15 | 杭州电子科技大学 | Healthy recipe recommendation system based on machine learning |
| CN112884547A (en) * | 2021-02-03 | 2021-06-01 | 绿瘦健康产业集团有限公司 | Intelligent commodity recommendation method, device, medium and terminal equipment |
| CN116911985A (en) * | 2023-07-24 | 2023-10-20 | 中国银行股份有限公司 | Product recommendation method, device, equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 实体零售店的数字化应用技术策略研究;刘岳恒;《商业研究》;20191231(第24期);第7-9页 * |
| 面向图神经网络的对抗攻击与防御综述;陈晋音等;《网络与信息安全学报》;第7卷(第3期);第1-28 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117172832A (en) | 2023-12-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| O’Cass et al. | Can Islam and status consumption live together in the house of fashion clothing? | |
| Brazier et al. | Testing the validity of the Euroqol and comparing it with the SF-36 health survey questionnaire | |
| CN117172832B (en) | Intelligent recommendation method for collagen peptide health products based on machine learning | |
| Ieva et al. | Depth measures for multivariate functional data | |
| Baráth et al. | Heterogeneous technology, scale of land use and technical efficiency: The case of Hungarian crop farms | |
| Monahan et al. | Odds ratio, delta, ETS classification, and standardization measures of DIF magnitude for binary logistic regression | |
| CN108334592B (en) | Personalized recommendation method based on combination of content and collaborative filtering | |
| Hwang et al. | An extension of multiple correspondence analysis for identifying heterogeneous subgroups of respondents | |
| WO2018204763A2 (en) | Systems and methods for generating genetic profile test and related purchase recommendations via an artificial intelligence-enhanced chatbot | |
| CN112488863B (en) | Dangerous seed recommendation method and related equipment in user cold start scene | |
| US11232525B2 (en) | Method and apparatus for measuring influence on social network | |
| CN105653715A (en) | Pushing method and system for training courses | |
| CN110491520A (en) | A kind of construction method of the sclerotin status assessment model based on semi-supervised learning | |
| Geffersa et al. | Technical efficiency in crop production across agro-ecological zones in Ethiopia: A meta-analysis of frontier studies | |
| CN109636337A (en) | A kind of talent's base construction method and electronic equipment based on big data | |
| Freiman | The demand for healthcare among racial/ethnic subpopulations. | |
| Razzaq et al. | An automatic determining food security status: machine learning based analysis of household survey data | |
| CN116246743A (en) | Precise nutrition comprehensive evaluation system based on portrait label | |
| Healy et al. | Assessment of computer adaptive testing version of the Neuro-QOL for people with multiple sclerosis | |
| CN109583272B (en) | Footprint system capable of acquiring living state of human body | |
| CN111968740B (en) | Diagnostic label recommendation method and device, storage medium and electronic equipment | |
| Minicuci et al. | Data Resource Profile: Cross-national and cross-study sociodemographic and health-related harmonized domains from SAGE plus CHARLS, ELSA, HRS, LASI and SHARE (SAGE+ Wave 2) | |
| Ezin et al. | 'Fitness that Fits':-A Prototype Model for Workout Video Recommendation | |
| CN116340374A (en) | Personalized task recommendation method and system | |
| Johnson et al. | Effects of simplifying choice tasks on estimates of taste heterogeneity in stated-choice surveys |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |