User portrait generation method and system for medical beauty users

Abstract

The invention relates to the technical field of electric digital data processing, in particular to a medical beauty user-oriented user portrait generation method and system, and the method comprises the steps: carrying out the group division of medical beauty users based on the difference of consumption data of the medical beauty users, and obtaining a plurality of first medical beauty user groups; based on the medical beauty fashion data of the medical beauty platform and in combination with the consumption data of different medical beauty users in each first medical beauty user group, multi-platform medical beauty fashion trend analysis is carried out, the medical beauty feature fashion trend penetrability of each first medical beauty user group is determined, and then the medical beauty behavior influence degree of the fashion trend on each medical beauty user is determined; based on the difference between the medical beauty behavior influence degrees of different medical beauty users, performing group division on the medical beauty users again to obtain a plurality of second medical beauty user groups; and generating a user portrait of each medical beauty user based on the medical beauty feature tags corresponding to the two medical beauty user groups where each medical beauty user is located. The generation accuracy of the medical beauty portrait of the user is improved.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, specifically to a method and system for generating user profiles for medical aesthetics users. Background Technology

[0002] With consumption upgrades and people's growing demand for beauty, the medical aesthetics industry has experienced explosive growth in recent years. As user needs in the medical aesthetics industry become increasingly diversified, traditional static labeling is no longer sufficient to meet the demand for precise services. With the deep application of big data and artificial intelligence technologies, the industry is accelerating the construction of a dynamic user profile system to achieve in-depth insights into customer needs through real-time data mining.

[0003] Traditional methods, such as the RFM model, rely primarily on historical consumption data or static labels generated from single consultations. This makes it difficult to capture dynamic user behavior in real time, thus hindering effective insights into underlying trends in user needs and predictions of future interests. In particular, traditional RFM models focus solely on measuring a customer's past spending power, reflecting their historical value. This ignores the dynamic changes in user behavior influenced by current cosmetic trends and lacks a keen awareness of potential user needs and movements. Consequently, they fail to accurately predict new projects or emerging needs that users might be interested in, resulting in inaccurate user profiles. Summary of the Invention

[0004] To address the aforementioned technical problems, the present invention aims to provide a method and system for generating user profiles for medical aesthetics users. The specific technical solution adopted is as follows: In a first aspect, the present invention provides a method for generating user profiles for medical aesthetics users, comprising the following steps: Acquire consumption data from different medical aesthetics users and popular medical aesthetics data from different medical aesthetics platforms; Based on the differences in consumption data among different medical aesthetic users, all medical aesthetic users were first divided into several first medical aesthetic user groups. Based on the medical aesthetics popularity data of different medical aesthetics platforms, and combined with the consumption data of different medical aesthetics users within each first medical aesthetics user group, we conduct multi-platform medical aesthetics popularity trend analysis to determine the penetration of medical aesthetics characteristic popularity trends for each first medical aesthetics user group. Based on the consumption data of each medical aesthetics user and combined with the medical aesthetics popularity data of different medical aesthetics platforms, the dynamic characteristics of popular medical aesthetics are analyzed for each medical aesthetics user. In addition, the penetration degree of the medical aesthetics characteristic popularity trend of each medical aesthetics user's first medical aesthetics user group is combined to determine the degree of influence of popular medical aesthetics behavior on each medical aesthetics user. Based on the differences in the impact of different medical aesthetic behaviors among different medical aesthetic users, all medical aesthetic users are divided into several second medical aesthetic user groups. Based on the medical aesthetic feature tags corresponding to the first and second medical aesthetic user groups to which each medical aesthetic user belongs, a user profile is generated for each medical aesthetic user.

[0005] In conjunction with the first aspect mentioned above, among some possible implementation methods, the penetration of the medical aesthetics characteristic trend of each first medical aesthetics user group is determined, including: Based on the recent popular medical aesthetics data of each medical aesthetics platform, the frequency of occurrence of medical aesthetics keywords, and the frequency of co-occurrence of each medical aesthetics keyword with other medical aesthetics keywords in the same information source on each medical aesthetics platform, the linked keywords of each medical aesthetics keyword on each medical aesthetics platform, and the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords are determined. Based on the differences between the linked keywords of each medical aesthetics keyword on different medical aesthetics platforms, and the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords, the popularity trend of each medical aesthetics keyword across multiple platforms is determined. Based on the popularity trend of each medical aesthetics keyword across multiple platforms, and combined with the correlation between all medical aesthetics keywords involved in recent medical aesthetics projects and the multi-platform linked medical aesthetics keywords of each medical aesthetics keyword in the consumption data of all medical aesthetics users in each first medical aesthetics user group, the popularity penetration of medical aesthetics features of each medical aesthetics keyword in the recent medical aesthetics projects of each first medical aesthetics user group is determined. The popularity of medical aesthetic features among recent medical aesthetic procedures and all medical aesthetic keywords for each primary medical aesthetic user group was statistically analyzed to determine the penetration of the popularity trend of medical aesthetic features for each primary medical aesthetic user group.

[0006] In conjunction with the first aspect mentioned above, among some possible implementation methods, the associated keywords for each medical aesthetics keyword on each medical aesthetics platform, and the comprehensive high-frequency association between each medical aesthetics keyword and its associated keywords, include: Based on the recent popular medical aesthetics data of each medical aesthetics platform, the frequency of occurrence of medical aesthetics keywords is determined; Based on the frequency of each medical aesthetics keyword appearing together with other medical aesthetics keywords in the same information source on each medical aesthetics platform, the correlation coefficient between each medical aesthetics keyword and other medical aesthetics keywords is determined. Based on the frequency and correlation coefficient of each medical aesthetics keyword with other medical aesthetics keywords, the high frequency correlation of each medical aesthetics keyword with other medical aesthetics keywords is determined. Based on the aforementioned high-frequency correlation, the associated keywords for each medical aesthetics keyword on each medical aesthetics platform are determined from all other medical aesthetics keywords. Based on the high-frequency correlation between each medical aesthetics keyword and its associated keywords, the comprehensive high-frequency correlation between each medical aesthetics keyword and its associated keywords on each medical aesthetics platform is determined.

[0007] In conjunction with the first aspect mentioned above, among some possible implementation methods, determining the multi-platform popularity trend of each medical aesthetics keyword includes: Determine the intersection of each medical aesthetics keyword with its associated keywords on different medical aesthetics platforms to obtain the multi-platform associated medical aesthetics keywords for each medical aesthetics keyword; Based on the correlation between each linked keyword of each medical aesthetics keyword on each medical aesthetics platform and the linked medical aesthetics keywords of each multi-platform, the contribution of each linked keyword of each medical aesthetics keyword on each medical aesthetics platform to the popularity of medical aesthetics features is determined. Based on the comprehensive high-frequency correlation and the contribution of the popularity of each medical aesthetics keyword on various medical aesthetics platforms, the popularity trend of each medical aesthetics keyword across multiple platforms is determined.

[0008] In conjunction with the first aspect mentioned above, among some possible implementation methods, the analysis of the dynamic characteristics of popular cosmetic procedures for each user includes: Based on the consumption data of each medical aesthetics user, the distribution of medical aesthetics projects is analyzed, the pursuit of popular medical aesthetics projects by medical aesthetics users is analyzed, and the degree of pursuit of popular medical aesthetics techniques by each medical aesthetics user is determined. Based on each medical aesthetics user's consumption data, the types of medical aesthetics projects are identified, and combined with recent medical aesthetics trend data from different medical aesthetics platforms, the suitability of popular medical aesthetics projects for medical aesthetics users is analyzed to determine the individual suitability of each medical aesthetics user for popular medical aesthetics projects. Based on the popularity of certain cosmetic medical technologies and the individual's suitability for those technologies, the dynamic characteristics of each cosmetic medical user are obtained.

[0009] In conjunction with the first aspect mentioned above, among some possible implementation methods, the degree of pursuit of popular cosmetic techniques by each cosmetic user can be determined, including: Based on each medical aesthetic user's consumption data, the number of times each medical aesthetic procedure was performed was determined to assess the actual degree of each medical aesthetic procedure performed by each user. Based on the distribution level of the actual number of cosmetic procedures performed by each cosmetic user, the actual number of cosmetic procedures involved by each user is determined. Based on the consumption data of each medical aesthetics user, the number of types of medical aesthetics projects is counted, and combined with the actual involvement of each medical aesthetics user in medical aesthetics projects, the degree of pursuit of popular medical aesthetics techniques by each medical aesthetics user is determined.

[0010] In conjunction with the first aspect mentioned above, among some possible implementation methods, the individual trend suitability of cosmetic procedures for each user is determined, including: Based on each medical aesthetic user's consumption data, medical aesthetic keywords are extracted from all medical aesthetic projects to construct an individual medical aesthetic keyword set for each medical aesthetic user; Based on the popularity trend of all medical aesthetic keywords in the individual medical aesthetic keyword set across multiple platforms, the individual popularity medical aesthetic suitability of each medical aesthetic user is determined.

[0011] In conjunction with the first aspect mentioned above, among some possible implementation methods, the degree to which each cosmetic surgery user is influenced by popular trends in cosmetic surgery behavior is determined, including: Based on the penetration of the medical aesthetics characteristics and trends of the primary medical aesthetics user group to which each medical aesthetics user belongs, the weights of each medical aesthetics user's pursuit of popular medical aesthetics techniques and individual suitability for popular medical aesthetics are determined. Using the aforementioned weights, the degree of pursuit of popular cosmetic techniques and the individual suitability of each cosmetic user are weighted and summed to obtain the degree to which each cosmetic user's cosmetic behavior is affected by popular trends.

[0012] In conjunction with the first aspect mentioned above, in some possible implementation methods, the first grouping of different cosmetic surgery users is carried out to obtain several first cosmetic surgery user groups, including: Based on the difference in the consumption amount of medical aesthetic projects between any two medical aesthetic users, determine the first difference value; Based on the differences in frequency values ​​between each type of medical aesthetic procedure in the consumption data of any individual medical aesthetic user, a second difference value is determined. Based on the consumption data of any individual medical aesthetic user, the differences between the medical aesthetic care areas of medical aesthetic projects are analyzed to determine the third difference value; Based on the first difference value, the second difference value, and the third difference value, determine the multi-dimensional feature distance between any two medical aesthetic users; Based on the multi-dimensional feature distance, a clustering algorithm is used to cluster all medical aesthetic users, and the resulting clusters are used as several first medical aesthetic user groups.

[0013] Secondly, the present invention also provides a user profile generation system for medical aesthetics users, including a memory and a processor. The memory is used to store executable computer program code, and the processor is used to call and run the executable computer program code from the memory, causing the system to perform the methods in the first aspect or any possible implementation thereof.

[0014] Thirdly, the present invention also provides a user profile generation device for medical aesthetics users, the device comprising: The data acquisition module is used to acquire consumption data from different medical aesthetic users and popular medical aesthetic data from different medical aesthetic platforms; The first classification module is used to perform the first group division of all medical aesthetic users based on the differences in consumption data of different medical aesthetic users, and obtain several first medical aesthetic user groups. The penetration analysis module is used to analyze the popularity data of medical aesthetics on different medical aesthetics platforms and combine it with the consumption data of different medical aesthetics users within each first medical aesthetics user group to conduct multi-platform medical aesthetics popularity trend analysis and determine the penetration of the popularity trend of medical aesthetics characteristics of each first medical aesthetics user group. The impact analysis module is used to analyze the dynamic characteristics of popular medical aesthetics for each medical aesthetics user based on their consumption data and the popular medical aesthetics data of different medical aesthetics platforms. It also combines the penetration degree of the popular medical aesthetics trend of each medical aesthetics user's first medical aesthetics user group to determine the degree of impact of popular medical aesthetics behavior on each medical aesthetics user. The second classification module is used to further divide all medical aesthetic users into several second medical aesthetic user groups based on the differences in the impact of different medical aesthetic behaviors. The profile generation module is used to generate a user profile for each medical aesthetics user based on the medical aesthetics feature tags corresponding to the first and second medical aesthetics user groups to which each medical aesthetics user belongs.

[0015] Fourthly, the present invention also provides a computer program product comprising: computer program code, which, when run on a computer, causes the computer to execute the user profile generation method for medical aesthetics users as described in the first aspect or any possible implementation thereof.

[0016] Fifthly, the present invention also provides a computer-readable storage medium storing computer program code, which, when run on a computer, causes the computer to execute the user profile generation method for medical aesthetics users as described in the first aspect or any possible implementation thereof.

[0017] This invention has the following beneficial effects: It integrates internal consumption data of medical aesthetics users with popular medical aesthetics data from different external platforms. Based on the differences in consumption data among different medical aesthetics users to divide them into several first-level medical aesthetics user groups, and using popular medical aesthetics data from different platforms, combined with consumption data from different users within each first-level medical aesthetics user group, it conducts multi-platform medical aesthetics trend analysis to determine the penetration of medical aesthetics feature trends among medical aesthetics user groups under the influence of dynamic trends in medical aesthetics features. Furthermore, it analyzes the trends of each medical aesthetics user... This invention analyzes the dynamic characteristics of cosmetic surgery itself and combines the analysis results with the penetration of cosmetic surgery characteristic trends in the first cosmetic surgery user group to determine the degree to which each user's cosmetic surgery behavior is influenced by popular trends. Based on the differences in the degree of influence of different users' cosmetic surgery behaviors, the users are further divided into several second cosmetic surgery user groups, thereby capturing changes in users' cosmetic surgery behavior. Finally, based on the cosmetic surgery characteristic tags corresponding to each user's first and second cosmetic surgery user groups, a user profile is generated for each user. This invention, by analyzing the degree to which each user's cosmetic surgery behavior is influenced by popular trends, can obtain more complete user cosmetic surgery characteristic tags, achieving trend updates and high-precision prediction of cosmetic surgery user profiles, enhancing the ability to understand users' potential needs and emerging projects, and improving the accuracy of user profile generation. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the steps of a user profile generation method for medical aesthetics users according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a user profile generation system for medical aesthetics users according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a user profile generation device for medical aesthetics users according to an embodiment of the present invention. Detailed Implementation

[0020] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings.

[0021] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0022] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0023] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0024] It should be noted that the concepts of "first" and "second" mentioned in this invention are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0025] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of the present invention, this should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of the present invention, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.

[0026] Furthermore, it is understood that the data involved in the technical solutions of this invention (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions. Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains, and all parameters or indicators in the formulas involved in this invention are normalized values ​​that have eliminated the influence of dimensions.

[0027] The following will describe in detail, with reference to the accompanying drawings, a user profile generation method and system for medical aesthetics users provided by an embodiment of the present invention.

[0028] Figure 1This diagram illustrates the basic flowchart of a user profile generation method for medical aesthetics users provided by an embodiment of the present invention. Figure 1 As shown, the method specifically includes the following steps: Step S100: Obtain consumption data of different medical aesthetic users and medical aesthetic popularity data of different medical aesthetic platforms.

[0029] Long-term consumption records and habits of cosmetic surgery users can help uncover potential consumption trends and preferences. Therefore, order systems and databases can be used to obtain consumption data from different cosmetic surgery users. This consumption data, also known as internal data of cosmetic surgery users, refers to the historical consumption transaction data of different users. In this embodiment of the invention, the acquired consumption data from different cosmetic surgery users includes: project category (e.g., injectable, skin-related, surgical, etc.), project tags (e.g., moisturizing, whitening, anti-aging, firming, etc.), consumption amount, consumption time, discount information, etc.

[0030] Meanwhile, popular medical aesthetic data from different medical aesthetic platforms are obtained. Among them, popular medical aesthetic data, also known as external trend data, refers to the publicly available data of each medical aesthetic platform. In this embodiment of the invention, the popular medical aesthetic data from different medical aesthetic platforms includes: (1) Project / keyword volume: the frequency and growth rate of specific medical aesthetic projects (such as "photorejuvenation", "fotona 4D", "gold microneedling") being mentioned, discussed, liked, and collected; (2) Popular notes / posts: title, body keywords, hashtags, interaction volume (number of likes, collections, and comments), etc.

[0031] This allows us to obtain consumption data from different medical aesthetics users and popular medical aesthetics data from different medical aesthetics platforms, in order to facilitate subsequent operations.

[0032] Step S200: Based on the differences in consumption data among different medical aesthetic users, perform the first group division of all medical aesthetic users to obtain several first medical aesthetic user groups.

[0033] Traditional RFM models typically segment cosmetic surgery users from a single value perspective, focusing primarily on dimensions such as spending power, frequency of purchase, and amount spent. However, the needs of users in the cosmetic surgery industry are complex and diverse, and a single value perspective cannot fully reflect their characteristics. Therefore, segmenting cosmetic surgery users requires incorporating more dimensions of analysis. In addition to traditional spending power and frequency, user preferences should be considered, such as the type of treatment chosen (anti-aging, hydration, minimally invasive procedures, etc.), treatment areas, time periods, and specific needs for cosmetic services. These dimensions allow for the creation of more business-meaning and practically valuable initial tags for users, such as "high-end anti-aging seekers," "frequent basic skincare customers," and "potential eye plastic surgery interested groups." These tags not only more accurately reflect user needs and behavioral characteristics but also provide a more robust baseline profile for subsequent user analysis and targeted marketing.

[0034] Therefore, step S200 first divides all medical aesthetic users into groups based on the differences in consumption data between different medical aesthetic users, such as differences in multiple dimensions such as spending power, frequency of various medical aesthetic projects, and medical aesthetic areas. These medical aesthetic user groups are referred to as the first medical aesthetic user group.

[0035] Furthermore, step S200 above performs a first group division on all medical aesthetic users, resulting in several first medical aesthetic user groups, including: determining a first difference value based on the difference in the consumption amount of medical aesthetic projects between any two medical aesthetic users' consumption data; determining a second difference value based on the difference in the frequency value of each type of medical aesthetic project between any two medical aesthetic users' consumption data; determining a third difference value based on the difference in the medical aesthetic care areas of medical aesthetic projects between any two medical aesthetic users' consumption data; determining a multi-dimensional feature distance between any two medical aesthetic users based on the first difference value, the second difference value, and the third difference value; and using a clustering algorithm to cluster all medical aesthetic users based on the multi-dimensional feature distance, and taking the resulting clusters as several first medical aesthetic user groups.

[0036] In this embodiment of the invention, firstly, the average of all consumption amounts in the consumption data of each medical aesthetics user is calculated, and then the average is normalized using a normalization function (such as the maximum-minimum normalization function) to obtain the consumption capacity of each medical aesthetics user.

[0037] Secondly, the types of cosmetic procedures performed by each user are sorted in a fixed order based on their consumption data, such as anti-aging, moisturizing, and minimally invasive procedures, thus forming a fixed set of cosmetic procedure types. Then, a normalization function (such as a maximum-minimum normalization function) is used to normalize the number of times each user performs each type of cosmetic procedure, thus obtaining the frequency value of each user for each cosmetic procedure type. Finally, each frequency value of a user's cosmetic procedure type is mapped to the fixed set of cosmetic procedure types for each user, ensuring that each cosmetic procedure type has its corresponding frequency value.

[0038] Next, based on the cosmetic care areas performed in each cosmetic user's consumption data, a set of cosmetic care areas for each cosmetic user is constructed.

[0039] Finally, based on the three dimensions determined above—spending power, frequency of different cosmetic procedures, and treatment areas—all cosmetic users are clustered. Before performing clustering, a metric for the multi-dimensional feature distance between any two cosmetic users is defined, namely: Regarding the spending power of medical aesthetics users, the difference between the spending power of any two medical aesthetics users is taken and the absolute value is used to determine the difference in spending power between any two medical aesthetics users, also known as the first difference value. For the types and frequency values ​​of cosmetic procedures for medical aesthetic users, the difference between the frequency values ​​of each type of cosmetic procedure for any two medical aesthetic users is calculated, and the absolute values ​​are summed together. Then, a normalization function (such as the maximum value minus the minimum value normalization function) is used to normalize the difference in the types of cosmetic procedures for any two medical aesthetic users, which is also called the second difference value. For the set of cosmetic care areas of cosmetic users, the correlation coefficient of any two cosmetic care areas of cosmetic users is calculated using the Jaccard correlation coefficient, and the difference between the value 1 and the correlation coefficient is calculated to determine the difference of cosmetic care areas of any two cosmetic users, also known as the third difference value. Thus, by using Euclidean distance to calculate the multi-dimensional feature distance between any two medical aesthetic users based on the differences in the above three dimensions, the distance can be calculated.

[0040] Therefore, based on the multi-dimensional feature distance between any two cosmetic surgery users, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering method is used to cluster all cosmetic surgery users, thereby identifying several user groups, which are referred to as the first cosmetic surgery user groups. Each first cosmetic surgery user group can reflect three different dimensions of cosmetic surgery user characteristics, and each dimension can reflect the specific characteristics of the user group in that dimension.

[0041] Several historical medical aesthetics user groups and their consumption data are obtained in advance. Based on the three dimensions of consumption data of each user in each historical medical aesthetics user group, namely, the consumption capacity, the frequency value of the corresponding medical aesthetics project type, and the corresponding medical aesthetics treatment area, a medical aesthetics characteristic tag is manually formulated for each historical medical aesthetics user group, such as high net worth - high frequency maintenance - facial refinement type, rational and practical - milestone ritual - skin management type, and novelty experience - low frequency trial - facial care type.

[0042] Based on the three dimensions of information in the consumption data—spending capacity, frequency of different cosmetic procedures, and treatment areas—each primary cosmetic user group is matched with each of the aforementioned historical cosmetic user groups to determine the most matching historical cosmetic user group for each primary user group. Since the specific implementation process of data matching is well-known to those skilled in the art, it will not be elaborated here. The feature tags of the most matching historical cosmetic user group for each primary user group are used as its own cosmetic feature labels, thus obtaining the first feature labels for each primary user group for subsequent use.

[0043] Step S300: Based on the medical aesthetics popularity data of different medical aesthetics platforms, and combined with the consumption data of different medical aesthetics users within each first medical aesthetics user group, conduct multi-platform medical aesthetics popularity trend analysis to determine the penetration degree of medical aesthetics characteristic popularity trend for each first medical aesthetics user group.

[0044] Step S200 above analyzed the consumption data of different medical aesthetic users to segment all medical aesthetic users into groups and assigned specific feature tags to each group. These tags are static features generated based on the users' current consumption data. However, due to the nature of the medical aesthetic industry, its development is subject to certain trends, and user needs will continue to change as people's aesthetic concepts evolve and individual medical aesthetic needs are updated. This change is not only reflected in the diversification of needs but may also give rise to new market segments and potential demands. Therefore, in order to more accurately capture user needs and grasp the future development trend of the medical aesthetic market, it is necessary to consider these dynamic factors and not just rely on the analysis of static tags.

[0045] Therefore, in step S300 above, based on the medical aesthetics popularity data of different medical aesthetics platforms and combined with the consumption data of different medical aesthetics users within each first medical aesthetics user group, a multi-platform medical aesthetics popularity trend analysis is conducted to determine the penetration degree of the medical aesthetics characteristic popularity trend of each first medical aesthetics user group.

[0046] Furthermore, in step S300 above, determining the penetration of the medical aesthetics characteristic trends for each first medical aesthetics user group includes: Step S301: Based on the frequency of occurrence of medical aesthetic keywords in the recent medical aesthetic trends data of each medical aesthetic platform, and the frequency of co-occurrence of each medical aesthetic keyword with other medical aesthetic keywords in the same information source on each medical aesthetic platform, determine the linked keywords of each medical aesthetic keyword on each medical aesthetic platform, and the comprehensive high-frequency linkage between each medical aesthetic keyword and its linked keywords.

[0047] In the medical aesthetics industry, the extension of trends does not originate from a single procedure, but rather from the collaborative development and evolution of multiple related procedures or technologies. The interaction of different technological modules drives overall industry trends, leading to overlap and interweaving of high-frequency keywords in content. In this process, medical aesthetics technologies continuously innovate and integrate, jointly promoting the industry's diversification and in-depth development. Therefore, when different high-frequency medical aesthetics keywords frequently appear in the same content source, it indicates a higher correlation and synergy among them under current trends, reflecting the close connection between technology and demand.

[0048] Therefore, relevant characteristic terms from the medical aesthetics platform's popular medical aesthetics data, including those related to medical aesthetics project types, treatment areas, and techniques, are collectively used as keywords for medical aesthetics-related terms, thus constructing a medical aesthetics keyword set. When a certain medical aesthetics keyword in the keyword set appears frequently with other keywords from the same information source across various medical aesthetics platforms, it indicates that the other keyword is a linked keyword for that particular medical aesthetics keyword, and the overall high-frequency correlation between the particular medical aesthetics keyword and the linked keyword is relatively high.

[0049] Furthermore, in step S301 above, determining the linked keywords for each medical aesthetics keyword on each medical aesthetics platform, and the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords, includes: determining the high frequency of each medical aesthetics keyword based on the frequency of occurrence of medical aesthetics keywords in recent medical aesthetics trend data on each medical aesthetics platform; determining the linkage occurrence coefficient between each medical aesthetics keyword and other medical aesthetics keywords based on the frequency of co-occurrence of each medical aesthetics keyword and other medical aesthetics keywords in the same information source on each medical aesthetics platform; determining the high-frequency linkage between each medical aesthetics keyword and other medical aesthetics keywords based on the high frequency and linkage occurrence coefficient; determining the linked keywords for each medical aesthetics keyword on each medical aesthetics platform among all other medical aesthetics keywords; and determining the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords on each medical aesthetics platform based on the high-frequency linkage between each medical aesthetics keyword and its linked keywords.

[0050] In this embodiment of the invention, firstly, based on any one of the medical aesthetics platforms, according to the recent (e.g., within 2 months) medical aesthetics trend data (i.e., the information published on the platform), the occurrence frequency of each medical aesthetics keyword in the medical aesthetics keyword set is counted. Then, the occurrence frequency of each medical aesthetics keyword is normalized using a normalization function (e.g., maximum-minimum value normalization) to determine the high frequency of each medical aesthetics keyword.

[0051] Secondly, for each medical aesthetics keyword, we analyze its interaction with other medical aesthetics keywords. Specifically, for each medical aesthetics keyword, we iterate through the number of times it appears in the same information source with each other medical aesthetics keyword. We then use a normalization function (such as maximum-minimum normalization) to normalize the number of times it appears in the same information source, thereby determining the interaction coefficient between each medical aesthetics keyword and any other medical aesthetics keyword.

[0052] Next, when each medical aesthetics keyword maintains a relatively high frequency of occurrence with any other medical aesthetics keyword, the higher the correlation coefficient between the two, the more they align with the industry's extension trend. This determines the high-frequency correlation between each medical aesthetics keyword and any other medical aesthetics keyword. Therefore, the average frequency of each medical aesthetics keyword with any other medical aesthetics keyword is calculated and multiplied by the correlation coefficient between each medical aesthetics keyword and any other medical aesthetics keyword (as an adjustment factor) to determine the high-frequency correlation between each medical aesthetics keyword and any other medical aesthetics keyword.

[0053] Next, a high-frequency linkage threshold is pre-set (e.g., 0.85). Other medical aesthetic keywords whose high-frequency linkage with any other medical aesthetic keyword is greater than or equal to this threshold are then selected and used as the linked keywords for each keyword. This yields the linked keywords for each medical aesthetic keyword.

[0054] Finally, the average high-frequency correlation between each medical aesthetics keyword and its associated keywords will be calculated, and this average value will be used as the comprehensive high-frequency correlation between each medical aesthetics keyword and its associated keywords.

[0055] This allows us to obtain the associated keywords for each medical aesthetics keyword on each medical aesthetics platform, as well as the overall high-frequency correlation between each medical aesthetics keyword and its associated keywords.

[0056] Step S302: Based on the differences between the linked keywords of each medical aesthetics keyword on different medical aesthetics platforms, and the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords, determine the popularity trend of each medical aesthetics keyword across multiple platforms.

[0057] Because different medical aesthetics platforms have different user groups, interaction patterns, and content dissemination mechanisms, the popularity trends of medical aesthetics-related information on each platform may exhibit different characteristics. Therefore, it is necessary to fully consider the relative contribution of each medical aesthetics platform to the popularity of different medical aesthetics features. Based on this relative contribution, the overall high-frequency correlation between each medical aesthetics keyword and its associated keywords should be adjusted to obtain the multi-platform popularity trend of each medical aesthetics keyword.

[0058] Furthermore, the determination of the multi-platform medical aesthetics feature popularity trend of each medical aesthetics keyword in step S302 above includes: determining the intersection of each medical aesthetics keyword with each linked keyword on different medical aesthetics platforms to obtain the multi-platform linked medical aesthetics keywords of each medical aesthetics keyword; determining the medical aesthetics feature popularity contribution of each linked keyword of each medical aesthetics keyword on each medical aesthetics platform based on the correlation between each linked keyword of each medical aesthetics keyword on each medical aesthetics platform and the multi-platform linked medical aesthetics keywords; and determining the multi-platform medical aesthetics feature popularity trend of each medical aesthetics keyword based on the comprehensive high-frequency linkage degree and the medical aesthetics feature popularity contribution degree of each medical aesthetics keyword on each medical aesthetics platform.

[0059] In this embodiment of the invention, firstly, the union of all linked keywords on different medical aesthetic platforms for each medical aesthetic keyword is taken, and this union is recorded as the multi-platform linked medical aesthetic keyword for each medical aesthetic keyword.

[0060] Secondly, the correlation coefficient between each medical aesthetics keyword and its associated keywords on each medical aesthetics platform and its associated medical aesthetics keywords across multiple platforms is calculated using the Jaccard correlation coefficient. The correlation coefficient is then normalized using Softmax to determine the contribution of each medical aesthetics keyword to the popularity of its medical aesthetics features on each medical aesthetics platform.

[0061] Finally, the contribution of each medical aesthetic keyword to the popularity of its associated keywords on each medical aesthetic platform is multiplied by the comprehensive high-frequency association degree between each medical aesthetic keyword and its associated keywords on each medical aesthetic platform. The resulting product is then summed across multiple medical aesthetic platforms to determine the popularity trend degree of each medical aesthetic keyword across multiple platforms.

[0062] Step S303: Based on the popularity trend of each medical aesthetics keyword across multiple platforms, and combined with the correlation between all medical aesthetics keywords involved in recent medical aesthetics projects and the multi-platform linked medical aesthetics keywords of each medical aesthetics keyword in the consumption data of all medical aesthetics users in each first medical aesthetics user group, determine the popularity penetration of the medical aesthetics features of each medical aesthetics keyword in the recent medical aesthetics projects of each first medical aesthetics user group.

[0063] The above process analyzes the trend of medical aesthetic features across multiple medical aesthetic platforms and the medical aesthetic user groups segmented by multi-dimensional information from medical aesthetic user consumption data. Based on the recent trend of medical aesthetic features across different user groups, we can infer the possible transmission characteristics of individuals in each group, which helps to more accurately understand the behavioral patterns of individuals within the group.

[0064] In this embodiment of the invention, based on the consumption data of medical aesthetic users, the medical aesthetic projects performed by all medical aesthetic users in each medical aesthetic user group (i.e., the first medical aesthetic user group) in the recent period (e.g., within 2 months) are obtained. If the medical aesthetic projects performed by these medical aesthetic users in the recent period are more in line with the recent popular medical aesthetic characteristics, it can be inferred that the medical aesthetic user group is more likely to try the currently popular related medical aesthetic projects to a greater extent in the present and future.

[0065] Therefore, by utilizing all medical aesthetic keywords related to the medical aesthetic procedures performed by all users within each medical aesthetic user group recently, a set of recent medical aesthetic procedure keywords for each user group is constructed. The correlation coefficient between the recent medical aesthetic procedure keyword set for each user group and the multi-platform linked medical aesthetic keyword sequences for each medical aesthetic keyword is calculated using the Jaccard correlation coefficient. This correlation coefficient is then used as the correlation between the recent medical aesthetic procedure keyword set for each user group and the multi-platform linked medical aesthetic keywords for each medical aesthetic keyword.

[0066] Furthermore, the relevance of the recent medical aesthetics project keyword set of each medical aesthetics user group to the multi-platform linked medical aesthetics keywords of each medical aesthetics keyword is used as a weight. This weight is then used to multiply the multi-platform medical aesthetics feature popularity trend of each medical aesthetics keyword to obtain the popularity penetration degree of the medical aesthetics features of each first medical aesthetics user group's recent medical aesthetics projects and each medical aesthetics keyword.

[0067] Step S304: Calculate the penetration of the recent medical aesthetic procedures and all medical aesthetic keywords of each first medical aesthetic user group to determine the penetration of the medical aesthetic trend of each first medical aesthetic user group.

[0068] In this embodiment of the invention, the popularity penetration of medical aesthetic features of each medical aesthetic user group is accumulated by summing the recent medical aesthetic projects and all medical aesthetic keywords, and the accumulated value is normalized by a normalization function (such as the Sigmoid function) to obtain the popularity penetration of medical aesthetic features of each medical aesthetic user group.

[0069] At this point, we can obtain the penetration of the medical aesthetics characteristics and popular trends of each medical aesthetics user group.

[0070] Step S400: Based on the consumption data of each medical aesthetics user and combined with the medical aesthetics popularity data of different medical aesthetics platforms, conduct a dynamic characteristic analysis of the popular medical aesthetics of each medical aesthetics user, and combine the penetration degree of the medical aesthetics characteristic popularity trend of the first medical aesthetics user group to which each medical aesthetics user belongs to determine the degree of influence of the popular medical aesthetics behavior of each medical aesthetics user on the medical aesthetics behavior.

[0071] Step S300 above analyzed the potential behavioral patterns of individuals within the cosmetic surgery user group, particularly their potential behaviors related to popular cosmetic surgery trends. However, based on the group's behavioral patterns, it is also necessary to consider the individual differences within the group. This requires analyzing the dynamic characteristics of popular cosmetic surgery trends for each user, especially their pursuit of popular techniques and their individual suitability for them. This is because these two characteristics are relatively fixed behavioral traits of cosmetic surgery users and typically do not fluctuate drastically with changes in trends. Therefore, by considering these invariant characteristics in addition to the group's behavioral patterns, we can further refine individual differences, thereby enabling more accurate predictions of individual behavioral responses under specific trends.

[0072] Furthermore, step S400 above involves analyzing the dynamic characteristics of popular cosmetic procedures for each user, including: Step S401: Based on the consumption data of each medical aesthetics user, analyze the distribution of medical aesthetics projects, analyze the pursuit of popular medical aesthetics projects by medical aesthetics users, and determine the degree of pursuit of popular medical aesthetics techniques by each medical aesthetics user.

[0073] Users' pursuit of popular cosmetic medical techniques is essentially about optimizing their self-image and satisfying their personalized needs. This can be analyzed by examining the breadth of cosmetic medical procedures users have undergone in the past. For example, the more types of cosmetic medical procedures a user has undergone and the more times each procedure has been performed, the higher the user's pursuit of popular cosmetic medical techniques is usually.

[0074] Furthermore, in step S401 above, determining the degree of pursuit of popular cosmetic medical techniques for each cosmetic medical user includes: determining the actual degree of each cosmetic medical user's practice for each cosmetic medical procedure based on the number of times each cosmetic medical procedure was performed in each user's consumption data; determining the actual degree of involvement of each cosmetic medical user with cosmetic medical procedures based on the distribution level of the actual degree of involvement of each user with various cosmetic medical procedures; and determining the degree of pursuit of popular cosmetic medical techniques for each user based on the number of cosmetic medical procedures in each user's consumption data and in combination with the actual degree of involvement of each user with cosmetic medical procedures.

[0075] In this embodiment of the invention, the number of different types of cosmetic procedures and the number of times each type was performed are obtained from the consumption data of each cosmetic user. A normalization function (such as max-min normalization) is used to normalize the number of times each type of cosmetic procedure was performed for each user, thereby obtaining the actual progress rate of each type of cosmetic procedure for each user. The higher the actual progress rate of each type of cosmetic procedure for each user, the higher the user's investment in that type of procedure, and the more likely they are to have actual involvement in that type of cosmetic procedure.

[0076] Furthermore, the average of the actual number of each type of cosmetic procedure performed by each user is calculated to determine the true extent of each user's involvement in multiple types of cosmetic procedures. This true extent of involvement is then multiplied by the number of different types of cosmetic procedures performed by each user, and the product is normalized using a normalization function (such as the Sigmoid function) to obtain the degree of pursuit of popular cosmetic techniques by each user.

[0077] Step S402: Based on each medical aesthetic user's consumption data, identify the type of medical aesthetic procedure and combine it with recent medical aesthetic trend data from different medical aesthetic platforms to analyze the suitability of popular medical aesthetic procedures for medical aesthetic users and determine the individual suitability of each medical aesthetic user for popular medical aesthetic procedures.

[0078] Even if users are not very familiar with currently popular cosmetic procedures, their acceptance and trust will greatly increase if these procedures closely match their own needs. High compatibility means that the procedure can meet the user's specific needs, rather than just being popular or trendy; in this way, currently popular cosmetic procedures are more likely to be chosen by users.

[0079] Furthermore, in step S402 above, determining the individual popularity of medical aesthetics for each medical aesthetics user includes: constructing an individual medical aesthetics keyword set for each medical aesthetics user by analyzing medical aesthetics keywords in all medical aesthetics projects based on each user's consumption data; and determining the individual popularity of medical aesthetics for each medical aesthetics user based on the multi-platform medical aesthetics feature popularity trend of all medical aesthetics keywords in the individual medical aesthetics keyword set.

[0080] In this embodiment of the invention, an individual medical aesthetics keyword set is constructed for each medical aesthetics user based on the historical medical aesthetics procedures performed in their consumption data. Then, the popularity trend scores of all medical aesthetics keywords in this individual keyword set across multiple platforms are summed, and the sum is normalized using a normalization function (such as the Sigmoid function) to obtain the individual popularity medical aesthetics suitability score for each medical aesthetics user.

[0081] Step S403: Based on the popularity of the medical aesthetics techniques and the individual's suitability for the popular medical aesthetics techniques, obtain the dynamic characteristics of each medical aesthetics user.

[0082] Based on the above-determined pursuit of popular cosmetic techniques and individual suitability for each cosmetic user, the dynamic characteristics of each cosmetic user are constituted.

[0083] Furthermore, based on the dynamic characteristics of each medical aesthetics user, and by leveraging the penetration of the medical aesthetics trend of the group to which each medical aesthetics user belongs, we can analyze the degree to which each medical aesthetics user is influenced by the medical aesthetics behavior of popular medical aesthetics trends. That is, when the penetration of the medical aesthetics trend of the group to which the medical aesthetics user belongs is higher, we should pay more attention to the degree of pursuit of popular medical aesthetics techniques by the medical aesthetics user; conversely, we should pay more attention to the individual suitability of popular medical aesthetics for the medical aesthetics user.

[0084] Furthermore, in step S400 above, determining the degree to which each medical aesthetic user is affected by the medical aesthetic behavior of popular trends includes: determining the weights of each medical aesthetic user's pursuit of popular medical aesthetic techniques and individual suitability for popular medical aesthetics based on the penetration degree of the medical aesthetic characteristics of the first medical aesthetic user group to which each medical aesthetic user belongs; using the weights, weighted summing of each medical aesthetic user's pursuit of popular medical aesthetic techniques and individual suitability for popular medical aesthetics to obtain the degree to which each medical aesthetic user is affected by the medical aesthetic behavior of popular trends.

[0085] In this embodiment of the invention, based on the dynamic characteristics of each medical aesthetics user and combined with the penetration degree of the medical aesthetics characteristic trend of the first medical aesthetics user group to which each medical aesthetics user belongs, the degree of influence of the medical aesthetics behavior of each medical aesthetics user on the trend is determined by the following formula. : in, This represents the penetration of the medical aesthetics characteristics trend of each medical aesthetics user's primary medical aesthetics user group, and is used as a weight for the pursuit of popular medical aesthetics techniques. This indicates the degree to which each cosmetic surgery user pursues popular cosmetic techniques; This indicates the individual suitability of each cosmetic surgery user for popular cosmetic procedures. The weighting represents the individual's suitability for popular cosmetic procedures.

[0086] This allows us to determine the degree to which each cosmetic surgery user's behavior is influenced by popular trends.

[0087] Step S500: Based on the differences in the impact of different medical aesthetic behaviors among different medical aesthetic users, a second group division is performed on all medical aesthetic users to obtain several second medical aesthetic user groups.

[0088] The above process can determine the degree to which each user is influenced by the medical aesthetic behaviors of popular medical aesthetic features. This degree of influence reflects whether medical aesthetic users will undergo new changes in their current medical aesthetic behaviors under the influence of popular medical aesthetic trends. This will lead to changes in the user's medical aesthetic feature labels, that is, the user may choose to try new medical aesthetic technologies or products under popular trends.

[0089] In this embodiment of the invention, the degree to which medical aesthetic users are influenced by popular medical aesthetic trends is used as the fourth dimension feature of the classification method to remeasure the feature distance between any two medical aesthetic users. That is, the absolute value of the difference between the degree of influence of medical aesthetic behavior of any two medical aesthetic users is calculated as the feature distance. Based on this feature distance, all medical aesthetic users are clustered again by the DBSCAN clustering method to obtain several medical aesthetic user groups after secondary classification. These groups are called the second medical aesthetic user groups.

[0090] Step S600: Generate a user profile for each medical aesthetic user based on the medical aesthetic feature tags corresponding to the first and second medical aesthetic user groups to which each medical aesthetic user belongs.

[0091] Obtain the medical aesthetic feature tags corresponding to each first medical aesthetic user group and each second medical aesthetic user group, and generate a user profile for each medical aesthetic user based on the medical aesthetic feature tags corresponding to the first and second medical aesthetic user groups to which each medical aesthetic user belongs.

[0092] In this embodiment, based on the description in step S200 above, the medical aesthetic feature tag corresponding to the first medical aesthetic user group to which each medical aesthetic user belongs can be obtained. Following the same method of obtaining the medical aesthetic feature tag corresponding to the first medical aesthetic user group to which each medical aesthetic user belongs, several historical medical aesthetic user groups and their consumption data can be obtained in advance. Simultaneously, following the same method of obtaining the medical aesthetic behavior influence of each medical aesthetic user, the medical aesthetic behavior influence of each user in the several additional historical medical aesthetic user groups can be obtained. A medical aesthetic feature tag is then manually assigned to each of these additional historical medical aesthetic user groups, such as "high-potential new type" or "conservative existing type." Furthermore, by matching the medical aesthetic behavior influence of each medical aesthetic user group in the second medical aesthetic user group with the medical aesthetic behavior influence of each of the additional historical medical aesthetic user groups, the most matching historical medical aesthetic user group for each second medical aesthetic user group is determined. The medical aesthetic feature tag of the most matching historical user group for each second medical aesthetic user group is then used as its own medical aesthetic feature tag. Since a second feature tag for each second medical aesthetic user group can be obtained...

[0093] At this point, we can obtain the medical aesthetic feature tags (first feature tag and second feature tag) corresponding to each first medical aesthetic user group and each second medical aesthetic user group.

[0094] Furthermore, the medical aesthetic feature tags (first feature tag and second feature tag) corresponding to the first medical aesthetic user group and the second medical aesthetic user group to which each medical aesthetic user belongs are used as the medical aesthetic feature tags of each medical aesthetic user, thereby completing the generation of user profiles for each medical aesthetic user.

[0095] Based on the same inventive concept, embodiments of the present invention also provide a user profile generation system for medical aesthetics users, such as... Figure 2 As shown, the system includes: a memory, a processor, and computer program code stored in the memory and running on the processor, wherein when the processor executes the computer program code, the system can execute any of the aforementioned user profile generation methods for medical aesthetic users.

[0096] Based on the same inventive concept, embodiments of the present invention also provide a user profile generation device for medical aesthetics users, such as... Figure 3 As shown, the device includes: The data acquisition module is used to acquire consumption data from different medical aesthetic users and popular medical aesthetic data from different medical aesthetic platforms; The first classification module is used to perform the first group division of all medical aesthetic users based on the differences in consumption data of different medical aesthetic users, and obtain several first medical aesthetic user groups. The penetration analysis module is used to analyze the popularity data of medical aesthetics on different medical aesthetics platforms and combine it with the consumption data of different medical aesthetics users within each first medical aesthetics user group to conduct multi-platform medical aesthetics popularity trend analysis and determine the penetration of the popularity trend of medical aesthetics characteristics of each first medical aesthetics user group. The impact analysis module is used to analyze the dynamic characteristics of popular medical aesthetics for each medical aesthetics user based on their consumption data and the popular medical aesthetics data of different medical aesthetics platforms. It also combines the penetration degree of the popular medical aesthetics trend of each medical aesthetics user's first medical aesthetics user group to determine the degree of impact of popular medical aesthetics behavior on each medical aesthetics user. The second classification module is used to further divide all medical aesthetic users into several second medical aesthetic user groups based on the differences in the impact of different medical aesthetic behaviors. The profile generation module is used to generate a user profile for each medical aesthetics user based on the medical aesthetics feature tags corresponding to the first and second medical aesthetics user groups to which each medical aesthetics user belongs.

[0097] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0098] In this embodiment of the invention, the system can be divided into functional modules according to the above method example. For example, each module can correspond to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0099] Based on the same inventive concept, embodiments of the present invention also provide a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute any of the aforementioned methods for generating user profiles for medical aesthetic users.

[0100] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing computer program code, which, when run on a computer, causes the computer to execute any of the aforementioned methods for generating user profiles for medical aesthetic users.

[0101] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for generating user profiles for medical aesthetics users, characterized in that, Includes the following steps: Acquire consumption data from different medical aesthetics users and popular medical aesthetics data from different medical aesthetics platforms; Based on the differences in consumption data among different medical aesthetic users, all medical aesthetic users were first divided into several first medical aesthetic user groups. Based on the medical aesthetics popularity data of different medical aesthetics platforms, and combined with the consumption data of different medical aesthetics users within each first medical aesthetics user group, we conduct multi-platform medical aesthetics popularity trend analysis to determine the penetration of medical aesthetics characteristic popularity trends for each first medical aesthetics user group. Based on the consumption data of each medical aesthetics user and combined with the medical aesthetics popularity data of different medical aesthetics platforms, the dynamic characteristics of popular medical aesthetics are analyzed for each medical aesthetics user. In addition, the penetration degree of the medical aesthetics characteristic popularity trend of each medical aesthetics user's first medical aesthetics user group is combined to determine the degree of influence of popular medical aesthetics behavior on each medical aesthetics user. Based on the differences in the impact of different medical aesthetic behaviors among different medical aesthetic users, all medical aesthetic users are divided into several second medical aesthetic user groups. Based on the medical aesthetic feature tags corresponding to the first and second medical aesthetic user groups to which each medical aesthetic user belongs, a user profile is generated for each medical aesthetic user.

2. The user profile generation method for medical aesthetics users according to claim 1, characterized in that, Determine the penetration of medical aesthetic characteristics and trends for each primary medical aesthetic user group, including: Based on the recent popular medical aesthetics data of each medical aesthetics platform, the frequency of occurrence of medical aesthetics keywords, and the frequency of co-occurrence of each medical aesthetics keyword with other medical aesthetics keywords in the same information source on each medical aesthetics platform, the linked keywords of each medical aesthetics keyword on each medical aesthetics platform, and the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords are determined. Based on the differences between the linked keywords of each medical aesthetics keyword on different medical aesthetics platforms, and the comprehensive high-frequency linkage between each medical aesthetics keyword and its linked keywords, the popularity trend of each medical aesthetics keyword across multiple platforms is determined. Based on the popularity trend of each medical aesthetics keyword across multiple platforms, and combined with the correlation between all medical aesthetics keywords involved in recent medical aesthetics projects and the multi-platform linked medical aesthetics keywords of each medical aesthetics keyword in the consumption data of all medical aesthetics users in each first medical aesthetics user group, the popularity penetration of medical aesthetics features of each medical aesthetics keyword in the recent medical aesthetics projects of each first medical aesthetics user group is determined. The popularity of medical aesthetic features among recent medical aesthetic procedures and all medical aesthetic keywords for each primary medical aesthetic user group was statistically analyzed to determine the penetration of the popularity trend of medical aesthetic features for each primary medical aesthetic user group.

3. The user profile generation method for medical aesthetics users according to claim 2, characterized in that, Determine the associated keywords for each medical aesthetics keyword on each medical aesthetics platform, and the overall high-frequency correlation between each medical aesthetics keyword and its associated keywords, including: Based on the recent popular medical aesthetics data of each medical aesthetics platform, the frequency of occurrence of medical aesthetics keywords is determined; Based on the frequency of each medical aesthetics keyword appearing together with other medical aesthetics keywords in the same information source on each medical aesthetics platform, the correlation coefficient between each medical aesthetics keyword and other medical aesthetics keywords is determined. Based on the frequency and correlation coefficient of each medical aesthetics keyword with other medical aesthetics keywords, the high frequency correlation of each medical aesthetics keyword with other medical aesthetics keywords is determined. Based on the aforementioned high-frequency correlation, the associated keywords for each medical aesthetics keyword on each medical aesthetics platform are determined from all other medical aesthetics keywords. Based on the high-frequency correlation between each medical aesthetics keyword and its associated keywords, the comprehensive high-frequency correlation between each medical aesthetics keyword and its associated keywords on each medical aesthetics platform is determined.

4. The user profile generation method for medical aesthetics users according to claim 2, characterized in that, Determining the popularity trend of each medical aesthetics keyword across multiple platforms includes: Determine the intersection of each medical aesthetics keyword with its associated keywords on different medical aesthetics platforms to obtain the multi-platform associated medical aesthetics keywords for each medical aesthetics keyword; Based on the correlation between each linked keyword of each medical aesthetics keyword on each medical aesthetics platform and the linked medical aesthetics keywords of each multi-platform, the contribution of each linked keyword of each medical aesthetics keyword on each medical aesthetics platform to the popularity of medical aesthetics features is determined. Based on the comprehensive high-frequency correlation and the contribution of the popularity of each medical aesthetics keyword on various medical aesthetics platforms, the popularity trend of each medical aesthetics keyword across multiple platforms is determined.

5. The user profile generation method for medical aesthetics users according to claim 1, characterized in that, The analysis of the dynamic characteristics of popular cosmetic procedures for each user includes: Based on the consumption data of each medical aesthetics user, the distribution of medical aesthetics projects is analyzed, the pursuit of popular medical aesthetics projects by medical aesthetics users is analyzed, and the degree of pursuit of popular medical aesthetics techniques by each medical aesthetics user is determined. Based on each medical aesthetics user's consumption data, the types of medical aesthetics projects are identified, and combined with recent medical aesthetics trend data from different medical aesthetics platforms, the suitability of popular medical aesthetics projects for medical aesthetics users is analyzed to determine the individual suitability of each medical aesthetics user for popular medical aesthetics projects. Based on the popularity of certain cosmetic medical technologies and the individual's suitability for those technologies, the dynamic characteristics of each cosmetic medical user are obtained.

6. The user profile generation method for medical aesthetics users according to claim 5, characterized in that, Determine the level of interest in popular cosmetic techniques among each cosmetic patient, including: Based on each medical aesthetic user's consumption data, the number of times each medical aesthetic procedure was performed was determined to assess the actual degree of each medical aesthetic procedure performed by each user. Based on the distribution level of the actual number of cosmetic procedures performed by each cosmetic user, the actual number of cosmetic procedures involved by each user is determined. Based on the consumption data of each medical aesthetics user, the number of types of medical aesthetics projects is counted, and combined with the actual involvement of each medical aesthetics user in medical aesthetics projects, the degree of pursuit of popular medical aesthetics techniques by each medical aesthetics user is determined.

7. The user profile generation method for medical aesthetics users according to claim 5, characterized in that, Determine the individual suitability of each cosmetic surgery user for popular cosmetic procedures, including: Based on each medical aesthetic user's consumption data, medical aesthetic keywords are extracted from all medical aesthetic projects to construct an individual medical aesthetic keyword set for each medical aesthetic user; Based on the popularity trend of all medical aesthetic keywords in the individual medical aesthetic keyword set across multiple platforms, the individual popularity medical aesthetic suitability of each medical aesthetic user is determined.

8. The user profile generation method for medical aesthetics users according to claim 5, characterized in that, Determine the degree to which each cosmetic surgery user is influenced by popular trends in cosmetic surgery behavior, including: Based on the penetration of the medical aesthetics characteristics and trends of each medical aesthetics user's primary medical aesthetics user group, the weights of each medical aesthetics user's pursuit of popular medical aesthetics techniques and individual suitability for popular medical aesthetics are determined. Using the aforementioned weights, the degree of pursuit of popular cosmetic techniques and the individual suitability of each cosmetic user are weighted and summed to obtain the degree of influence of popular cosmetic trends on each cosmetic user's cosmetic behavior.

9. The user profile generation method for medical aesthetics users according to claim 1, characterized in that, The initial segmentation of different cosmetic surgery users yielded several primary cosmetic surgery user groups, including: Based on the difference in the consumption amount of medical aesthetic projects between any two medical aesthetic users, determine the first difference value; Based on the differences in frequency values ​​between each type of medical aesthetic procedure in the consumption data of any individual medical aesthetic user, a second difference value is determined. Based on the consumption data of any individual medical aesthetic user, the differences between the medical aesthetic care areas of medical aesthetic projects are analyzed to determine the third difference value; Based on the first difference value, the second difference value, and the third difference value, determine the multi-dimensional feature distance between any two medical aesthetic users; Based on the multi-dimensional feature distance, a clustering algorithm is used to cluster all medical aesthetic users, and the resulting clusters are used as several first medical aesthetic user groups.

10. A user profile generation system for medical aesthetics users, characterized in that, The device includes a memory, a processor, and executable computer program code stored in the memory and executable on the processor. When the processor executes the computer program code, it performs a user profile generation method for medical aesthetics users as described in any one of claims 1 to 9.

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