Media big data-based marketing advertisement putting method

Abstract

The invention discloses a marketing advertisement putting method based on media big data, and particularly relates to the technical field of advertisement putting, and the method comprises the steps: building an advertisement mapping database according to the labels of advertisements, the types of users and the advertisement effect, screening out the advertisements highly related to a target user group through similarity calculation, and carrying out the advertisement putting according to the advertisement effect. The method comprises the following steps: determining a promotion user, collecting a push success rate, a jump-out rate and a conversion rate of the promotion user, determining interested information of the promotion user, combining a knowledge graph of a constructed label with a purchase history record of the promotion user, collecting a time difference between adjacent purchase histories of the promotion user, and analyzing a consumption history behavior of the promotion user. The consumption information of the promotion user is determined, the interested information of the promotion user and the consumption information are comprehensively analyzed, the potential value of the user is quantitatively evaluated, the high-quality promotion user is determined, and the advertisement and the user can be accurately matched, the advertisement putting effect is optimized, and the advertisement conversion rate and the putting efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of advertising delivery, and more specifically, to a marketing advertising delivery method based on media big data. Background Art

[0002] With the rapid development of digital advertising, the accuracy and effectiveness of advertising have received more and more attention. Traditional advertising methods are usually based on simple user behavior data and advertising content matching. The existing advertising delivery system lacks effective user portrait construction and intelligent matching mechanism for advertising tags, resulting in low relevance of advertising push, poor user experience, and low advertising conversion rate. At the same time, users' consumption history and behavior are often analyzed in isolation, and fail to effectively combine users' interest preferences and consumption behavior for comprehensive analysis.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a marketing advertisement delivery method based on media big data to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A marketing advertising method based on media big data specifically includes the following steps:

[0007] S1: Build an advertisement mapping database based on advertisement labels, user categories, and advertisement effects. Filter out advertisements that are highly relevant to the target user group through similarity calculations, and determine promotion users based on advertisement effects.

[0008] S2: Collect the push success rate, bounce rate and conversion rate of the promoted users to determine the information of interest to the promoted users;

[0009] S3: By building a knowledge graph of labels and combining it with the purchase history of the promoted user, and collecting the time difference between the adjacent purchase histories of the promoted user, the consumption history behavior of the promoted user is analyzed to determine the consumption information of the promoted user;

[0010] S4: Comprehensively analyze the promotion users’ interest information and consumption information, quantitatively evaluate the users’ potential value, and identify high-quality promotion users.

[0011] In a preferred embodiment, screening out advertisements that are highly relevant to the target user group includes:

[0012] The labels of the advertisements to be placed and the labels of the advertisements in the advertisement mapping database are represented by vectors, and the labels of the advertisements to be placed are marked as: XYm =[XY1,XY2,XY3,……,XY M ], where m = 1, 2, 3, ..., M, where M is a positive integer and m is the label number of the advertisement to be placed. The label of the advertisement in the advertisement mapping database is marked as: SJK ij =[SJK i1 ,SJK i2 ,SJK i3 ,……,SJK iJ ], i=1, 2, 3, ..., I, I is a positive integer, i is the number of the mapping relationship in the advertisement mapping database, j=1, 2, 3, ..., J, J is a positive integer, j is the number of the advertisement label in the i-th mapping relationship;

[0013] The similarity coefficient measures the similarity between tags through Jaccard similarity. The calculation formula of the similarity coefficient is: Among them, XS xy is the similarity coefficient;

[0014] Set a similarity coefficient threshold to filter out advertisements that are highly relevant to the target user group, compare the similarity coefficient of the advertisement to be placed with the similarity coefficient threshold, obtain mapping relationships in the advertisement mapping database that are greater than the similarity coefficient threshold, and regard mapping relationships that are greater than the similarity coefficient threshold as advertisements that are highly relevant to the target user group.

[0015] In a preferred embodiment, determining the promoted user includes:

[0016] Set a click volume threshold to further screen the filtered ads. By comparing the click volume threshold with the click rate of ads that are highly relevant to the target user group, obtain ads that are highly relevant to the target user group and are greater than the click volume threshold, determine the user categories that are greater than the click volume threshold, and mark the user categories that are greater than the click volume threshold as promoted users.

[0017] In a preferred embodiment, determining the information of interest to the promoted user includes:

[0018] The information of interest to the promoted user is represented by the interest deviation coefficient;

[0019] The logic for obtaining the interest deviation coefficient is as follows: obtaining the number of times the promotion user receives different promotion advertisements in the historical records, determining the promotion user's push success rate, and marking the promotion user's push success rate as: CG, where: CS dj The number of times the promotional user clicks on the promotional ad, CS tg The number of times the promotion user received different promotional ads in the historical records;

[0020] Obtain the bounce rate of the promoted users in the historical records, and mark the bounce rate of the promoted users in the historical records as: TC; obtain the conversion rate of the promoted users in the historical records, and mark the conversion rate of the promoted users in the historical records as: ZH;

[0021] Set the push success rate standard value CG yz , Bounce rate standard value TC yz And the conversion rate standard value ZH yz , obtain the deviations of push success rate, bounce rate and conversion rate, and mark the deviations of push success rate, bounce rate and conversion rate as: CG pc TC pc and ZH pc , among which, CG pc =CG-CG yz , TC pc =TC-TC yz ,ZH pc =ZH-ZH yz ;

[0022] Calculate the interest bias coefficient, the calculation formula is: Among them, XQ pc is the interest bias coefficient.

[0023] In a preferred embodiment, determining consumption information of the promoted user includes:

[0024] The consumption information of the promoted user is represented by the consumption correlation coefficient;

[0025] The logic for obtaining the consumption correlation coefficient is as follows: construct a knowledge graph of labels, where entities in the knowledge graph represent product labels, edges represent the correlation between product labels, and edges between adjacent entities have different weights. The weights are determined by determining the number of co-occurrences of each pair of labels in all products, and are standardized by the number of co-occurrences. The Min-Max standardization method is used, and the expression is: Among them, Q(a,b) is the weight between adjacent entities a and b, CS(a,b) is the number of co-occurrences between adjacent entities a and b, and min cs is the minimum value of the co-occurrence count of all label pairs, max cs is the maximum value of the co-occurrence counts of all label pairs;

[0026] According to the purchase history of the promoted user, determine the length of time from the promoted user's purchase of the product to the current time, and mark the length of time from the promoted user's purchase of the product to the current time as: T k , where k = 1, 2, 3, ..., K, K is a positive integer, and k is the type of purchased goods;

[0027] According to the label of the promotional advertisement, determine the entity of the promotional advertisement and mark the entity of the promotional advertisement as: GGST r , r=1, 2, 3, ..., R, R is a positive integer, r is the ID of the promotion advertisement entity, and according to the purchase history of the promotion user, the entity of the promotion user who purchased the product is determined, and the entity of the promotion user who purchased the product is marked as: YHST P , p = 1, 2, 3, ..., P, P is a positive integer, p is the ID of the entity that the user purchases the commodity, and the consumption correlation coefficient is calculated. The calculation formula is: Among them, GL xf is the consumption correlation coefficient.

[0028] In a preferred embodiment, the consumption information of the promoted user is represented by a consumption performance coefficient, including:

[0029] The logic for obtaining the consumption performance coefficient is as follows: according to the user's purchase history, determine the time difference between the adjacent purchase history of the promoted user, and mark the time difference between the adjacent purchase history of the promoted user as: SJ j , where j = 1, 2, 3, ..., J, J is a positive integer, and j is the number of the adjacent purchase history;

[0030] Set the time difference threshold and mark it as: YZ sj , compare the time difference between the adjacent purchase histories of the promoted users with the time difference threshold, obtain the time difference between the adjacent purchase histories of the promoted users that is greater than the time threshold, and re-mark the time difference between the adjacent purchase histories of the promoted users that is greater than the time threshold as: SJ y , where y=1, 2, 3, ..., Y, Y is a positive integer, and y is the serial number of the time difference between the adjacent purchase histories of the promoted user that is greater than the time threshold;

[0031] Calculate the consumption performance coefficient, the calculation formula is: Among them, BX xf is the consumption performance coefficient.

[0032] In a preferred embodiment, determining high-quality promotion users includes:

[0033] Through comprehensive analysis of interest information and consumption information, the interest deviation coefficient, consumption correlation coefficient and consumption performance coefficient are weighted and calculated to construct a promotion evaluation model and generate a promotion evaluation coefficient. The calculation formula is: Among them, PG tg is the promotion evaluation coefficient, α1, α2, and α3 are the proportional coefficients of interest deviation coefficient, consumption relevance coefficient, and consumption performance coefficient, respectively, and α1, α2, and α3 are all greater than 0;

[0034] Set a promotion evaluation coefficient threshold, and compare the promotion evaluation coefficient of each promotion user with the promotion evaluation coefficient threshold. If the promotion evaluation coefficient of the promotion user is greater than the promotion evaluation coefficient threshold, the promotion user will be marked as a high-quality promotion user. If the promotion evaluation coefficient of the promotion user is less than the promotion evaluation coefficient threshold, the promotion user will not be marked.

[0035] Technical effects and advantages of the present invention:

[0036] The present invention proposes an intelligent advertising promotion method based on advertising tags, user behavior analysis and tag knowledge graph. By constructing an advertising mapping database, advertisements that are highly relevant to the target user group are screened out, and the user's push success rate, bounce rate, conversion rate and other data are combined to analyze their interest information. At the same time, the knowledge graph is used in combination with the user's purchase history and time difference to deeply explore the user's consumption behavior, comprehensively evaluate its potential value, and finally identify high-quality promotion users. The present invention helps to accurately match advertisements with users, optimize advertising delivery effects, and improve advertising conversion rates and delivery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0038] Figure 1 The present invention is a flowchart of a marketing advertisement delivery method based on media big data. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1

[0041] Figure 1 A flowchart of a marketing advertisement delivery method based on media big data is provided in the present invention, which specifically includes the following steps:

[0042] S1: Build an advertisement mapping database based on advertisement labels, user categories, and advertisement effects. Filter out advertisements that are highly relevant to the target user group through similarity calculations, and determine promotion users based on advertisement effects.

[0043] S2: Collect the push success rate, bounce rate and conversion rate of the promoted users to determine the information of interest to the promoted users;

[0044] S3: By building a knowledge graph of labels and combining it with the purchase history of the promoted user, and collecting the time difference between the adjacent purchase histories of the promoted user, the consumption history behavior of the promoted user is analyzed to determine the consumption information of the promoted user;

[0045] S4: Comprehensively analyze the promotion users’ interest information and consumption information, quantitatively evaluate the users’ potential value, and identify high-quality promotion users.

[0046] Accurate advertising is achieved by determining the advertising delivery tags. Advertising tags are automatically generated through natural language processing (NLP) and machine learning (ML) technologies, including:

[0047] Through a series of preprocessing steps, it is transformed into a format that can be used for machine learning and natural language processing;

[0048] By calculating the frequency of words in the ad copy and the prevalence in all ad copies, it helps identify the most discriminative keywords and extract meaningful features from the ad copy;

[0049] By building a classification model, the advertising copy is mapped to predefined advertising label categories. Classification models include machine learning models such as support vector machines (SVM), random forests or neural networks, which can be used for advertising label classification. The model learns the relationship between advertising copy and labels, and can eventually automatically generate labels for new advertising copy.

[0050] Identifying different types of users is a key step in targeted advertising, personalized recommendations, and market segmentation. Based on the user's various behavioral records on the platform, such as browsing history, search history, purchase history, etc., users are divided through clustering algorithms to obtain users in different clusters.

[0051] Based on the label of the advertisement, the advertisement is initially delivered, and according to the number of clicks on the advertisement by users of different categories, the user category with the best advertisement effect is determined, and an advertisement mapping database is constructed. The specific steps of constructing the advertisement mapping database include:

[0052] The advertisement mapping database contains multiple tables that record the delivery effects of different advertisement tags and user categories, including an advertisement tag table, a user category table, and an advertisement effect table;

[0053] After each advertisement is placed, the advertisement placement effect is collected and entered into the advertisement effect table, and the advertisement tag, user category and advertisement effect data are associated, for example: advertisement tag: electronic product + limited time offer → user category: potential buyer → click rate = 5%;

[0054] As advertising continues, new combinations of advertising tags and user categories will be continuously added to the database.

[0055] Perform similarity analysis on the labels of ads to be placed and the labels of ads in the ad mapping database, calculate the similarity coefficient, and set the similarity coefficient threshold to determine whether the labels of ads to be placed and the labels of ads in the ad mapping database are similar enough to ensure that the labels of ads to be placed match the user behavior well.

[0056] Among them, the label of the advertisement to be placed and the label of the advertisement in the advertisement mapping database are represented by a vector, and the label of the advertisement to be placed is marked as: XY m =[XY1,XY2,XY3,……,XY M ], where m = 1, 2, 3, ..., M, where M is a positive integer and m is the label number of the advertisement to be placed. The label of the advertisement in the advertisement mapping database is marked as: SJK ij =[SJK i1 ,SJK i2 ,SJK i3 ,……,SJK iJ ], i=1, 2, 3, ..., I, I is a positive integer, i is the number of the mapping relationship in the advertisement mapping database, j=1, 2, 3, ..., J, J is a positive integer, j is the number of the advertisement label in the i-th mapping relationship;

[0057] It should be noted that the mapping relationship represents the user category and the advertising effect corresponding to the advertising label.

[0058] The similarity coefficient measures the similarity between tags through Jaccard similarity. The calculation formula of the similarity coefficient is: Among them, XS xy is the similarity coefficient;

[0059] Set a similarity coefficient threshold to filter out advertisements that are highly relevant to the target user group, compare the similarity coefficient of the advertisement to be placed with the similarity coefficient threshold, obtain mapping relationships in the advertisement mapping database that are greater than the similarity coefficient threshold, and use the mapping relationships that are greater than the similarity coefficient threshold as advertisements that are highly relevant to the target user group;

[0060] Set a click volume threshold to further screen the filtered ads. By comparing the click volume threshold with the click rate of ads that are highly relevant to the target user group, obtain ads that are highly relevant to the target user group and are greater than the click volume threshold, determine the user categories that are greater than the click volume threshold, and mark the user categories that are greater than the click volume threshold as promoted users.

[0061] The advantages of obtaining promotional users through screening include:

[0062] By setting the similarity coefficient and click volume threshold, you can filter out irrelevant or poorly performing ads, leaving only those ads that are most effective for the target user group, which can greatly increase click-through rate and conversion rate and reduce advertising budget waste;

[0063] As advertising data accumulates, the system can continuously optimize advertising strategies based on click volume and similarity analysis. For example, if an ad has an unusually high click-through rate among a certain user group, the system can further promote it to a wider user group.

[0064] Through this data-based ad screening and optimization method, advertising platforms can more accurately understand the relationship between different advertisements and user groups, thereby providing stronger data support for advertising placement decisions.

[0065] By collecting the interest information and consumption information of the promoted users, the interest information of the promoted users is represented by the interest deviation coefficient, and the consumption information of the promoted users is represented by the consumption correlation coefficient and the consumption performance coefficient.

[0066] The logic for obtaining the interest deviation coefficient is as follows: obtaining the number of times the promotion user receives different promotion advertisements in the historical records, determining the promotion user's push success rate, and marking the promotion user's push success rate as: CG, where: CS dj The number of times the promotional user clicks on the promotional ad, CS tg The number of times the promotion user received different promotional ads in the historical records;

[0067] Obtain the bounce rate of the promoted users in the historical records, and mark the bounce rate of the promoted users in the historical records as: TC; obtain the conversion rate of the promoted users in the historical records, and mark the conversion rate of the promoted users in the historical records as: ZH;

[0068] It should be noted that the bounce rate refers to the proportion of promoted users who leave the website after viewing only one page after clicking on the advertisement, and the conversion rate refers to the proportion of promoted users who complete the expected behavior of the advertisement. The higher the bounce rate of promoted users, the more seriously the promoted users will browse the advertisement content and they usually have greater interest and patience in the advertisement. The higher the conversion rate of promoted users, the more likely the promoted users will make purchases after browsing the advertisement.

[0069] Set the push success rate standard value CG yz , Bounce rate standard value TC yz And the conversion rate standard value ZH yz , obtain the deviations of push success rate, bounce rate and conversion rate, and mark the deviations of push success rate, bounce rate and conversion rate as: CG pc,TC pc and ZH pc , among which, CG pc =CG-CG yz , TC pc =TC-TC yz ,ZH pc =ZH-ZH yz ;

[0070] Calculate the interest bias coefficient, the calculation formula is: Among them, XQ pc is the interest bias coefficient.

[0071] It can be seen from the formula that the larger the interest deviation coefficient is, the smaller the push success rate, bounce rate and conversion rate of the promoted user are, which means that the promoted user may not be interested in the push of advertisements.

[0072] The advantages of the consumption correlation coefficient include:

[0073] By building associations between tags, the knowledge graph can help identify the deep associations between user purchase behaviors and ad tags, which enables ads to be accurately matched to content that is most relevant to user needs and interests, thereby increasing ad click-through rates and conversion rates.

[0074] By establishing associations between user interest tags and product tags, the knowledge graph can effectively understand user interest changes and purchase potential. Through these tags, the advertising system can quickly adapt to dynamically changing user needs and behaviors and achieve personalized advertising recommendations.

[0075] Knowledge graphs can support in-depth analysis of the relationships between different tags. For example, by analyzing the co-occurrence pattern of a tag (such as "sports shoes") and other related tags (such as "sports accessories"), advertisers can determine which tag combinations are most attractive to specific user groups.

[0076] Knowledge graphs can mine cross-category relationships between product tags, which makes cross-category advertising possible.

[0077] The logic for obtaining the consumption correlation coefficient is as follows: construct a knowledge graph of labels, where entities in the knowledge graph represent product labels, edges represent the correlation between product labels, and edges between adjacent entities have different weights. The weights are determined by determining the number of co-occurrences of each pair of labels in all products, and are standardized by the number of co-occurrences. The Min-Max standardization method is used, and the expression is: Among them, Q(a,b) is the weight between adjacent entities a and b, CS(a,b) is the number of co-occurrences between adjacent entities a and b, and min csis the minimum value of the co-occurrence count of all label pairs, max cs is the maximum value of the co-occurrence counts of all label pairs;

[0078] It should be noted that the number of co-occurrences refers to the number of times these two tags appear in the same product at the same time.

[0079] According to the purchase history of the promoted user, determine the length of time from the promoted user's purchase of the product to the current time, and mark the length of time from the promoted user's purchase of the product to the current time as: T k , where k = 1, 2, 3, ..., K, K is a positive integer, and k is the type of purchased goods;

[0080] According to the label of the promotional advertisement, determine the entity of the promotional advertisement and mark the entity of the promotional advertisement as: GGST r , r=1, 2, 3, ..., R, R is a positive integer, r is the ID of the promotion advertisement entity, and according to the purchase history of the promotion user, the entity of the promotion user who purchased the product is determined, and the entity of the promotion user who purchased the product is marked as: YHST P , p = 1, 2, 3, ..., P, P is a positive integer, p is the ID of the entity that the user purchases the commodity, and the consumption correlation coefficient is calculated. The calculation formula is: Among them, GL xf is the consumption correlation coefficient.

[0081] From the formula, we can see that the larger the consumption correlation coefficient is, the more likely the promoted user is to pay attention to the advertisement in the future, and the higher the correlation between the promoted advertisement and the user, the more likely the user is to purchase goods based on the promoted advertisement.

[0082] The logic for obtaining the consumption performance coefficient is as follows: according to the user's purchase history, determine the time difference between the adjacent purchase history of the promoted user, and mark the time difference between the adjacent purchase history of the promoted user as: SJ j , where j = 1, 2, 3, ..., J, J is a positive integer, and j is the number of the adjacent purchase history;

[0083] Set the time difference threshold and mark it as: YZ sj , compare the time difference between the adjacent purchase histories of the promoted users with the time difference threshold, obtain the time difference between the adjacent purchase histories of the promoted users that is greater than the time threshold, and re-mark the time difference between the adjacent purchase histories of the promoted users that is greater than the time threshold as: SJ y , where y=1, 2, 3, ..., Y, Y is a positive integer, and y is the serial number of the time difference between the adjacent purchase histories of the promoted user that is greater than the time threshold;

[0084] It should be noted that the time difference threshold is set by professional staff and is a specific length of time used to measure the frequency of user purchases.

[0085] Calculate the consumption performance coefficient, the calculation formula is: Among them, BX xf is the consumption performance coefficient.

[0086] From the formula, we can see that the larger the consumption performance coefficient is, the longer the time interval between each consumption of the user may be, which means that the user's purchase frequency is low, which means that they may not be regular consumers of this type of product, or their consumption habits are not frequent. Therefore, the more users need to be promoted through advertising to activate their consumption needs.

[0087] Through comprehensive analysis of interest information and consumption information, the interest deviation coefficient, consumption correlation coefficient and consumption performance coefficient are weighted and calculated to construct a promotion evaluation model and generate a promotion evaluation coefficient. The calculation formula is: Among them, PG tg is the promotion evaluation coefficient, α1, α2, and α3 are the proportional coefficients of interest deviation coefficient, consumption correlation coefficient, and consumption performance coefficient, respectively, and α1, α2, and α3 are all greater than 0.

[0088] It can be seen from the formula that the larger the interest deviation coefficient is and the smaller the consumption correlation coefficient and the consumption performance coefficient are, the smaller the promotion evaluation coefficient is, which means that the promotion user does not need promotional advertising for promotion. Conversely, the smaller the interest deviation coefficient is and the larger the consumption correlation coefficient and the consumption performance coefficient are, the larger the promotion evaluation coefficient is, which means that the promotion user does not need promotional advertising for promotion.

[0089] A promotion evaluation coefficient threshold is set, and the promotion evaluation coefficient of each promotion user is compared with the promotion evaluation coefficient threshold. If the promotion evaluation coefficient of the promotion user is greater than the promotion evaluation coefficient threshold, the promotion user is marked as a high-quality promotion user, indicating that the more the promotion user needs promotion advertisements for promotion, the more likely the promotion user is to make a purchase. If the promotion evaluation coefficient of the promotion user is less than the promotion evaluation coefficient threshold, the promotion user is not marked.

[0090] It should be noted that the promotion evaluation coefficient is used to quantitatively evaluate the potential value of users, so as to make more accurate advertising decisions. By screening out high-quality promotion users, personalized advertising can be delivered to high-quality promotion users, which helps to improve the effectiveness of advertising delivery and ensure that advertising resources are used efficiently, thereby improving the overall marketing return rate.

[0091] The present invention proposes an intelligent advertising promotion method based on advertising tags, user behavior analysis and tag knowledge graph. By constructing an advertising mapping database, advertisements that are highly relevant to the target user group are screened out, and the user's push success rate, bounce rate, conversion rate and other data are combined to analyze their interest information. At the same time, the knowledge graph is used in combination with the user's purchase history and time difference to deeply explore the user's consumption behavior, comprehensively evaluate its potential value, and finally identify high-quality promotion users. The present invention helps to accurately match advertisements with users, optimize advertising delivery effects, and improve advertising conversion rates and delivery efficiency.

[0092] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0093] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0094] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0095] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0096] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0098] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0099] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A marketing advertising method based on media big data, characterized in that: The specific steps include: S1: Build an advertisement mapping database based on advertisement labels, user categories, and advertisement effects. Filter out advertisements that are highly relevant to the target user group through similarity calculations, and determine promotion users based on advertisement effects. S2: Collect the push success rate, bounce rate and conversion rate of the promoted users to determine the information of interest to the promoted users; S3: By building a knowledge graph of labels and combining it with the purchase history of the promoted user, and collecting the time difference between the adjacent purchase histories of the promoted user, the consumption history behavior of the promoted user is analyzed to determine the consumption information of the promoted user; S4: Comprehensively analyze the promotion users’ interest information and consumption information, quantitatively evaluate the users’ potential value, and identify high-quality promotion users.

2. A marketing advertising method based on media big data according to claim 1, characterized in that: Filter out ads that are highly relevant to the target user group, including: The labels of the advertisements to be placed and the labels of the advertisements in the advertisement mapping database are represented by vectors, and the labels of the advertisements to be placed are marked as: XY m =[XY1,XY2,XY3,……,XY M ], where m = 1, 2, 3, ..., M, where M is a positive integer and m is the label number of the advertisement to be placed. The label of the advertisement in the advertisement mapping database is marked as: SJK ij =[SJK i1 ,SJK i2 ,SJK i3 ,……,SJK iJ ], i=1, 2, 3, ..., I, I is a positive integer, i is the number of the mapping relationship in the advertisement mapping database, j=1, 2, 3, ..., J, J is a positive integer, j is the number of the advertisement label in the i-th mapping relationship; The similarity coefficient measures the similarity between tags through Jaccard similarity. The calculation formula of the similarity coefficient is: Among them, XS xy is the similarity coefficient; Set a similarity coefficient threshold to filter out advertisements that are highly relevant to the target user group, compare the similarity coefficient of the advertisement to be placed with the similarity coefficient threshold, obtain mapping relationships in the advertisement mapping database that are greater than the similarity coefficient threshold, and regard mapping relationships that are greater than the similarity coefficient threshold as advertisements that are highly relevant to the target user group.

3. A marketing advertising method based on media big data according to claim 2, characterized in that: Identify promotion users, including: Set a click volume threshold to further screen the filtered ads. By comparing the click volume threshold with the click rate of ads that are highly relevant to the target user group, obtain ads that are highly relevant to the target user group and are greater than the click volume threshold, determine the user categories that are greater than the click volume threshold, and mark the user categories that are greater than the click volume threshold as promoted users.

4. The method for delivering marketing advertisements based on media big data according to claim 3 is characterized in that: Determine the information that users are interested in, including: The information of interest to the promoted user is represented by the interest deviation coefficient; The logic for obtaining the interest deviation coefficient is as follows: obtaining the number of times the promotion user receives different promotion advertisements in the historical records, determining the promotion user's push success rate, and marking the promotion user's push success rate as: CG, where: CS dj The number of times the promotional user clicks on the promotional ad, CS tg The number of times the promotion user received different promotional ads in the historical records; Obtain the bounce rate of the promoted users in the historical records, and mark the bounce rate of the promoted users in the historical records as: TC; obtain the conversion rate of the promoted users in the historical records, and mark the conversion rate of the promoted users in the historical records as: ZH; Set the push success rate standard value CG yz , Bounce rate standard value TC yz And the conversion rate standard value ZH yz , obtain the deviations of push success rate, bounce rate and conversion rate, and mark the deviations of push success rate, bounce rate and conversion rate as: CG pc ,TC pc and ZH pc , among which, CG pc =CG-CG yz , TC pc =TC-TC yz ,ZH pc =ZH-ZH yz ; Calculate the interest bias coefficient, the calculation formula is: Among them, XQ pc is the interest bias coefficient.

5. A marketing advertising method based on media big data according to claim 4, characterized in that: Determine the consumption information of the promoted user, including: The consumption information of the promoted user is represented by the consumption correlation coefficient; The logic for obtaining the consumption correlation coefficient is as follows: construct a knowledge graph of labels, where entities in the knowledge graph represent product labels, edges represent the correlation between product labels, and edges between adjacent entities have different weights. The weights are determined by determining the number of co-occurrences of each pair of labels in all products, and are standardized by the number of co-occurrences. The Min-Max standardization method is used, and the expression is: Among them, Q(a,b) is the weight between adjacent entities a and b, CS(a,b) is the number of co-occurrences between adjacent entities a and b, and min cs is the minimum value of the co-occurrence count of all label pairs, max cs is the maximum value of the co-occurrence counts of all label pairs; According to the purchase history of the promoted user, determine the length of time from the promoted user's purchase of the product to the current time, and mark the length of time from the promoted user's purchase of the product to the current time as: T k , where k = 1, 2, 3, ..., K, K is a positive integer, and k is the type of purchased goods; According to the label of the promotional advertisement, determine the entity of the promotional advertisement and mark the entity of the promotional advertisement as: GGST r , r=1, 2, 3, ..., R, R is a positive integer, r is the ID of the promotion advertisement entity, and according to the purchase history of the promotion user, the entity of the promotion user who purchased the product is determined, and the entity of the promotion user who purchased the product is marked as: YHST P , p = 1, 2, 3, ..., P, P is a positive integer, p is the ID of the entity that the user purchases the commodity, and the consumption correlation coefficient is calculated. The calculation formula is: Among them, GL xf is the consumption correlation coefficient.

6. A marketing advertising method based on media big data according to claim 5, characterized in that: The consumption information of the promoted user is expressed through the consumption performance coefficient, including: The logic for obtaining the consumption performance coefficient is as follows: according to the user's purchase history, determine the time difference between the adjacent purchase history of the promoted user, and mark the time difference between the adjacent purchase history of the promoted user as: SJ j , where j = 1, 2, 3, ..., J, J is a positive integer, and j is the number of the adjacent purchase history; Set the time difference threshold and mark it as: YZ sj , compare the time difference between the adjacent purchase histories of the promoted users with the time difference threshold, obtain the time difference between the adjacent purchase histories of the promoted users that is greater than the time threshold, and re-mark the time difference between the adjacent purchase histories of the promoted users that is greater than the time threshold as: SJ y , where y=1, 2, 3, ..., Y, Y is a positive integer, and y is the serial number of the time difference between the adjacent purchase histories of the promoted user that is greater than the time threshold; Calculate the consumption performance coefficient, the calculation formula is: Among them, BX xf is the consumption performance coefficient.

7. A marketing advertising method based on media big data according to claim 6, characterized in that: Identify high-quality promotion users, including: Through comprehensive analysis of interest information and consumption information, the interest deviation coefficient, consumption correlation coefficient and consumption performance coefficient are weighted and calculated to construct a promotion evaluation model and generate a promotion evaluation coefficient. The calculation formula is: Among them, PG tg is the promotion evaluation coefficient, α1, α2, and α3 are the proportional coefficients of interest deviation coefficient, consumption relevance coefficient, and consumption performance coefficient, respectively, and α1, α2, and α3 are all greater than 0; Set a promotion evaluation coefficient threshold, and compare the promotion evaluation coefficient of each promotion user with the promotion evaluation coefficient threshold. If the promotion evaluation coefficient of the promotion user is greater than the promotion evaluation coefficient threshold, the promotion user will be marked as a high-quality promotion user. If the promotion evaluation coefficient of the promotion user is less than the promotion evaluation coefficient threshold, the promotion user will not be marked.

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