A big data-based advertising push marketing method and system

Abstract

The present invention relates to the field of data processing technology, and in particular to a method and system for advertising push marketing based on big data. The method includes: obtaining the behavioral data of each advertisement when it is pushed to each user in the recent period and the age of each user; determining the rating value of the advertisement by the users to whom the advertisement has been pushed; determining the recognized users among the users to whom the target advertisement has been pushed based on the rating values ​​of all users to whom the advertisement has been pushed; determining the preliminary similarity between advertisements; determining the final similarity between advertisements; determining the recommended predicted value of the advertisement to users to whom the advertisement has not been pushed; and performing advertising push marketing to users based on the order of the recommended predicted values. The present invention determines the similarity of advertisements by combining the behavioral data of the recognized users of the advertisements and factors such as the average age, fully considering the differences among different advertising audiences, and avoiding the problem of reduced push accuracy caused by ignoring the objective characteristics of the advertisements.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a big data-based advertising push marketing method and system. Background Art

[0002] With the rapid development of internet technology, mobile communications technology, artificial intelligence, and the Internet of Things, the digital transformation of all industries is accelerating. Data has become one of the most important production factors in modern society, especially in the advertising and marketing industries. Big data technology provides accurate user profiling, behavioral analysis, and decision-making support for advertising push. Traditional advertising marketing models typically rely on radio and television broadcasts, resulting in inaccurate effect evaluation. However, big data-based advertising push methods, through real-time data analysis and user behavior prediction, can provide users with customized advertising content, improve the efficiency and accuracy of advertising delivery, and reduce advertising costs. One existing algorithm for advertising push is the collaborative filtering algorithm. Its high recommendation accuracy and computational efficiency make this algorithm promising for application in the advertising push field, significantly improving advertising effectiveness and user experience.

[0003] Patent document CN106095841B discloses a mobile Internet advertising recommendation method based on collaborative filtering, the method comprising: step 1, obtaining user check-in data and Weibo text data; step 2, preprocessing the user check-in data and Weibo text data; step 3, calculating the user interest similarity; step 4, analyzing the user check-in data using a hierarchical clustering method, comparing the similarity of user behavior trajectories from both overall and local aspects based on the spatiotemporal intersection of user behavior trajectories, and obtaining the similarity of user behavior trajectories through linear combination; step 5, comprehensively considering the user interest similarity, the similarity of user behavior trajectories, the similarity of user behavior in different time periods, and the influence of the popularity index of advertisements, using a collaborative filtering recommendation method to calculate the recommendation value of mobile Internet advertisements and recommend the top-ranked advertisements to users.

[0004] However, the above patent document does not address the problem that when using collaborative filtering algorithms to calculate the similarity between advertisements, the algorithm relies only on user interaction data and does not take into account the different audiences of different advertisements. As a result, the algorithm can only calculate similarity based on user behavior, ignoring the objective characteristics of advertisements. Then, if some advertisements show similarity in user behavior but are completely different in audience, the algorithm may still recommend them, resulting in a decrease in the accuracy of advertisement push. Summary of the Invention

[0005] In order to solve the problem that traditional collaborative filtering algorithms rely on user interaction data, fail to take into account the different audiences of different advertisements, ignore the objective characteristics of advertisements, and thus reduce the accuracy of advertisement push, the present invention provides an advertisement push marketing method and system based on big data.

[0006] In a first aspect, the present invention provides an advertising push marketing method based on big data, which adopts the following technical solutions:

[0007] A big data-based advertising push marketing method comprises: obtaining behavioral data of each advertisement when it is recently pushed to each user and the age of each user; for a target advertisement in each advertisement, determining a rating value of the pushed user for the target advertisement based on the behavioral record of any pushed user; determining the recognized users among the pushed users of the target advertisement based on the size of the rating values ​​of the target advertisement by all pushed users; for the target advertisement and any other advertisement, obtaining the similarity of the rating values ​​of the two advertisements by all pushed users as the preliminary similarity of the two advertisements; determining the final similarity of the two advertisements based on the preliminary similarity, the total number of purchases in the purchase records in the behavioral data of the recognized users of the two advertisements, and the average age of the recognized users of the two advertisements; taking any user to whom the target advertisement has not been pushed as a target user, determining a recommendation prediction value of the target advertisement for the target user based on the target user's rating value of the pushed advertisement and the final similarity between the target user's pushed advertisement and the target advertisement; and performing advertising push marketing on the target user based on the size order of the recommendation prediction values.

[0008] The beneficial effects are: by obtaining the user's age and behavior data, it is possible to build a more comprehensive and detailed user portrait, thereby improving the matching degree of advertisements, which can not only reflect the user's interest preferences, but also capture the user's potential needs, and provide more efficient data support for advertisement recommendations; through comprehensive analysis of the score value, similarity analysis, recognized user behavior data and purchase records of pushed advertisements, it can effectively improve the accuracy of advertisement recommendations, no longer relying solely on the user's historical behavior data, but taking into account factors such as the characteristics of the advertisement content itself and the preferences of the audience group, thereby achieving accurate push; traditional advertisement push methods may cause information overload and advertisement fatigue to users, By pushing advertisements that are highly matched with target users, the push of irrelevant advertisements is reduced, and users' negative emotions caused by excessive advertisement push are avoided. Since advertisement push is more accurate, users' interest and acceptance of advertisements will be significantly improved, thereby improving the conversion rate of advertisements; by combining users' historical behavior, ratings and advertisement similarity, users' reactions to advertisements can be predicted more accurately, thereby optimizing advertisement delivery strategies; through real-time feedback and rating data analysis of big data, the system can continuously optimize advertisement push strategies. In actual operation, the results of advertisement push can be adjusted according to the ever-changing user behavior and advertisement effects, ensuring that advertising marketing activities always remain efficient.

[0009] Furthermore, the behavior data also includes display rating values ​​and browsing time.

[0010] Furthermore, the scoring value satisfies:

[0011] Where, For the Pushed to users The rating value of the advertisements. For the Pushed to users The explicit rating value in the behavioral data of the advertisement, For the Pushed to users The viewing time in the behavioral data of ads, For the Pushed to users The contribution value of purchase records in the behavioral data of advertisements, For the preset contribution time, Contribute to the preset viewing time. Contribute to the preset viewing time limit, is the standard normalization function, is the minimum function, is the floor symbol.

[0012] The beneficial effects are: by combining multiple factors such as users' explicit ratings, viewing time and purchase history, the rating value is more comprehensive and can more accurately reflect users' interest in and interaction with advertisements; the use of preset contribution values ​​and upper limits for viewing time effectively avoids the excessive influence of single duration data on the rating, making the rating more reasonable and stable; through the standard normalization function, the differences between different users and advertisements are eliminated, making the ratings highly consistent and comparable, and improving the fairness of the system; the flexibility of the rating mechanism can be adjusted according to the needs of different advertisements and users, thereby achieving more personalized and accurate advertising recommendations, improving advertising conversion rates and user satisfaction.

[0013] Furthermore, the approved users are selected by sorting the target advertisement scores of all users who have pushed the advertisement, and selecting a preset percentage of users as approved users.

[0014] The beneficial effect is that by utilizing the behavioral data and interest preferences of recognized users (i.e., users who have a high score for the target advertisement), it is possible to recommend advertisements that are more in line with their interests and needs to users who are not subsequently pushed the advertisement, thereby improving the relevance of advertisements and user engagement; for users who are not pushed the advertisement, directly pushing the advertisement may have the risk of inaccuracy. By drawing on the preferences of recognized users, it is possible to find a suitable advertisement recommendation path for users who are not pushed the advertisement more quickly, reducing "information overload" and improving the browsing experience.

[0015] Furthermore, the similarity is cosine similarity.

[0016] Furthermore, the final similarity satisfies:

[0017] Where, For the Article and The final similarity of the ads, For the Article and The initial similarity of the ads, and Respectively Article and The total number of purchases recorded in the behavioral data of users who recognized the advertisement, and Respectively Article and The average age of users who recognized the ads, is a hyperparameter, is the standard normalization function.

[0018] The beneficial effects are: by combining the similarity of advertising content, purchase history and user age, the accuracy of advertising similarity calculation is improved, which better meets the interests and needs of users and improves the relevance of advertisements; effectively screens out the advertisements that are most suitable for users, improves click-through rate and conversion rate, and improves the efficiency of advertising delivery through user behavior and feature data.

[0019] Furthermore, the recommended prediction value satisfies:

[0020] Where, For the Ads were not pushed to users The recommended prediction value of a user, For the The number of ads delivered to users. For the Article and The final similarity of the ads, For the Users responded to the first The rating value of the advertisement.

[0021] The beneficial effects are: by combining ad similarity and user ratings, accurate predictions are made for users who have not been pushed the ad, thereby improving the personalization and relevance of the push; by comprehensively considering ad similarity and user feedback, the push is more in line with user preferences, improving ad click-through rate and conversion rate; based on the rating data of user historical behavior, the push is more in line with user interests and needs; the weighting mechanism allows the system to adjust the weights of similarity and ratings according to actual needs, thereby improving the flexibility and adaptability of the push.

[0022] In a second aspect, the present invention provides an advertising push marketing system based on big data, which adopts the following technical solutions:

[0023] A big data-based advertising push marketing system includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned big data-based advertising push marketing method is implemented.

[0024] By adopting the above technical solution, the above-mentioned big data-based advertising push marketing method is generated into a computer program and stored in a memory to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0025] The present invention has the following technical effects:

[0026] The present invention no longer relies solely on user interaction data. Instead, it determines ad similarity by combining factors such as the behavioral data of users who approve ads and the average age of approved users. This fully considers the differences in different ad audiences, namely the objective characteristics of ads, and avoids the problem of reduced push accuracy caused by ignoring these factors. It can more accurately portray the relationship between ads, laying the foundation for subsequent precise push notifications. The final ad similarity determined based on this comprehensive consideration is further combined with the target user's rating of the pushed ads to determine the target ad's recommendation prediction value for the target user. This multi-dimensional data fusion approach comprehensively and meticulously analyzes the relationship between users and ads. Compared with traditional collaborative filtering algorithms, it can more accurately predict the target user's interest in the target ad. Ad push marketing is then carried out based on the order of the recommended prediction value, greatly improving the accuracy of ad push and increasing the likelihood that ads will reach truly interested users. By obtaining behavioral data on each ad when it is pushed to each user in the recent period, comprehensively collecting and utilizing big data information, and deeply exploring the potential needs and preferences behind user behavior, it achieves refined ad push marketing based on big data, effectively improving the effectiveness and efficiency of advertising marketing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] By reading the detailed description below with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding numbers are the same or corresponding parts.

[0028] Figure 1 This is a flow chart of a method for advertising push marketing based on big data in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work shall fall within the scope of protection of the present invention.

[0030] It should be understood that when the terms "first," "second," and the like are used in the claims, description, and drawings of the present invention, they are merely used to distinguish between different objects, rather than to describe a specific order. The terms "comprise" and "comprising" used in the description and claims of the present invention indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof.

[0031] The embodiment of the present invention discloses an advertising push marketing method based on big data, referring to Figure 1 , including steps S1 to S7:

[0032] S1: Obtain the behavioral data of each advertisement when it is pushed to each user in the recent period and the age of each user.

[0033] Big data technology is used to obtain behavioral data on each advertisement pushed to each user in the recent period (for example, within one month), including explicit rating values, viewing time, purchase records (including whether and how many times the advertisement was purchased), and the age of each user.

[0034] S2: For the target advertisement in each advertisement, determine the rating value of the target advertisement given by any user to whom the advertisement has been pushed according to the behavior record of the user to whom the advertisement has been pushed.

[0035] It should be noted that this step uses the explicit ratings, viewing time, and purchase records of different users for different ads to analyze and calculate the ratings of different users for different ads, which facilitates the subsequent calculation of the preliminary similarity between different ads based on the ratings of different users for different ad data. Therefore, different user behaviors need to be assigned different scores, which are subsequently used to calculate a user's rating for a pushed ad. Among them, the normalized value of a user's explicit rating for an ad will be determined as the contribution of a user's explicit rating behavior to the user's rating of the ad; for every increase in the viewing time of a user for an ad, the contribution of a user's viewing time behavior to the user's rating of the ad will increase accordingly until the upper limit; if a user makes a purchase for an ad, the contribution of a user's purchase behavior to the user's rating of the ad will be greater.

[0036] Specifically, the scoring value satisfies:

[0037] ;

[0038] Where, For the Pushed to users The rating value of the advertisements. For the Pushed to users The explicit rating value in the behavioral data of the advertisement, For the Pushed to users The viewing time in the behavioral data of ads, For the Pushed to users The contribution value of purchase records in the behavioral data of advertisements, For the preset contribution time, Contribute to the preset viewing time. Contribute to the preset viewing time limit, is the standard normalization function, is the minimum function, is the floor symbol.

[0039] Among them, the exemplary , , and Indicates the Pushed to users For every 5 seconds that the viewing time of an ad increases, the score contributed by the user to the viewing time of the ad increases by 0.1. Indicates the upper limit of the score that a user contributes to the viewing time of an advertisement. , Indicates taking the Pushed to users The viewing time of an ad is used to calculate the smaller value between the user's actual contribution to the ad's rating and the upper limit. This is done to mitigate the impact of other activities on users, which may cause them to stay on an ad page for too long without actually viewing the ad. Indicates the Pushed to users The contribution value of the purchase record in the behavior data of the advertisement. For example, if a purchase behavior occurs, If no purchase occurs, .

[0040] S3: Determine the users who approve the target advertisement among the users to whom the target advertisement has been pushed, based on the ratings of all users to whom the target advertisement has been pushed.

[0041] It should be noted that in order to more accurately quantify the similarity of advertisements with respect to target audiences, the users corresponding to the top 10% (which can be adjusted based on specific implementation circumstances) of the ratings given by users to each advertisement have been pushed are considered approved users.

[0042] Specifically, the approved users are selected by sorting the ratings of the target advertisements by all users who have pushed the advertisements, and selecting a preset percentage of users as approved users.

[0043] S4: For the target advertisement and any other advertisement, obtain the similarity of the rating values ​​of the two advertisements by all pushed users as the preliminary similarity of the two advertisements.

[0044] Specifically, the preliminary similarity satisfies:

[0045] ;

[0046] Where, For the Article and The initial similarity of the ads, is the number of users who watched the two ads at the same time. and Respectively Pushed to users Article and Since cosine similarity is an existing technology, the logical principle of this formula will not be described in detail.

[0047] S5: Determine a final similarity between the two advertisements based on the preliminary similarity, the total number of purchases in the purchase records in the behavioral data of the users who recognized the two advertisements, and the average age of the users who recognized the two advertisements.

[0048] It should be noted that in order to take into account the different characteristics of the audiences of different advertisements, this step will analyze the age data and purchasing power data of the pushed users corresponding to different advertisements to obtain the similarity of the two advertisements in terms of the audiences, and based on this indicator, correct the preliminary similarity between the two advertisements to obtain the final similarity between the two advertisements; when analyzing the similarity of the two advertisements in terms of the audiences, among the ratings of all users for the two advertisements, the smaller the difference in the total number of purchases made through the advertisements by the recognizing users in the past month, the greater the similarity of the two advertisements in terms of the audiences; however, different advertisements may have similar appeal to users of different age groups, so only analyzing the difference in users' purchasing power cannot well distinguish the audiences of different advertisements. Therefore, the smaller the difference in the average age of the recognizing users, the greater the similarity of the two advertisements in terms of the audiences.

[0049] Specifically, the final similarity satisfies:

[0050] ;

[0051] Where, For the Article and The final similarity of the ads, For the Article and The initial similarity of the ads, and Respectively Article and The total number of purchases recorded in the behavioral data of users who recognized the advertisement, and Respectively Article and The average age of users who recognized the ads, is a hyperparameter, is the standard normalization function.

[0052] Implementers can set hyperparameters according to specific implementation conditions, for example, 0.001. The existence of hyperparameters is to prevent and A value of 0 makes the formula meaningless.

[0053] in, The larger the value, the greater the initial similarity between the two ads based on the analysis of user behavior data, and the greater the final similarity between the two ads. The smaller the difference, the smaller the difference in the total number of times users purchased through ads in the past month. This indicates that the audiences of the two ads are more similar, and the corresponding final similarity of the two ads will be greater. The smaller it is, the smaller the difference in the average age of all recognized users is. This means that the greater the similarity between the audiences of the two advertisements, the greater the credibility, and correspondingly, the greater the final similarity between the two advertisements.

[0054] S6: Take any user to whom the target advertisement has not been pushed as the target user, and determine the recommendation prediction value of the target advertisement for the target user based on the target user's rating of the pushed advertisement and the final similarity between the target user's pushed advertisement and the target advertisement.

[0055] It should be noted that the final similarity between all advertisements is obtained through analysis and calculation in the above steps. In this step, the final similarity between an advertisement and the remaining advertisements pushed to users who have not pushed it and the advertisement itself (the final similarity is calculated based on the remaining users to whom the advertisement has been pushed), and then the weighted summation of the ratings of the remaining advertisements pushed to users who have not pushed it is combined to obtain the recommendation prediction value of the advertisement for the user to be pushed.

[0056] Specifically, the recommended prediction value satisfies:

[0057] ;

[0058] Where, For the Ads were not pushed to users The recommended prediction value of a user, For the The number of ads delivered to users. For the Article and The final similarity of the ads, For the Users responded to the first The rating value of the advertisements. For the of the ads sent to users Advertisements.

[0059] S7: Push advertising marketing to target users according to the order of the recommended prediction values.

[0060] Obtain the recommended predicted values ​​of all advertisements for target users, sort them according to the size of the recommended predicted values, and then push them to the target users in order to complete the advertising push marketing based on big data.

[0061] An embodiment of the present invention also discloses an advertising push marketing system based on big data, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, an advertising push marketing method based on big data according to the present invention is implemented.

[0062] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0063] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application, module, or both. Any such computer storage medium can be part of, accessible to, or connectable to the device.

[0064] While several embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous modifications, variations, and alternatives will occur to those skilled in the art without departing from the concept and spirit of the present invention. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed in practicing the present invention.

[0065] The above are all preferred embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An advertising push marketing method based on big data, characterized in that: include: Obtain the behavioral data of each advertisement pushed to each user in the recent period and the age of each user; Behavioral data includes explicit ratings, viewing time, and purchase records, where purchase records include purchase behavior and purchase frequency. For the target ad in each ad, determine the rating of the target ad by the user who has received the ad based on the behavior record of the user who has received the ad, and satisfy the following conditions: ; For the Pushed to users The rating value of the advertisements. For the Pushed to users The explicit rating value in the behavioral data of the advertisement, For the Pushed to users The viewing time in the behavioral data of ads, For the Pushed to users The contribution value of purchase records in the behavioral data of advertisements, For the preset contribution time, Contribute to the preset viewing time, Contribute to the preset viewing time limit, is the standard normalization function, is the minimum function, is the floor rounding symbol; To obtain the Pushed to users The viewing time of the ad is Pushed to users The smaller value between the actual score contributed by the advertisement and the upper limit of the score; Determine the users who approve the target advertisement among the users who have been sent the target advertisement according to the ratings of all users who have been sent the target advertisement; For the target ad and any other ad, obtain the similarity of the ratings of the two ads by all users who have been pushed to the ad, and use this as the preliminary similarity of the two ads; The final similarity between the two advertisements is determined based on the preliminary similarity, the total number of purchases in the purchase records in the behavioral data of the users who recognized the two advertisements, and the average age of the users who recognized the two advertisements, so as to satisfy: ; For the Article and The final similarity of the ads, For the Article and The initial similarity of the ads, and Respectively Article and The total number of purchases recorded in the behavioral data of users who recognized the advertisement, and Respectively Article and The average age of users who recognized the ads, is a hyperparameter; Taking any user to whom the target ad has not been pushed as the target user, and determining the target user's recommendation prediction value based on the target user's rating of the pushed ad and the final similarity between the target user's pushed ad and the target ad; Advertisement push marketing is performed to target users according to the order of the recommended prediction values.

2. The advertising push marketing method based on big data according to claim 1, characterized in that: The approved users are selected by sorting the ratings of the target advertisements by all users who have pushed the advertisements, and a preset percentage of users are selected as approved users.

3. The advertising push marketing method based on big data according to claim 1, characterized in that: The similarity is cosine similarity.

4. The big data-based advertising push marketing method according to claim 1, characterized in that: The recommended prediction value satisfies: ； Where, For the Ads were not pushed to users The recommended prediction value of a user, For the The number of ads delivered to users. For the Article and The final similarity of the ads, For the Users responded to the first The rating value of the advertisement.

5. An advertising push marketing system based on big data, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an advertising push marketing method based on big data according to any one of claims 1 to 4 is implemented.

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