Advertisement pushing system and method based on user behavior analysis

Abstract

The invention discloses an advertisement pushing system and method based on user behavior analysis, and relates to the technical field of advertisement pushing, the system discloses a user behavior recording module, a behavior input analysis module and an advertisement pushing analysis module, the user behavior recording module and the behavior input analysis module are set, various types of behaviors of a user are recorded, and the advertisement pushing analysis module is used for pushing the advertisement. The method comprises the following steps: setting a user behavior analysis module, regularly carrying out deep and accurate analysis on all behaviors of a user, capturing the user behaviors in a multi-dimensional and multi-factor manner, setting an advertisement push analysis module, accurately analyzing interests and preferences of the user for different types of commodities through deep capturing of the user behaviors, and carrying out advertisement pushing. The pushing sequence of different types of commodity advertisements is dynamically adjusted according to the analysis result, the user experience is ensured, and the advertisement click rate and conversion rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of advertisement push, and more specifically, to an advertisement push system and method based on user behavior analysis. Background Art

[0002] With the rapid development of Internet technology and the advent of the information age, advertising has become an important marketing tool for various online service platforms. Traditional advertising methods often use indiscriminate advertising to target a wide range of user groups. This "one-size-fits-all" strategy is not only inefficient, but also easily triggers user disgust and resistance, seriously affecting user experience.

[0003] In recent years, with the rise of big data and artificial intelligence technology, personalized advertising push has gradually become possible. The core of personalized advertising push lies in the accurate capture and analysis of user behavior, so as to customize push ads according to user interests, preferences and behavior patterns. However, the existing personalized advertising push system still has many shortcomings.

[0004] On the one hand, although some systems can record some of the user's behavior data, they often stay on the surface in behavior analysis and lack depth and accuracy. These systems can often only capture some of the user's explicit behaviors, such as clicks, purchases, etc., but lack effective means to capture and analyze the user's implicit behaviors, such as browsing time, page location, and other specific behaviors.

[0005] On the other hand, existing advertising push systems often only consider single-dimensional factors when analyzing user behavior, such as the user's historical purchase records or search keywords, while ignoring the correlation and complexity between user behaviors. This single-dimensional analysis method can easily lead to one-sided and inaccurate push results, thereby reducing the click-through rate and conversion rate of advertisements.

[0006] Therefore, the present invention proposes an advertisement push system and method based on user behavior analysis. Summary of the invention

[0007] In view of the deficiencies in the prior art, the object of the present invention is to provide an advertisement push system and method based on user behavior analysis.

[0008] To achieve the above object, the present invention provides the following technical solutions: An advertisement push system based on user behavior analysis, comprising a user behavior recording module, a behavior input parsing module, and an advertisement push analysis module; The user behavior record module generates a user behavior record each time the user completes a behavior; The behavior investment analysis module collects all user behavior records of the user in the user behavior analysis cycle every time it goes through a user behavior analysis cycle, obtains the commodity type of the user behavior record, and then obtains the commodity behavior investment value of each commodity type, and simultaneously obtains the stage behavior investment value of each commodity type; The advertising push analysis module obtains the advertising push benchmark value of each commodity type based on the commodity behavior input value and the stage behavior input value of each commodity type, determines whether to mark the commodity type as a promoted commodity type based on the comparison result between the advertising push benchmark value and the advertising push benchmark threshold, sorts all promoted commodity types in descending order according to the numerical value of the advertising push benchmark value, and pushes advertisements of the corresponding commodity types to users in the sorted order.

[0009] Furthermore, the user behavior record includes user behavior type, product type, user behavior data, behavior feature value, and record generation time.

[0010] Furthermore, the behavior feature value of the user behavior record is obtained through the following steps: obtaining the user behavior type and user behavior data of the user behavior record, obtaining a behavior analysis model of the user behavior type, performing data feature extraction on the user behavior data to obtain user behavior features, inputting the user behavior features into the behavior analysis model, and outputting the behavior feature value from the behavior analysis model.

[0011] Furthermore, the commodity behavior investment value of the commodity type is obtained through the following steps: marking all user behavior records of the same commodity type as first-class behavior records, obtaining the behavior characteristic value of the first-class behavior record, setting the behavior characteristic threshold, and when the behavior characteristic value of the first-class behavior record is greater than or equal to the behavior characteristic threshold, marking the first-class behavior record as an investment behavior record, sorting all investment behavior records in the order of record generation time, calculating the time difference between the record generation time of two adjacent investment behavior records after sorting, and obtaining the behavior investment interval, summing up all behavior investment intervals and taking the average value to obtain the behavior investment average interval Lzv, and After sorting, the user behavior types of two adjacent input behavior records are compared. When the two user behavior types are the same type, the number of input behavior retention times is increased once. When the two user behavior types are not the same type, the number of input behavior changes is increased once. The number of input behavior retention times is marked as Zd, and the number of input behavior changes is marked as Bt. The user behavior type of a product behavior record is obtained, and the product behavior records of the same user behavior type are marked as a product row record, and then a row of input values ​​of each user behavior type is obtained. The input values ​​of all user behavior types in a row are summed and averaged to obtain the behavior comprehensive input mean Bmd. The formula is used The commodity behavior input value Sga of the commodity type is obtained, where z1 is the behavior input averaging coefficient, z2 is the investment behavior maintenance coefficient, z3 is the investment behavior change coefficient, and z4 is the behavior comprehensive investment coefficient.

[0012] Furthermore, the input value of a row of user behavior types is obtained by the following steps: sorting all the records in a row in the order of the time when the records were generated, summing the behavior feature values ​​of two adjacent records in a row after sorting to obtain the behavior input continuous value, summing all the behavior input continuous values ​​and taking the average to obtain the behavior input continuous mean Lst, performing difference calculation on the behavior feature values ​​of two adjacent records in a row after sorting and taking the absolute value to obtain the behavior input turbulence value, summing all the behavior input turbulence values ​​and taking the average to obtain the behavior input turbulence mean Rah, and using the formula A row of input values ​​Vtgk for the user behavior type is obtained, where y1 is the behavior input continuity coefficient and y2 is the behavior input turbulence coefficient.

[0013] Furthermore, the stage behavior input value of the commodity type is obtained by the following steps: obtain all commodity behavior input values ​​of the same commodity type obtained in the previous i user behavior analysis cycles, sort all commodity behavior input values ​​in the order of the user behavior analysis cycles, calculate the difference between the two adjacent commodity behavior input values ​​after sorting and take the absolute value to obtain the behavior input fluctuation value, set the behavior input fluctuation threshold, when the behavior input fluctuation value is greater than or equal to the behavior input fluctuation threshold, increase the number of behavior input fluctuations by one, mark the number of behavior input fluctuations as Hzx, sum all commodity behavior input values ​​and take the average, obtain the commodity behavior input mean Kp, and use the formula The stage behavior input value Lmg of the commodity type is obtained, where s1 is the commodity behavior input coefficient and s2 is the behavior input fluctuation coefficient.

[0014] Furthermore, the advertising push benchmark value of a product type is obtained by the following steps: obtaining the product behavior input value Sga and the stage behavior input value Lmg of the same product type, and using the formula Get the advertising push benchmark value Dxs for this product type.

[0015] Furthermore, an advertisement push method based on user behavior analysis includes the following steps: Step 1: After each user behavior analysis cycle, collect all user behavior records within the user behavior analysis cycle; Step 2: Obtain the product type of the user behavior record, and then obtain the product behavior input value of each product type, and simultaneously obtain the stage behavior input value of each product type; Step 3: Based on the product behavior input value and stage behavior input value of each product type, obtain the advertising push benchmark value of each product type; Step 4: Based on the comparison result between the advertising push benchmark value and the advertising push benchmark threshold, determine whether to mark the product type as a promotion product type; Step 5: Sort all promoted product types in descending order according to the advertising push benchmark value, and push the corresponding product type advertisements to users in the sorted order.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The method of the present invention captures user behaviors in multiple dimensions and factors, and dynamically adjusts the push order of different types of product advertisements according to the analysis results, thereby ensuring user experience and improving advertisement click-through rate and conversion rate; 2. Set up a user behavior recording module and a behavior input analysis module to record various types of user behaviors and regularly conduct in-depth and accurate analysis of all user behaviors. Set up an advertising push analysis module to accurately analyze users' interests and preferences for different types of products by deeply capturing user behaviors. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A method flow chart of an advertisement push method based on user behavior analysis; Figure 2 This is a system module diagram of an advertising push system based on user behavior analysis. DETAILED DESCRIPTION

[0018] Example 1: Reference Figure 1 , an advertisement push method based on user behavior analysis, comprising the following steps: Step 1: After each user behavior analysis cycle, collect all user behavior records within the user behavior analysis cycle.

[0019] Step 2: Obtain the product type of the user behavior record, and then obtain the product behavior input value of each product type, and simultaneously obtain the stage behavior input value of each product type.

[0020] Step 3: Based on the product behavior input value and stage behavior input value of each product type, obtain the advertising push benchmark value of each product type.

[0021] Step 4: Based on the comparison result between the advertisement push benchmark value and the advertisement push benchmark threshold value, determine whether to mark the product type as a promotion product type.

[0022] Step 5: Sort all promoted product types in descending order according to the advertising push benchmark value, and push the corresponding product type advertisements to users in the sorted order.

[0023] Example 2: Reference Figure 2 , an advertisement push system based on user behavior analysis, including a user behavior recording module, a behavior input parsing module, and an advertisement push analysis module.

[0024] User behavior record module: Every time a user completes a behavior, a user behavior record is generated (taking browsing behavior as an example, every time a user completes a browsing behavior, a user behavior record is generated).

[0025] User behavior records include user behavior type (user behavior types include but are not limited to browsing behavior type, search behavior type, interactive behavior type, and purchase behavior type), product type, user behavior data (data generated when the user performs a behavior), behavior feature value, and record generation time (when the user completes the behavior, the record generation time is the time when the record is generated).

[0026] The behavior feature value of the user behavior record is obtained through the following steps: obtaining the user behavior type and user behavior data of the user behavior record, obtaining a behavior analysis model of the user behavior type, performing data feature extraction on the user behavior data to obtain user behavior features, inputting the user behavior features into the behavior analysis model, and outputting the behavior feature value from the behavior analysis model.

[0027] Each type of user behavior type corresponds to a behavior analysis model. The difference between different behavior analysis models lies in the replacement of training data. In this embodiment, a method for constructing a behavior analysis model of a public browsing behavior type is as follows: multiple browsing behavior features are collected to construct a neural network model. The browsing behavior features are used as training data for the neural network model. A behavior feature value is assigned to each training data. The value range of the behavior feature value is (1.0~4.0). The larger the value of the behavior feature value, the higher the degree of behavioral investment in the browsing behavior. The smaller the value of the behavior feature value, the lower the degree of behavioral investment in the browsing behavior. The training data is divided into a training set, a validation set, and a test set according to a set ratio of 5:1:1. The neural network is iteratively trained on the training set, the validation set, and the test set. After the training is completed, a behavior analysis model of the browsing behavior type is constructed.

[0028] If the purpose is to construct a behavior analysis model of the search behavior type, multiple search behavior features are collected. The larger the value of the behavior feature value, the higher the degree of behavioral investment in the search behavior, and the smaller the value of the behavior feature value, the lower the degree of behavioral investment in the search behavior.

[0029] Behavior investment analysis module: set the user behavior analysis cycle (the user behavior analysis cycle is infinitely looped during system operation). After each user behavior analysis cycle, collect all user behavior records within the user behavior analysis cycle, obtain the product type of the user behavior record, and then obtain the product behavior investment value of each product type, and simultaneously obtain the stage behavior investment value of each product type.

[0030] The commodity behavior investment value of a commodity type is obtained through the following steps: marking all user behavior records of the same commodity type as first-class behavior records, obtaining the behavior feature value of the first-class behavior record, setting the behavior feature threshold (the behavior feature threshold is the preset threshold of the system), and when the behavior feature value of the first-class behavior record is greater than or equal to the behavior feature threshold, marking the first-class behavior record as an investment behavior record, sorting all investment behavior records in the order of record generation time, calculating the time difference between the record generation time of two adjacent investment behavior records after sorting, and obtaining the behavior investment interval, summing up all behavior investment intervals and taking the average to obtain the behavior investment average interval Lzv, compare the user behavior types of two adjacent input behavior records after sorting. When the two user behavior types are the same, increase the number of input behavior retentions by one. When the two user behavior types are not the same, increase the number of input behavior changes by one. Mark the number of input behavior retentions as Zd, and the number of input behavior changes as Bt. Obtain the user behavior type of a product behavior record, mark a product behavior record of the same user behavior type as a product row record, and then obtain a row of input values ​​for each user behavior type. Sum and average the input values ​​of all user behavior types in one row to obtain the behavior comprehensive input mean Bmd. Use the formula The commodity behavior input value Sga of this commodity type is obtained, where z1 is the behavior input averaging coefficient, z2 is the investment behavior retention coefficient, z3 is the investment behavior change coefficient, and z4 is the comprehensive behavior input coefficient. The value of z1 is 1.19, the value of z2 is 0.87, the value of z3 is 0.93, and the value of z4 is 0.88.

[0031] The input value of a row of user behavior types is obtained by the following steps: sort all the records in the order of the time of record generation, sum the behavior feature values ​​of two adjacent records in the order, and obtain the behavior input continuous value, sum all the behavior input continuous values ​​and take the average, and obtain the behavior input continuous mean Lst, calculate the difference between the behavior feature values ​​of two adjacent records in the order, and take the absolute value, and obtain the behavior input turbulence value, sum all the behavior input turbulence values ​​and take the average, and obtain the behavior input turbulence mean Rah, and use the formula A row of input values ​​Vtgk for the user behavior type is obtained, where y1 is the behavior input continuity coefficient, y2 is the behavior input turbulence coefficient, the value of y1 is 0.81, and the value of y2 is 2.62.

[0032] The stage behavior input value of the commodity type is obtained through the following steps: obtain all commodity behavior input values ​​of the same commodity type obtained in the previous i user behavior analysis cycles, sort all commodity behavior input values ​​in the order of the user behavior analysis cycles, calculate the difference between the two adjacent commodity behavior input values ​​after sorting and take the absolute value to obtain the behavior input fluctuation value, set the behavior input fluctuation threshold (the behavior input fluctuation threshold is the preset threshold of the system), when the behavior input fluctuation value is greater than or equal to the behavior input fluctuation threshold, increase the number of behavior input fluctuations by one, mark the number of behavior input fluctuations as Hzx, sum all commodity behavior input values ​​and take the average, obtain the commodity behavior input mean Kp, and use the formula The stage behavior input value Lmg of the commodity type is obtained, where s1 is the commodity behavior input coefficient, s2 is the behavior input fluctuation coefficient, the value of s1 is 1.59, and the value of s2 is 1.48.

[0033] Set up a user behavior recording module and a behavior input analysis module to record various types of user behaviors, and regularly conduct in-depth and accurate analysis of all user behaviors to capture user behaviors in multiple dimensions and factors.

[0034] Advertisement push analysis module: based on the commodity behavior input value and stage behavior input value of each commodity type, obtain the advertisement push benchmark value of each commodity type, set the advertisement push benchmark threshold (the advertisement push benchmark threshold is the preset threshold of the system), when the advertisement push benchmark value is greater than or equal to the advertisement push benchmark threshold, mark the commodity type as a promoted commodity type, when the advertisement push benchmark value is less than the advertisement push benchmark threshold, do not process it, sort all promoted commodity types in descending order according to the value of the advertisement push benchmark value, and push the advertisements of the corresponding commodity types to users in the sorted order.

[0035] The advertising push benchmark value of a product type is obtained by the following steps: Get the product behavior input value Sga and the stage behavior input value Lmg of the same product type, and use the formula Get the advertising push benchmark value Dxs for this product type.

[0036] Set up an advertising push analysis module to accurately analyze users' interests and preferences for different types of products by deeply capturing user behaviors. Dynamically adjust the push order of advertisements for different types of products based on the analysis results to ensure user experience and improve advertising click-through rate and conversion rate.

[0037] The above formulas are all dimensionless and numerical calculations are performed, and the preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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. An advertisement push system based on user behavior analysis, characterized in that: Including user behavior recording module, behavior input analysis module, and advertising push analysis module; The user behavior record module generates a user behavior record each time the user completes a behavior; The behavior investment analysis module collects all user behavior records of the user in the user behavior analysis cycle every time it goes through a user behavior analysis cycle, obtains the commodity type of the user behavior record, and then obtains the commodity behavior investment value of each commodity type, and simultaneously obtains the stage behavior investment value of each commodity type; The advertising push analysis module obtains the advertising push benchmark value of each commodity type based on the commodity behavior input value and the stage behavior input value of each commodity type, determines whether to mark the commodity type as a promoted commodity type based on the comparison result between the advertising push benchmark value and the advertising push benchmark threshold, sorts all promoted commodity types in descending order according to the numerical value of the advertising push benchmark value, and pushes advertisements of the corresponding commodity types to users in the sorted order.

2. The advertisement push system based on user behavior analysis according to claim 1, characterized in that: User behavior records include user behavior type, product type, user behavior data, behavior feature value, and record generation time.

3. The advertisement push system based on user behavior analysis according to claim 2, characterized in that: The behavior feature value of the user behavior record is obtained through the following steps: obtaining the user behavior type and user behavior data of the user behavior record, obtaining a behavior analysis model of the user behavior type, performing data feature extraction on the user behavior data to obtain user behavior features, inputting the user behavior features into the behavior analysis model, and outputting the behavior feature value from the behavior analysis model.

4. The advertisement push system based on user behavior analysis according to claim 1, characterized in that: The commodity behavior investment value of a commodity type is obtained by the following steps: marking all user behavior records of the same commodity type as first-class behavior records, obtaining the behavior feature value of the first-class behavior record, setting the behavior feature threshold, and when the behavior feature value of the first-class behavior record is greater than or equal to the behavior feature threshold, marking the first-class behavior record as an investment behavior record, sorting all investment behavior records in the order of record generation time, calculating the time difference between the record generation time of two adjacent investment behavior records after sorting, and obtaining the behavior investment interval, summing up all behavior investment intervals and taking the average to obtain the behavior investment average interval Lzv, and The user behavior types of two adjacent input behavior records are compared. When the two user behavior types are the same type, the number of input behavior retentions is increased once. When the two user behavior types are not the same type, the number of input behavior changes is increased once. The number of input behavior retentions is marked as Zd, and the number of input behavior changes is marked as Bt. The user behavior type of a product behavior record is obtained, and the product behavior records of the same user behavior type are marked as a product row record, and then a row of input values ​​of each user behavior type is obtained. The input values ​​of all user behavior types in a row are summed and averaged to obtain the behavior comprehensive input mean Bmd. The formula is used The commodity behavior input value Sga of the commodity type is obtained, where z1 is the behavior input averaging coefficient, z2 is the investment behavior maintenance coefficient, z3 is the investment behavior change coefficient, and z4 is the behavior comprehensive investment coefficient.

5. The advertisement push system based on user behavior analysis according to claim 4, characterized in that: The input value of a row of user behavior types is obtained by the following steps: sort all the records in the order of the time of record generation, sum the behavior feature values ​​of two adjacent records in the order, and obtain the behavior input continuous value, sum all the behavior input continuous values ​​and take the average, and obtain the behavior input continuous mean Lst, calculate the difference between the behavior feature values ​​of two adjacent records in the order, and take the absolute value, and obtain the behavior input turbulence value, sum all the behavior input turbulence values ​​and take the average, and obtain the behavior input turbulence mean Rah, and use the formula A row of investment values ​​Vtgk for the user behavior type is obtained, where y1 is the behavior investment continuity coefficient and y2 is the behavior investment turbulence coefficient.

6. The advertisement push system based on user behavior analysis according to claim 1, characterized in that: The stage behavior input value of the commodity type is obtained through the following steps: obtain all commodity behavior input values ​​of the same commodity type obtained in the previous i user behavior analysis cycles, sort all commodity behavior input values ​​in the order of the user behavior analysis cycles, calculate the difference between the two adjacent commodity behavior input values ​​after sorting and take the absolute value to obtain the behavior input fluctuation value, set the behavior input fluctuation threshold, when the behavior input fluctuation value is greater than or equal to the behavior input fluctuation threshold, increase the number of behavior input fluctuations by one, mark the number of behavior input fluctuations as Hzx, sum all commodity behavior input values ​​and take the average, obtain the commodity behavior input mean Kp, and use the formula The stage behavior input value Lmg of the commodity type is obtained, where s1 is the commodity behavior input coefficient and s2 is the behavior input fluctuation coefficient.

7. The advertisement push system based on user behavior analysis according to claim 1, characterized in that: The advertising push benchmark value of a product type is obtained by the following steps: Get the product behavior input value Sga and the stage behavior input value Lmg of the same product type, and use the formula Get the advertising push benchmark value Dxs for this product type.

8. An advertisement push method based on user behavior analysis, applied to an advertisement push system based on user behavior analysis as claimed in any one of claims 1 to 7, characterized in that: The steps include: Step 1: After each user behavior analysis cycle, collect all user behavior records within the user behavior analysis cycle; Step 2: Obtain the product type of the user behavior record, and then obtain the product behavior input value of each product type, and simultaneously obtain the stage behavior input value of each product type; Step 3: Based on the product behavior input value and stage behavior input value of each product type, obtain the advertising push benchmark value of each product type; Step 4: Based on the comparison result between the advertising push benchmark value and the advertising push benchmark threshold, determine whether to mark the product type as a promotion product type; Step 5: Sort all promoted product types in descending order according to the advertising push benchmark value, and push the corresponding product type advertisements to users in the sorted order.

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