Advertisement content distribution method and device, equipment and medium

Abstract

The invention discloses an advertisement content distribution method and device, equipment and a medium, and the method comprises the steps: obtaining daily active user data in each cooperative application, calculating the push weight of each cooperative application according to the daily active user data, and determining a target cooperative application for pushing advertisement content according to the high-low order of the push weight of each cooperative application. And when the to-be-pushed advertisement content conforming to the interest of the user is screened out, step-by-step pushing of the to-be-pushed advertisement content is completed based on the content pushing strategy data. Visibly, according to the application, the sending sequence of the pushed advertisement content in each cooperative application is determined according to the high-low order of the push weight of each cooperative application obtained through calculation, and then when the to-be-pushed advertisement content conforming to the interest of the user is screened out, step-by-step pushing of the to-be-pushed advertisement content is completed based on the content push strategy data; therefore, accurate advertisement content distribution can be carried out in different applications according to user interests, and the advertisement click rate and the user activeness are improved.

Description

Technical Field

[0001] The present invention relates to the field of advertising dissemination technology, and in particular to an advertising content distribution method, device, equipment and medium. Background Art

[0002] With the continuous development of information technology, the speed and efficiency of information transmission are getting faster and faster. More and more application developers have begun to pay attention to the daily active user data of their APPs and use it as a key condition for cooperation negotiations with various small-screen and large-screen terminals.

[0003] At present, existing technologies usually implement advertising content distribution by uniformly pushing startup ads when users turn on their devices, uniformly pushing application opening ads when opening applications, or configuring promotional posters for new dramas on the home screen. However, the above technologies are only based on specific user behavior operations, and the pushed content is relatively single, and it is impossible to accurately distribute advertising content based on user interests in different applications, resulting in low advertising click-through rates and user activity. Summary of the invention

[0004] Based on this, it is necessary to address the above technical problems. The embodiments of the present invention provide an advertising content distribution method, device, equipment and medium to solve the problem that the prior art cannot accurately distribute advertising content according to user interests in different applications, thereby resulting in low advertising click-through rate and user activity.

[0005] A first aspect of an embodiment of the present application provides an advertisement content distribution method, the advertisement content distribution method comprising: Acquire daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period; Calculate the push weight of each of the cooperative applications according to the daily active user data; Determine the target cooperative application for pushing the advertisement content according to the push weight ranking of each cooperative application, and obtain content push strategy data; When the advertisement content to be pushed that meets the user's interest is screened out, the advertisement content to be pushed is pushed step by step based on the content push strategy data.

[0006] A second aspect of an embodiment of the present application provides an advertising content distribution device, the advertising content distribution device comprising: An acquisition module, used to acquire daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period; A calculation module, used to calculate the push weight of each of the cooperative applications according to the daily active user data; An obtaining module is used to determine the target cooperative application for pushing the advertisement content according to the order of the push weights of the cooperative applications, and obtain content push strategy data; The push module is used to complete the step-by-step push of the advertisement content to be pushed based on the content push strategy data when the advertisement content to be pushed that meets the user's interest is screened out.

[0007] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the advertising content distribution method as described in the first aspect is implemented.

[0008] According to a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the advertising content distribution method according to the first aspect is implemented.

[0009] In summary, the present invention provides a method, device, equipment and medium for distributing advertising content, which obtains daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period, and the push weight of each cooperative application is calculated based on the daily active user data, and the target cooperative application for pushing the advertising content is determined according to the high and low ranking of the push weights of each cooperative application, and the content push strategy data is obtained. When the advertising content to be pushed that meets the user's interests is screened out, the step-by-step push of the advertising content to be pushed is completed based on the content push strategy data. It can be seen that the present application determines the sending order of the advertising content to be pushed in each cooperative application by ranking the push weights of each cooperative application according to the calculation, and then when the advertising content to be pushed that meets the user's interests is screened out, the step-by-step push of the advertising content to be pushed is completed based on the content push strategy data, so that accurate advertising content distribution can be performed in different applications according to user interests, thereby improving the click-through rate of advertisements and user activity. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1 This is an application environment diagram of an advertisement content distribution method provided by an embodiment of the present invention; Figure 2 It is a flowchart of an advertisement content distribution method provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of an advertisement content distribution device provided by an embodiment of the present invention; Figure 4 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] 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 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 technical personnel in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “uponce” or “in response to determining”, depending on the context. Similarly, the phrases “if it is determined” or “if matched to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “uponce matched to [described condition or event]” or “in response to matching to [described condition or event]”, depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence 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 invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] See also Figure 1 , is an application environment diagram of an advertising content distribution method provided by an embodiment of the present invention. An advertising content distribution method provided by an embodiment of the present invention can be applied in Figure 1 In the application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs) and other computer devices. The client here is replaced by a business system. The server can be implemented with an independent server or a server cluster composed of multiple servers. Advertising content is distributed through the server to improve advertising click-through rate and user activity.

[0021] See also Figure 2 , is a flow chart of an advertising content distribution method provided by an embodiment of the present invention, such as Figure 2 As shown, the advertisement content distribution method can be implemented through the following steps.

[0022] S201: Acquire daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of users using the cooperative application within a preset time period.

[0023] In step S201, the cooperative application can be any cooperative application installed in the client, such as Tencent APP, iQiyi APP and Youku APP, etc., without specific limitation. The preset time period can be freely set according to the actual situation, such as one day, one week or one month, etc. The daily active user data refers to the recorded data generated when the user operates the cooperative application, that is, the active intensity of the user using the cooperative application in one day, including the user's behavior habits, usage time period, frequency and other information. If a user operates the cooperative application once through the user device, it will be recorded as active user data. By counting all the operations within 1 day, the daily active user data in the cooperative application can be obtained. For example, if a user successfully opens the Tencent APP through a smart phone, entering the APP homepage module operation is recorded as one time, refreshing the page once is recorded as one time, and when loading the next new page, loading once is recorded as one time, thereby real-time statistics of the daily active user data entering the Tencent APP within 1 day.

[0024] Furthermore, within a preset time period, after obtaining the active user data of all cooperative applications in the user's device on that day through the data platform, the active user data corresponding to each cooperative application is divided and classified according to the type of each cooperative application to determine the daily active user data of each cooperative application, thereby ensuring that the daily active user data of each cooperative application can be accurately analyzed later to formulate a more accurate advertising content distribution strategy.

[0025] It should be noted that, although the preset time period in the embodiments of the present disclosure is taken as one day, it should not be regarded as a limitation to the present disclosure.

[0026] In an embodiment of the invention, before obtaining the daily active user data of each cooperative application, the following steps are included: receiving a data cooperation request formed by a client in response to startup instructions of multiple preset applications, wherein the data cooperation request carries identification information of the multiple preset applications; According to the identification information and the preset cooperative application standard, determining in sequence whether the plurality of preset applications are cooperative applications; If the plurality of preset applications are cooperative applications, the step of obtaining daily active user data in each cooperative application is performed.

[0027] Specifically, the startup instruction can be an instruction generated by the user starting multiple preset applications in the application list of the client (for example, the user clicks on the icon of a certain application in the application list of the client). For example, the applications in the application list may include, but are not limited to: third-party applications released within the enterprise to which the client belongs or third-party applications released in other application stores and downloaded by the user himself. When the client responds to the startup instructions of multiple preset applications, it sends a cooperation data request to the server, which carries the identification information of the multiple preset applications. Among them, the identification information can be ID, name, version number, developer information, etc., by traversing multiple preset applications, and then judging whether multiple preset applications are cooperative applications in turn according to the identification information and the preset cooperative application standards, if multiple preset applications are cooperative applications, then the daily active user data in each cooperative application is obtained in turn, so as to accurately process the daily active user data in each cooperative application later, wherein the preset cooperative application standards are a series of criteria and conditions defined in advance based on specific business needs, technical architecture, compliance requirements, and partnerships, etc., for screening and evaluating potential cooperative applications, and this application does not make any restrictions on this. Through the above steps, cooperative applications can be quickly screened out according to identification information and preset cooperative application standards, avoiding unnecessary screening and evaluation processes, thereby significantly improving the efficiency of judgment.

[0028] S202: Calculate the push weight of each of the cooperative applications according to the daily active user data.

[0029] In step S202, the push weight is used to determine the priority of each cooperative application as an advertising content push channel, that is, the greater the push weight, the higher the priority of the corresponding cooperative application; for example, the push weight of cooperative application A is greater than that of cooperative application B, so it can be determined that the priority of cooperative application A is greater than that of cooperative application B. This application calculates the push weight of each cooperative application based on the daily active user data, so that push resources can be reasonably allocated according to the push weight of each cooperative application in the future, thereby improving the overall push effect, user satisfaction and retention rate.

[0030] In an embodiment of the invention, calculating and obtaining the push weight of each cooperative application includes: According to the daily active user data, the time-sharing proportion of each of the cooperative applications in each time period is calculated; Compare the time-sharing proportion of each cooperative application in each time period with the actual completion status to obtain a comparison result, wherein the actual completion status is used to indicate the completion status of the actual push of the advertising content by each cooperative application in each time period; According to the comparison result, a preset algorithm is used to assign a push weight to each of the cooperative applications.

[0031] In an embodiment of the invention, the time-sharing proportion of each cooperative application in each time period is calculated, including: Obtaining the target total daily active volume of each of the cooperative applications within a preset time period; According to the daily active user data, the target total daily active users are split into target sub-daily active users corresponding to each time period using a target splitting algorithm, and the active weights of the target sub-daily active users in each time period are determined; Based on the active weight of the target sub-DAU in each time period and the user's historical behavior data, the time-sharing proportion of each cooperative application in each time period is calculated, wherein the user's historical behavior data includes the user's actual completed DAU in each time period; The time-sharing proportion of each cooperative application in each time period is calculated by the following formula: in, It is expressed as the time-sharing proportion in the i-th hour, Expressed as the target total daily activity, Expressed as the active weight within the i-th hour, Expressed as the sum of active weights, Expressed as a smoothing factor between [0, 1], Expressed as the trend factor within the i-th hour, It is expressed as the actual daily activity completed in the kth hour. Expressed as the target sub-daily activity within the kth hour.

[0032] Specifically, by obtaining the target total daily active volume of each cooperative application in a preset time period, and then according to the daily active user data, the target total daily active volume is split into target sub-daily active volumes corresponding to each time period using the target splitting algorithm, and the active weight of the target sub-daily active volume in each time period is determined. The higher the weight, the greater the contribution of the active users in the time period to the overall daily active volume, and the weight can be adjusted accordingly according to business needs or market changes. The target splitting algorithm is a technology for breaking down complex problems or goals into smaller and more manageable sub-goals. Then, based on the active weight of the target sub-daily active volume in each time period and the user's historical behavior data, the time-sharing proportion of each cooperative application in each time period is calculated. Among them, the user's historical behavior data includes the actual daily active volume completed by the user in each time period, and the time-sharing proportion of each cooperative application in each time period is calculated by the following formula: in, It is expressed as the time-sharing proportion in the i-th hour, Expressed as the target total daily activity, Expressed as the active weight within the i-th hour, Expressed as the sum of active weights, It is expressed as a smoothing factor between [0, 1], which is used to control the speed of adjusting the target sub-daily activity. The smaller the value, the faster the adjustment. A value of 0 makes the adjustment smoother. Expressed as the trend factor within the i-th hour, It is expressed as the actual daily activity completed in the kth hour. Expressed as the target sub-daily active volume in the kth hour. The smoothing factor in this application is set based on historical behavior data, and the target sub-daily active volume in each time period is adjusted by the smoothing factor. That is, if the previous hourly time period does not reach the target sub-daily active volume, the target sub-daily active volume in the current hourly time period will increase accordingly. If the previous hourly time period is exceeded, the target sub-daily active volume in the current hourly time period will decrease accordingly, thereby eliminating noise and fluctuations in the data, making the adjusted target sub-daily active volume closer to the true value, and the distribution in different time periods is more reasonable, thereby improving the user experience. The trend factor is obtained by determining the changing trend of user behavior based on historical behavior data. For example, if the user activity shows an upward trend in a certain time period, It can be greater than 1, and if there is a downward trend, it can be less than 1. Through the above steps, the target sub-daily active volume of the current hour can be further adjusted to reflect the trend change of user behavior, so that the target sub-daily active volume is closer to the actual situation.

[0033] Take the user activity intensity of App A and App B in one day as an example. If the target total daily active users of App A is 10,000 and the target total daily active users of App B is 5,000, a day is divided into four time periods according to the daily active user data of App A and App B, namely: 0-6 o'clock, 6-12 o'clock, 12-18 o'clock, and 18-24 o'clock. For App A: at 0-6 o'clock, the target sub-daily active users are set to 1,000 people (based on past data, the activity in this time period is relatively low, and the active weight is 10%); at 6-12 o'clock, the target sub-daily active users are set to 3,000 people (the activity starts to rise, and the active weight is 30%); at 12-18 o'clock, the target sub-daily active users are set to 3,500 people (usually one of the peak usage periods, with an active weight of 30%). At 18:00-24:00, the target sub-daily active users are set at 2,500 (activity slightly decreased but still high, active weight is 25%); Application B: At 0-6:00, the target sub-daily active users are set at 400 (based on past data, the activity during this period is relatively low, and the active weight is 8%); At 6-12:00, the target sub-daily active users are set at 1,000 (activity begins to rise, active weight is 20%); At 12-18:00, the target sub-daily active users are set at 1,600 (usually one of the peak usage periods, active weight is 32%). At 18:00-24:00, the target sub-daily active users are set at 2,000 (activity slightly decreased but still high, active weight is 40%). Then, based on the active weights of the target sub-daily active volume of application A and application B in each time period, the target sub-daily active volume corresponding to each time period, and the user's historical behavior data, calculate the time-sharing proportion of application A and application B in each time period. For example, the proportion of cloud video small TV is relatively large during meal time (12-18 o'clock) (food videos can be promoted), and the proportion of fitness applications is relatively large after meals (18-24 o'clock) (sports videos can be promoted). Through the above steps, by clarifying the time-sharing proportion of each cooperative application in each time period, the advertising content can be accurately delivered according to this proportion, which helps to ensure that the advertisement is displayed during the time period when the user is most active, and improve the exposure and click-through rate of the advertisement.

[0034] In the embodiment of the present application, after calculating the time-sharing proportion of each cooperative application in each time period, the time-sharing proportion of each cooperative application in each time period is compared with the actual completion status to obtain a comparison result, wherein the actual completion status is used to indicate the completion status of the actual push of advertising content by each cooperative application in each time period, and the actual completion status can be calculated by the following formula: in, It is expressed as the actual completion status within the i-th hour, It is expressed as the actual daily activity in the i-th hour, It is expressed as the target sub-daily active volume within the i-th hour, and then according to the comparison result, the preset algorithm is used to allocate push weights to each cooperative application. It can be seen that the weight allocation of this application reflects the differentiated strategies for different cooperative applications and different time periods, thereby optimizing the advertising push effect.

[0035] It should be noted that the preset algorithm can be hierarchical analysis method, principal component analysis method, entropy method and CRITIC weight method, etc., which can be selected according to the application scenario and needs, and this application does not make any limitation on this.

[0036] In this embodiment, the push weight of each cooperative application is calculated based on the daily active user data, so that a more reasonable push strategy can be formulated later, thereby performing more accurate push and improving the user experience.

[0037] S203: Determine the target cooperative application for pushing the advertisement content according to the order of the push weights of the cooperative applications, and obtain content push strategy data.

[0038] In step S203, the content push strategy data refers to sorting according to the order of push weight, determining the priority of each cooperative application as a push channel, and first using the cooperative application with the highest priority, that is, the largest push weight, as the target cooperative application to push the advertising content (wherein, there can be multiple cooperative applications with the largest push weight at the same time). When the operation feedback generated by the receiving client on the advertising content is received within the specified time, it is confirmed that the push is successful; if the operation feedback generated by the receiving client on the advertising content is not received within the specified time, it is confirmed that the push failed, and a cooperative application with a smaller push weight than the current one is determined as the target cooperative application, and the advertising content push is continued until the operation feedback generated by the receiving client on the advertising content is received. For example, if WeChat has the largest weight value, that is, the highest priority, WeChat push is given priority. If the advertising content is finally displayed that the user has not clicked to reach it, the cooperative application with the second weight is immediately pushed for push, until the advertising content has pushed all the channels that need to be pushed, or the user clicks to reach it and terminates.

[0039] In this embodiment, all cooperative applications are sorted from high to low according to the calculated push weights. For example, there are applications C, D, E, F, etc., and the weights after calculation are 500, 450, 380, and 300 respectively. Then the order after sorting is application C> application D> application E> application F, and then the target cooperative application is determined according to the sorting result. A threshold can be set or several applications with high rankings can be selected as target cooperative applications. For example, the top 3 applications are selected as target cooperative applications. Assuming that the sorted applications are application 1, application 2, application 3, etc., then the content push strategy is to give priority to pushing advertising content to application 1, which is the target cooperative application. When the quota of the push channel of application 1 (the push amount allocated according to its weight ratio) is used up, it will be pushed to application 2, and application 2 will be the target cooperative application, and so on.

[0040] At the same time, the matching degree between the advertising content and the cooperative application should also be considered. For example, for a sports and fitness advertising content, it is more suitable to push it to sports and fitness cooperative applications or comprehensive applications whose user groups are mainly sports and fitness. If there is a food application with a higher weight in the push ranking, but it does not match the sports and fitness advertising content, you may need to adjust the strategy to give priority to pushing the advertising content to sports and fitness applications with a slightly lower weight but a better match. The resources for advertising push are allocated according to the weight ratio of the target cooperative application, including the number of pushes and push time period. For example, the weight of application A in the target cooperative application accounts for 40% of the total weight, the weight of application B accounts for 30%, and the weight of application C accounts for 30%. If the total number of advertising pushes is 1,000 times, then application A is allocated 400 push opportunities, and application B and application C are allocated 300 push opportunities each. The push time period can also be allocated according to the peak time of the daily active users of the application. For example, the user peak of application A is between 7 and 9 pm, so more advertising pushes can be allocated to application A during this period.

[0041] In this embodiment, by sorting the push weights of each cooperative application, it can be ensured that the advertising content is pushed first to the target cooperative application that is most relevant and most likely to attract user interest, and limited push content can be reasonably allocated, thereby improving user click-through rate and user activity.

[0042] S204: When the advertisement content to be pushed that meets the user's interest is screened out, the advertisement content to be pushed is pushed step by step based on the content push strategy data.

[0043] In step S204, in order to better achieve step-by-step push, the personalized differences of users in each cooperative application should be considered, and the advertising content should be screened by using the user data provided by the cooperative application, such as the user's historical behavior, preference settings, etc. For example, in application A, some users are role-playing game enthusiasts (recommended role-playing content), and some users are casual game enthusiasts (recommended casual game content). Then, by analyzing the user's historical behavior, interest preferences, and current market trends, etc., the advertising content to be pushed that meets the user's interests is screened from the database, and the screened advertising content is matched with the target application and its user group. When ensuring that the advertising content not only meets the user's interests, but also keeps consistent with the tonality and user portrait of the target application, the step-by-step push of the advertising content to be pushed is completed based on the content push strategy data.

[0044] Specifically, when the advertising content to be pushed that meets the user's interests is screened out, the cooperative application with the largest push weight is determined as the target cooperative application based on the content push strategy data, and the target cooperative application is used as the push channel for the advertising content push. The advertising content to be pushed is pushed. When the receiving client's operational feedback on the advertising content is received within the specified time period, it is confirmed that the push is successful. If the receiving client's operational feedback on the advertising content is not received within the specified time period, it is confirmed that the push has failed. Then a push instruction is generated again, and a cooperative application with a smaller push weight than the current one is determined as the target cooperative application. The advertising content push is continued until the receiving client's operational feedback on the advertising content is received.

[0045] In an embodiment of the invention, before filtering out the advertisement content to be pushed that meets the user's interest, the process includes: Obtaining a user tag of the target cooperative application; Based on the user tag, the advertisement content to be pushed that matches the target cooperative application and meets the user's interest is screened out from a preset database.

[0046] Specifically, by obtaining the user tag of the target cooperative application, wherein the user tag is a label used to describe information such as user characteristics, behaviors, interests and preferences, for example, for a fitness application, its user tag may include the items that the user frequently exercises (such as yoga, running, strength training, etc.), exercise frequency (how many times a week), fitness equipment brand preferences used, etc., and then based on the user tag, the advertising content to be pushed that matches the target cooperative application and meets the user's interests is screened from the preset database, that is, the advertising content is stored in a preset database, and this database can be indexed according to multiple dimensions such as advertising category, target audience, product features, etc. After obtaining the user tag of the target cooperative application, the query function of the database is used to filter according to the preset matching rules. For example, in a database indexed by interest classification, if the user tag shows interest in technology and digital products, the advertising content of the technology and digital category in the database is queried, which may include advertisements for smart phones, computer accessories, smart wearable devices, etc. By using the advanced query functions of the database, such as fuzzy query, associated query, etc., the accuracy of filtering the advertising content to be pushed is improved. For example, for a user tagged with "healthy diet", fuzzy query can find advertising content containing keywords such as "organic food", "low-calorie food", and "nutritious meal replacement", and through associated query, advertisements for kitchen supplies related to healthy diet (such as juicers, air fryers, etc.) can be found. These advertising contents are likely to meet the user's interests. Through the above steps, based on user tags, it can be ensured that the pushed ads are highly relevant to the user's interests and needs, thereby improving the user's acceptance of advertising content and click-through rate.

[0047] It should be noted that the preset matching rules can be set specifically according to user interests and user behaviors, and this application does not impose any limitation on this.

[0048] In an embodiment of the invention, when the advertisement content to be pushed that meets the user's interest is screened out, the step-by-step pushing of the advertisement content to be pushed is completed based on the content pushing strategy data, including: When the advertisement content to be pushed that meets the user's interest is screened out, a target cooperative application with the largest push weight value is determined as a push channel based on the content push strategy data; Pushing the advertisement content to be pushed to the client of the user in a preset form through the push channel; When a successful reception feedback of the content sent by the client is received within the set time, the push of the advertisement content to be pushed is completed; When no successful content reception feedback is received from the client within the set time period, determining a target cooperative application with the largest next-level push weight as a push channel based on the content push strategy data; The step of pushing the advertisement content to be pushed in a preset form to the client where the user is located through the push channel is executed until a successful reception feedback of the content sent by the client is received within a set time period, thereby completing the pushing of the advertisement content to be pushed.

[0049] Specifically, by analyzing the user's interests, the advertising content to be pushed that meets the user's interests is screened out, and the target cooperative application with the largest push weight value is determined based on the content push strategy data. The target cooperative application is used as the push channel for pushing the advertising content, and the advertising content to be pushed is pushed to the user's client in a preset form through the push channel. When the successful reception feedback of the content sent by the client is received within the set time, the push of the advertising content to be pushed is completed. When the successful reception feedback of the content sent by the client is not received within the set time, the target cooperative application with the largest push weight at the next level is determined as the push channel based on the content push strategy data, and then the advertising content to be pushed is pushed to the user's client in a preset form through the push channel until the successful reception feedback of the content sent by the client is received within the set time, and the push of the advertising content to be pushed is completed. Among them, the preset form can be but not limited to pop-up ads, information flow ads, video ads, graphic ads, coupons and other forms to increase user participation and interactivity. If the target cooperative application is a sports app, in the form of information flow ads, when the user opens the App to browse the information page, the fitness ads will be embedded in a suitable position to display to the user and pushed to the user's mobile client. The setting duration can be set according to actual business needs and network conditions. For example, it is set to 10 minutes. The system starts timing after pushing and waits for the client to feedback whether the advertising content has been successfully received. If feedback is received, it means that the user client has received the advertisement normally, and the push task is completed. Through the above steps, push is based on weight, so that the advertising content is more likely to be received and interacted by users, so as to accurately push according to user interests, thereby improving the success rate of advertising and user experience.

[0050] In an embodiment of the invention, before the advertisement content to be pushed is pushed to the client of the user in a preset form through the push channel, the method includes: Obtaining the number of advertisement contents pushed through the push channel that have not received corresponding feedback; When the number of the advertising contents is greater than or equal to the preset number of advertising contents, determining the target cooperative application with the largest push weight at the next level as the push channel based on the content push strategy data; The step of pushing the advertisement content to be pushed in a preset form to the client where the user is located through the push channel is executed.

[0051] Specifically, after the advertising content is sent through the push channel, the system needs to monitor the feedback information returned from the user client in real time. This can be achieved through an event monitoring mechanism or by periodically querying the feedback status of the user device, and then obtaining the number of advertising content that has not received feedback. At each push or periodic check, the system will determine whether the number of advertisements that have not received feedback is greater than or equal to the preset number threshold. For example, if the preset number is 5 and the current number is 6, the next step is triggered, that is, if the number of advertising content is greater than or equal to the preset number of advertising content, the system will re-evaluate the target cooperative application based on the content push strategy data, that is, calculate the target cooperative application with the largest push weight at the next level as the push channel, and based on the newly determined best push channel, push the advertising content to be pushed to the user client through the channel in a preset form again. When the content sent by the client is successfully received within the set time, the push of the advertising content to be pushed is completed. This application can avoid waste of resources by monitoring the number of unfeedback and selecting the appropriate push channel in a timely manner according to the user's feedback, thereby improving the overall advertising delivery effect and user satisfaction.

[0052] In this embodiment, when the advertising content to be pushed that meets the user's interests is screened out, the push priority of each push channel is determined based on the content push strategy data, and then pushed step by step according to the push priority of each push channel, thereby ensuring that the advertising content can be pushed more accurately, increasing the daily active reach rate, and improving the user experience.

[0053] In summary, the present invention provides a method, device, equipment and medium for distributing advertising content, which obtains daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period, and the push weight of each cooperative application is calculated based on the daily active user data, and the target cooperative application for pushing the advertising content is determined according to the high and low ranking of the push weights of each cooperative application, and the content push strategy data is obtained. When the advertising content to be pushed that meets the user's interests is screened out, the step-by-step push of the advertising content to be pushed is completed based on the content push strategy data. It can be seen that the present application determines the sending order of the advertising content to be pushed in each cooperative application by ranking the push weights of each cooperative application according to the calculation, and then when the advertising content to be pushed that meets the user's interests is screened out, the step-by-step push of the advertising content to be pushed is completed based on the content push strategy data, so that accurate advertising content distribution can be performed in different applications according to user interests, thereby improving the click-through rate of advertisements and user activity.

[0054] See also Figure 3 , Figure 3is a schematic diagram of the structure of the advertising content distribution device provided by the embodiment of the present invention. The advertising content distribution device corresponds to the advertising content distribution method in the above embodiment. Figure 2 as well as Figure 2 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 3 The advertisement content distribution device 30 includes: an acquisition module 31 , a calculation module 32 , a obtaining module 33 , and a pushing module 34 .

[0055] An acquisition module 31 is used to acquire daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period; A calculation module 32, configured to calculate the push weight of each of the cooperative applications according to the daily active user data; An obtaining module 33 is used to determine the target cooperative application to push the advertisement content according to the order of the push weights of the cooperative applications, and obtain content push strategy data; The push module 34 is used to complete the step-by-step push of the advertisement content to be pushed based on the content push strategy data when the advertisement content to be pushed that meets the user's interest is screened out.

[0056] Optionally, the acquisition module 31 is specifically used for: receiving a data cooperation request formed by a client in response to startup instructions of multiple preset applications, wherein the data cooperation request carries identification information of the multiple preset applications; According to the identification information and the preset cooperative application standard, determining in sequence whether the plurality of preset applications are cooperative applications; If the plurality of preset applications are cooperative applications, the step of obtaining daily active user data in each cooperative application is performed.

[0057] Optionally, the calculation module 32 is specifically used for: According to the daily active user data, the time-sharing proportion of each of the cooperative applications in each time period is calculated; Compare the time-sharing proportion of each cooperative application in each time period with the actual completion status to obtain a comparison result, wherein the actual completion status is used to indicate the completion status of the actual push of the advertising content by each cooperative application in each time period; According to the comparison result, a preset algorithm is used to assign a push weight to each of the cooperative applications.

[0058] Optionally, the execution module 33 is also used to: Obtaining the target total daily active volume of each of the cooperative applications within a preset time period; According to the daily active user data, the target total daily active users are split into target sub-daily active users corresponding to each time period using a target splitting algorithm, and the active weights of the target sub-daily active users in each time period are determined; Based on the active weight of the target sub-DAU in each time period and the user's historical behavior data, the time-sharing proportion of each cooperative application in each time period is calculated, wherein the user's historical behavior data includes the user's actual completed DAU in each time period; The time-sharing proportion of each cooperative application in each time period is calculated by the following formula: in, It is expressed as the time-sharing proportion in the i-th hour, Expressed as the target total daily activity, Expressed as the active weight within the i-th hour, Expressed as the sum of active weights, Expressed as a smoothing factor between [0, 1], Expressed as the trend factor within the i-th hour, It is expressed as the actual daily activity completed in the kth hour. Expressed as the target sub-daily activity within the kth hour.

[0059] Optionally, the push module 34 is specifically used for: Obtaining a user tag of the target cooperative application; Based on the user tag, the advertisement content to be pushed that matches the target cooperative application and meets the user's interest is screened out from a preset database.

[0060] Optionally, the push module 34 is specifically used for: When the advertisement content to be pushed that meets the user's interest is screened out, a target cooperative application with the largest push weight value is determined as a push channel based on the content push strategy data; Pushing the advertisement content to be pushed to the client of the user in a preset form through the push channel; When a successful reception feedback of the content sent by the client is received within the set time, the push of the advertisement content to be pushed is completed; When no successful content reception feedback is received from the client within the set time period, determining a target cooperative application with the largest next-level push weight as a push channel based on the content push strategy data; The step of pushing the advertisement content to be pushed in a preset form to the client where the user is located through the push channel is executed until a feedback indicating successful reception of the content sent by the client is received within a set time period, thereby completing the pushing of the advertisement content to be pushed.

[0061] Optionally, the push module 34 is further used for: Obtaining the number of advertisement contents pushed through the push channel that have not received corresponding feedback; When the number of the advertising contents is greater than or equal to the preset number of advertising contents, determining the target cooperative application with the largest push weight at the next level as the push channel based on the content push strategy data; The step of pushing the advertisement content to be pushed in a preset form to the client where the user is located through the push channel is executed.

[0062] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0063] Figure 4 Schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned advertising content distribution method embodiments are implemented.

[0064] The computer device may include, but is not limited to, a processor and a memory. It can be understood by those skilled in the art that Figure 4 These are merely examples of computer devices and do not constitute limitations on the computer devices. The computer devices may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, they may also include a network interface, a display screen, and an input system.

[0065] In one embodiment, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor in a computer device, the computer device can perform the steps of any embodiment of an advertising content distribution method disclosed in the present invention, which will not be repeated here. The computer-readable storage medium can be non-volatile or volatile.

[0066] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0067] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, a cooperative application, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0068] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0069] The technical business in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0070] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for distributing advertising content, characterized in that: include: Acquire daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period; Calculate the push weight of each of the cooperative applications according to the daily active user data; Determine the target cooperative application for pushing the advertisement content according to the push weight ranking of each cooperative application, and obtain content push strategy data; When the advertisement content to be pushed that meets the user's interest is screened out, the advertisement content to be pushed is pushed step by step based on the content push strategy data.

2. The method for distributing advertising content according to claim 1, characterized in that: Before obtaining the daily active user data of each cooperative application, the following steps are included: receiving a data cooperation request formed by a client in response to startup instructions of multiple preset applications, wherein the data cooperation request carries identification information of the multiple preset applications; According to the identification information and the preset cooperative application standard, determining in sequence whether the plurality of preset applications are cooperative applications; If the plurality of preset applications are cooperative applications, the step of obtaining daily active user data in each cooperative application is performed.

3. The method for distributing advertising content according to claim 1, characterized in that: The calculating, according to the daily active user data, the push weight of each of the cooperative applications includes: According to the daily active user data, the time-sharing proportion of each of the cooperative applications in each time period is calculated; Compare the time-sharing proportion of each cooperative application in each time period with the actual completion status to obtain a comparison result, wherein the actual completion status is used to indicate the completion status of the actual push of the advertising content by each cooperative application in each time period; According to the comparison result, a preset algorithm is used to assign a push weight to each of the cooperative applications.

4. The method for distributing advertising content according to claim 3, characterized in that: The time-sharing proportion of each cooperative application in each time period is calculated according to the daily active user data, including: Obtaining the target total daily active volume of each of the cooperative applications within a preset time period; According to the daily active user data, the target total daily active users are split into target sub-daily active users corresponding to each time period using a target splitting algorithm, and the active weights of the target sub-daily active users in each time period are determined; Based on the active weight of the target sub-DAU in each time period and the user's historical behavior data, the time-sharing proportion of each cooperative application in each time period is calculated, wherein the user's historical behavior data includes the user's actual completed DAU in each time period; The time-sharing proportion of each cooperative application in each time period is calculated by the following formula: in, It is expressed as the time-sharing proportion in the i-th hour, Expressed as the target total daily activity, Expressed as the active weight within the i-th hour, Expressed as the sum of active weights, Expressed as a smoothing factor between [0, 1], Expressed as the trend factor within the i-th hour, It is expressed as the actual daily activity completed in the kth hour. Expressed as the target sub-daily activity within the kth hour.

5. The method for distributing advertising content according to claim 1, characterized in that: Before filtering out the advertisement content to be pushed that matches the user's interest, the method includes: Obtaining a user tag of the target cooperative application; Based on the user tag, the advertisement content to be pushed that matches the target cooperative application and meets the user's interest is screened out from a preset database.

6. The method for distributing advertising content according to claim 1, characterized in that: When the advertisement content to be pushed that meets the user's interest is screened out, the step-by-step pushing of the advertisement content to be pushed is completed based on the content pushing strategy data, including: When the advertisement content to be pushed that meets the user's interest is screened out, a target cooperative application with the largest push weight value is determined as a push channel based on the content push strategy data; Pushing the advertisement content to be pushed to the client of the user in a preset form through the push channel; When a successful reception feedback of the content sent by the client is received within the set time, the push of the advertisement content to be pushed is completed; When no successful content reception feedback is received from the client within the set time period, determining a target cooperative application with the largest next-level push weight as a push channel based on the content push strategy data; The step of pushing the advertisement content to be pushed in a preset form to the client where the user is located through the push channel is executed until a feedback indicating successful reception of the content sent by the client is received within a set time period, thereby completing the pushing of the advertisement content to be pushed.

7. The method for distributing advertising content according to claim 6, characterized in that: Before the advertisement content to be pushed is pushed to the client where the user is located in a preset form through the push channel, the method includes: Obtaining the number of advertisement contents pushed through the push channel that have not received corresponding feedback; When the number of the advertising contents is greater than or equal to the preset number of advertising contents, determining the target cooperative application with the largest push weight at the next level as the push channel based on the content push strategy data; The step of pushing the advertisement content to be pushed in a preset form to the client where the user is located through the push channel is executed.

8. An advertising content distribution device, characterized in that: include: An acquisition module, used to acquire daily active user data in each cooperative application, wherein the daily active user data is used to indicate the active intensity of the user using the cooperative application within a preset time period; A calculation module, used to calculate the push weight of each of the cooperative applications according to the daily active user data; An obtaining module is used to determine the target cooperative application for pushing the advertisement content according to the order of the push weights of the cooperative applications, and obtain content push strategy data; The push module is used to complete the step-by-step push of the advertisement content to be pushed based on the content push strategy data when the advertisement content to be pushed that meets the user's interest is screened out.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the advertising content distribution method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the advertising content distribution method according to any one of claims 1 to 7 is implemented.

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