Method and system for marketing management of pan-entertainment products

Abstract

The invention provides a marketing management method and system for a pan-entertainment product, and belongs to the technical field of marketing management, and the method specifically comprises the steps: determining the matching conditions with user portraits of different users according to the use data of a current user, user portrait matching coefficients of the current user and different users and matched users are determined on the basis of the matching conditions, historical operation data of the matched users in the pan-entertainment products are obtained, and the pan-entertainment products are obtained according to the product association conditions between the different pan-entertainment products. And determining product association coefficients and associated products of different pan-entertainment products and matched users, and performing marketing recommendation management of the pan-entertainment products of the current user by using the user portrait matching coefficients of different matched users and the product association coefficients of different associated products and matched users. And the accuracy of marketing recommendation management is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of marketing management, and in particular, relates to a marketing management method and system for pan-entertainment products. Background Art

[0002] With the acceleration of the intelligent process of IPTV, in order to realize personalized marketing management for different TV users, the invention patent application CN201810865656.8 "A method and system for processing TV advertisements" obtains user behavior data and then feeds back user portrait data and advertising marketing effect data, thereby solving the problem of low efficiency of TV advertisement processing in the prior art. However, there are the following technical problems: In the process of marketing and management of IPTV's pan-entertainment products, there are often multiple users in a family for the same IPTV, and there are large differences in the user portraits of different users, such as children, the elderly and young people. Therefore, if the current operating user cannot be identified based on the user's operation data, it is impossible to accurately implement precise marketing recommendation management of pan-entertainment products.

[0003] In view of the above technical problems, the present invention provides a marketing management method and system for pan-entertainment products. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, a marketing management method for pan-entertainment products is provided.

[0005] A marketing management method for pan-entertainment products, specifically comprising: S1, based on the deviation of the historical usage data of the IPTV at different usage times, when it is determined that there are multiple users using the IPTV, proceed to the next step; S2 constructs user profiles of different users based on historical usage data at different usage times, determines matching conditions between the current user and the user profiles of different users based on the usage data of the current user, and determines matching coefficients between the current user and the user profiles of different users and matching users based on the matching conditions; S3: obtaining the historical operation data of the matched user on the pan-entertainment product, and determining the product correlation coefficients and the related products of different pan-entertainment products and the matched user in combination with the product correlation between different pan-entertainment products; S4 utilizes the user portrait matching coefficients of different matching users and the product association coefficients of different associated products and matching users to perform marketing recommendation management of the pan-entertainment products for the current user.

[0006] The beneficial effects of the present invention are: Based on the deviation of IPTV's historical usage data at different usage times, it is determined whether IPTV has multiple users, thereby realizing the deviation of IPTV's historical usage data and screening IPTV with multiple users, avoiding the technical problem that the reliability of marketing recommendation management of pan-entertainment products cannot meet the requirements due to multiple users, and also laying the foundation for further targeted improvement of the accuracy and conversion rate of marketing recommendations.

[0007] By utilizing the user portrait matching coefficients with different matching users, and the product association coefficients between different associated products and matching users, marketing recommendation management of pan-entertainment products for the current user is carried out. This not only takes into account the association between associated products and different matching users, but also takes into account the matching of user portraits of different matching users. This enables targeted marketing recommendation management of pan-entertainment products from multiple angles, and improves the reliability of marketing recommendation associations.

[0008] A further technical solution is that the historical usage data includes software usage data and video browsing data at different usage times.

[0009] A further technical solution is to determine that there are multiple users of the IPTV, specifically including: Determine software usage data and video browsing data at different usage times based on the historical usage data of the IPTV at different usage times; Determine the usage deviation of software types and the browsing deviation of video types at different usage times according to the software usage data and video browsing data at different usage times, and determine the deviation usage times based on the usage deviation and the browsing deviation; The deviation usage times are used to determine whether there are multiple users using the IPTV.

[0010] A further technical solution is that the deviation usage times are determined based on the usage deviation of the software type with other usage times and the browsing deviation of the video type. Specifically, when the other usage times with usage deviation of the software type and the browsing deviation of the video type are greater than the preset usage times, the usage times are determined to be the deviation usage times.

[0011] A further technical solution is that the method for determining the marketing recommendation management of the current user's pan-entertainment products is: Determine the comprehensive correlation coefficient between the associated product and the current user based on the product correlation coefficients between different associated products and matching users and the user portrait matching coefficients between different matching users; Determine whether it is necessary to manage the marketing recommendation of the associated product as a pan-entertainment product for the current user according to the comprehensive correlation coefficient.

[0012] A further technical solution is that the comprehensive correlation coefficient between the associated product and the current user is determined based on the sum of the products of the product correlation coefficient between the associated product and the matching user and the user portrait matching coefficients of different matching users.

[0013] A further technical solution is that when the comprehensive correlation coefficient between the associated product and the current user is greater than a preset correlation coefficient limit value, it is determined that the associated product needs to be managed as a marketing recommendation for the current user's pan-entertainment product.

[0014] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned method for marketing and management of pan-entertainment products when running the computer program.

[0015] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flowchart of the marketing management method of pan-entertainment products; Figure 2 is a flow chart of a method for constructing a user portrait using a user; Figure 3 It is a flow chart of a method for determining a user profile matching coefficient between a current user and different users; Figure 4 It is a flow chart of a method for determining a correlation coefficient between a pan-entertainment product and a product that matches a user; Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0019] For the same IPTV, the user portraits of users within the family, such as children, the elderly and young people, are quite different. Therefore, it is necessary to identify the current operating user based on the user's operation data, so as to accurately implement precision marketing recommendation management of pan-entertainment products.

[0020] Determine that there are multiple users using the IPTV: determine a historical usage number group with similar historical usage data according to historical usage data at different usage times. When there are multiple historical usage number groups, it is determined that there are multiple users using the IPTV.

[0021] User portrait matching coefficient: It is determined based on the ratio of the number of usage features that match the current user's usage data and the user's user portrait. When the ratio of the number of usage features is 0.3, the user portrait matching coefficient is determined to be 0.3, and the user with a user portrait matching coefficient greater than 0.4 is regarded as a matched user.

[0022] The product correlation coefficient between pan-entertainment products and matching users: It is determined based on the correlation between pan-entertainment products and the matching users' historical operations. It is specifically determined based on the proportion of pan-entertainment products with historical operations that are associated. Products with a correlation coefficient greater than 0.6 are considered associated products.

[0023] Conduct marketing recommendation management of pan-entertainment products for the current user: determine the comprehensive correlation coefficient between the related products and the current user based on the product correlation coefficients of different related products and matching users and the user portrait matching coefficients of different matching users, and determine whether the related products need to be managed as marketing recommendations for the current user's pan-entertainment products based on the comprehensive correlation coefficient. Example

[0024] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a marketing management method for pan-entertainment products is provided, which specifically includes: S1, based on the deviation of the historical usage data of the IPTV at different usage times, when it is determined that there are multiple users using the IPTV, proceed to the next step; S2 constructs user profiles of different users based on historical usage data at different usage times, determines matching conditions between the current user and the user profiles of different users based on the usage data of the current user, and determines matching coefficients between the current user and the user profiles of different users and matching users based on the matching conditions; S3: obtaining the historical operation data of the matched user on the pan-entertainment product, and determining the product correlation coefficients and the related products of different pan-entertainment products and the matched user in combination with the product correlation between different pan-entertainment products; S4 utilizes the user portrait matching coefficients of different matching users and the product association coefficients of different associated products and matching users to perform marketing recommendation management of the pan-entertainment products for the current user.

[0025] Furthermore, the historical usage data includes software usage data and video browsing data at different usage times.

[0026] Specifically, determining that there are multiple users of the IPTV includes: Determine software usage data and video browsing data at different usage times based on the historical usage data of the IPTV at different usage times; Determine the usage deviation of software types and the browsing deviation of video types at different usage times according to the software usage data and video browsing data at different usage times, and determine the deviation usage times based on the usage deviation and the browsing deviation; The deviation usage times are used to determine whether there are multiple users using the IPTV.

[0027] Optionally, the deviation usage times are determined based on the usage deviation of the software type with other usage times and the browsing deviation of the video type. Specifically, when the usage times of other types with usage deviations and video types with usage deviations are greater than the preset usage times, the usage times are determined to be the deviation usage times.

[0028] It should be noted that, when the deviation usage times is greater than a preset deviation times threshold, it is determined that there are multiple users using the IPTV.

[0029] Further, determining that there are multiple users of the IPTV specifically includes: Determine software usage data and video browsing data at different usage times based on the historical usage data of the IPTV at different usage times; Determine, according to the software usage data and video browsing data at different usage times, usage similarities of software types and browsing similarities of video types at different usage times, and divide the usage times into different usage time groups based on the usage similarities and browsing similarities; The usage times of different usage times groups are used to determine whether there are multiple users of the IPTV.

[0030] It should also be noted that determining whether there are multiple users of the IPTV by using the usage times of different usage times groups specifically includes: When there are multiple usage number groups whose usage numbers are greater than the preset usage number, it is determined that there are multiple users using the IPTV.

[0031] Optionally, determining that there are multiple users using the IPTV specifically includes: S11 determines the software usage data and video browsing data at different usage times based on the historical usage data of the IPTV at different usage times; Optionally, the above step S11 includes the following contents: S111 determines that the usage times of the IPTV are within a preset usage times range based on the historical usage data of the IPTV at different usage times, then determines that there are no multiple users of the IPTV, and when the usage times of the IPTV are not within the preset usage times range, proceeds to step S112; S112 determines the accumulated usage time of the IPTV based on the historical usage data of the IPTV at different usage times, and when the accumulated usage time of the IPTV does not meet the requirement, proceeds to step S113; when the accumulated usage time of the IPTV meets the requirement, proceeds to step S114; S113 obtains the usage duration under different usage times. When the usage duration is greater than the preset usage duration and the usage times meet the requirement, the process proceeds to step S114. When the usage duration is greater than the preset usage duration and the usage times do not meet the requirement, it is determined that there are no multiple users of the IPTV. S114 determines the software usage data and video browsing data at different usage times based on the historical usage data of the IPTV at different usage times, and then proceeds to step S12.

[0032] S12, according to the software usage data and video browsing data at different usage times, determining the usage similarity of software types and the browsing similarity of video types at different usage times, and dividing the usage times into different usage time groups based on the usage similarity and the browsing similarity; Optionally, the above step S12 includes the following contents: S121 determines the usage deviation of the software type and the browsing deviation of the video type under different usage times according to the software usage data and the video browsing data under different usage times. When there is no usage times with usage deviation of the software type and browsing deviation of the video type, it is determined that there are no multiple users of the IPTV. When there is a usage times with usage deviation of the software type and browsing deviation of the video type, the process proceeds to step S122. S122: the number of times the software type has a usage deviation and the number of times the video type has a browsing deviation is used as the deviation usage number. When the deviation usage number is within the preset deviation number interval, it is determined that there are no multiple users of the IPTV. When the deviation usage number is not within the preset deviation number interval, the process proceeds to step S213. S123 determines the usage similarity of software types and the browsing similarity of video types under different usage times, and divides the usage times into different usage time groups based on the usage similarity and browsing similarity, and then proceeds to step S13.

[0033] S13 determines the usage frequency coefficients of different usage frequency groups by using the usage frequency of different usage frequency groups and the usage durations of different usage frequency groups, and determines whether there are multiple users of the IPTV based on the usage frequency coefficients of different usage frequency groups.

[0034] Optionally, the above step S13 includes the following contents: S131 determines that when the usage number group whose usage number is greater than the preset usage number is within the preset group number interval by using the usage number of different usage number groups, it is determined that there are no multiple users of the IPTV. When the usage number group whose usage number is greater than the preset usage number is not within the preset group number interval, the process proceeds to step S132; S132: The usage number group whose usage number is greater than the preset usage number is used as the alternative number group. When the number of alternative usage groups whose cumulative usage time corresponding to the alternative number group is greater than the preset cumulative time threshold is within the preset group number interval, it is determined that there are no multiple users of the IPTV. When the number of alternative usage groups whose cumulative usage time corresponding to the alternative number group is greater than the preset cumulative time threshold is not within the preset group number interval, the process proceeds to step S133. S133 determines the usage frequency coefficients of different usage frequency groups by using the usage frequency of different usage frequency groups and the usage durations of different usage frequency groups, and determines whether there are multiple users of the IPTV based on the usage frequency coefficients of different usage frequency groups.

[0035] Specifically, Figure 2 As shown, the method for constructing the user portrait of the user is: Determine, according to the software usage data and video browsing data at different usage times, usage similarities of software types and browsing similarities of video types at different usage times, and divide the usage times into different usage time groups based on the usage similarities and browsing similarities; Determine the usage frequency coefficients of different usage frequency groups according to the usage times of different usage frequency groups and the usage durations of different usage times; The software usage data and video browsing data corresponding to the usage number group whose usage frequency coefficient is greater than the preset frequency coefficient threshold are used as the user profile of the user corresponding to the usage group.

[0036] Further, such as Figure 3 As shown, the method for determining the user portrait matching coefficient between the current user and different users is: Using the usage data of the current user to determine matching usage data with features of different dimensions of user profiles of different users; Determine the matching usage time of features in different dimensions using the matching usage data, and determine the matching features using the matching usage time; The user portrait matching coefficient between the current user and the using user is determined according to the ratio of the number of matching features.

[0037] Furthermore, the matching feature is a feature that the matching usage time is longer than a preset matching time.

[0038] Optionally, the user portrait matching coefficient between the current user and the using user ranges from 0 to 1, wherein when the user portrait matching coefficient between the current user and the using user is greater than a preset coefficient threshold, the using user is determined to be a matching using user.

[0039] In another embodiment, the method for determining the user portrait matching coefficient between the current user and different users is: Using the usage data of the current user to determine matching usage data with features of different dimensions of user profiles of different users; Determine the matching usage time of features in different dimensions using the matching usage data, and determine the matching coefficients with features in different dimensions using the matching usage time; The user portrait matching coefficient between the current user and the using user is determined according to the average value of the matching coefficients of the features of different dimensions.

[0040] Optionally, the method for determining the user portrait matching coefficient between the current user and different users is: S21 determines matching usage data of features of different dimensions with the user profile of the user using the usage data of the current user, and determines a basic profile matching coefficient using the number ratio of features with matching usage data; S22: obtaining the matching usage data to determine the matching usage times of features in different dimensions and the matching usage durations of different matching usage times, and determining the matching coefficients with features in different dimensions by using the matching usage times and the matching usage durations of different matching usage times; S23 determines matching features according to matching coefficients with features of different dimensions, and determines a user portrait matching coefficient between the current user and the using user using a proportion of the number of matching features and the basic portrait matching coefficient.

[0041] Furthermore, the historical operation data includes historical browsing data and historical ordering data of the pan-entertainment products.

[0042] Specifically, the product association between the pan-entertainment products is based on the corresponding customers, product types and service forms of the pan-entertainment products.

[0043] It should be noted that if Figure 4 As shown, the method for determining the correlation coefficient between the pan-entertainment product and the product matching the user is as follows: By matching the historical operation data of the user on the pan-entertainment products, the historical browsing data and historical ordering data on different pan-entertainment products are determined, and the historical browsing data and historical ordering data of the pan-entertainment products are used to determine the historical matching products; Determine the number of historical matching products associated with the pan-entertainment product according to the matching conditions between different pan-entertainment products and historical matching products; The product correlation coefficient between the pan-entertainment product and the matching user is determined by using the number of historical matching products associated with the pan-entertainment product.

[0044] Furthermore, when the correlation coefficient between the pan-entertainment product and the product of the matching user is greater than a preset correlation coefficient threshold, the pan-entertainment product is determined to be a related product.

[0045] It should be noted that the method for determining the marketing recommendation management of the current user's pan-entertainment products is: Determine the comprehensive correlation coefficient between the associated product and the current user based on the product correlation coefficients between different associated products and matching users and the user portrait matching coefficients between different matching users; Determine whether it is necessary to manage the marketing recommendation of the associated product as a pan-entertainment product for the current user according to the comprehensive correlation coefficient.

[0046] Furthermore, the comprehensive correlation coefficient between the associated product and the current user is determined based on the sum of the products of the product correlation coefficient between the associated product and the matching user and the user portrait matching coefficients of different matching users.

[0047] It should be further explained that, when the comprehensive correlation coefficient between the associated product and the current user is greater than a preset correlation coefficient limit value, it is determined that the associated product needs to be managed as a marketing recommendation of the current user's pan-entertainment product.

[0048] Optionally, the above step S21 includes the following contents: S211 determines the current usage time of the current user based on the usage data of the current user. When the current usage time of the current user is less than the preset usage time, it is determined that the current user cannot determine a matching user. When the current usage time of the current user is not less than the preset usage time, the process proceeds to step S212. S212 determines matching usage data of features of different dimensions of the user profile of the user using the usage data of the current user. If there is no feature with matching usage data, it is determined that the user does not belong to the matching user. If there is a feature with matching usage data, the process proceeds to step S213. S213 determines the basic portrait matching coefficient using the percentage of the number of features that have matching usage data. When the basic portrait matching coefficient does not meet the requirements, it is determined that the user does not belong to the matching user. When the basic portrait matching coefficient meets the requirements, it proceeds to step S22.

[0049] Optionally, the above step S22 includes the following contents: S221 determines the total duration of matching and using the features in different dimensions with the matching and using data. When the total duration of matching and using the features in different dimensions meets the requirement, the process proceeds to step S223. When the total duration of matching and using the features in different dimensions does not meet the requirement, the process proceeds to step S222. S222: obtaining the matching usage data to determine the matching usage time of features in different dimensions. When the matching usage time of features in different dimensions is less than a preset usage time threshold, it is determined that the user does not belong to the matching usage user. When the matching usage time of features in different dimensions is not less than the preset usage time threshold, the process proceeds to step S223. S223 uses the matching usage times and the matching usage durations of different matching usage times to determine the matching coefficients with features of different dimensions. When there are features whose matching coefficients meet the requirements, the process proceeds to step S23. When there are no features whose matching coefficients meet the requirements, it is determined that the user does not belong to the matched user. Example

[0050] Second, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned method for marketing and management of pan-entertainment products when running the computer program.

[0051] Optionally, the method for determining the correlation coefficient between the pan-entertainment product and the product matching the user is: By matching the historical operation data of the user on the pan-entertainment products, the historical browsing data and the historical ordering data on different pan-entertainment products are determined, and the historical browsing data and the historical ordering data of the pan-entertainment products are used to perform historical matching products and historical matching coefficients of different historical matching products; When it is determined that there is no associated historical matching product for the pan-entertainment product according to the matching conditions between different pan-entertainment products and historical matching products, it is determined that the pan-entertainment product does not belong to an associated product; When determining that the pan-entertainment product has associated historical matching products: Obtain the number of historical matching products associated with the pan-entertainment product. When the number of historical matching products associated with the pan-entertainment product is within a preset product quantity range: Taking the historical matching products associated with the pan-entertainment product as the associated matching products, obtaining the correlation coefficients with different associated matching products, and when the average value of the correlation coefficients with different associated matching products is within a preset correlation coefficient range: determining that the pan-entertainment product is not an associated product; When the average value of the correlation coefficients with different correlation matching products is not within the preset correlation coefficient range: Obtaining historical matching coefficients of different associated matching products, and when the historical matching coefficients of different associated matching products are within a preset matching coefficient range: determining that the pan-entertainment product does not belong to an associated product; When the historical matching coefficients of different associated matching products are not within the preset matching coefficient range: Determine the corrected correlation coefficients of different associated matching products by multiplying the historical matching times of different associated matching products by the correlation coefficients. When the average values ​​of the corrected correlation coefficients of different associated matching products are within a preset correction coefficient range, determine that the pan-entertainment product does not belong to an associated product. When the number of historical matching products associated with the pan-entertainment product is within the preset product quantity range or the average value of the corrected correlation coefficients of different associated matching products is not within the preset correction coefficient range: The product correlation coefficient between the pan-entertainment product and the matched user is determined by using the corrected correlation coefficient of the historical matching product associated with the pan-entertainment product.

[0052] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0053] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A marketing management method for pan-entertainment products, characterized in that: Specifically include: When it is determined that there are multiple users using the IPTV based on the deviation of the historical usage data of the IPTV at different usage times, proceed to the next step; Constructing user portraits of different users based on historical usage data at different usage times, determining matching conditions between the current user and the user portraits of different users based on the usage data of the current user, and determining matching coefficients between the current user and the user portraits of different users and matching users based on the matching conditions; Obtain the historical operation data of the matched user on the pan-entertainment products, and determine the product correlation coefficients and related products of different pan-entertainment products and the matched user in combination with the product correlation between different pan-entertainment products; By utilizing the user portrait matching coefficients with different matching users and the product association coefficients between different associated products and matching users, marketing recommendation management of the pan-entertainment products for the current user is performed.

2. The marketing management method for pan-entertainment products according to claim 1, characterized in that: The historical usage data includes software usage data and video browsing data at different usage times.

3. The marketing management method for pan-entertainment products according to claim 1, characterized in that: Determining that there are multiple users of the IPTV specifically includes: Determine software usage data and video browsing data at different usage times based on the historical usage data of the IPTV at different usage times; Determine the usage deviation of software types and the browsing deviation of video types at different usage times according to the software usage data and video browsing data at different usage times, and determine the deviation usage times based on the usage deviation and the browsing deviation; The deviation usage times are used to determine whether there are multiple users using the IPTV.

4. The marketing management method for pan-entertainment products according to claim 3, characterized in that: The deviation usage times are determined based on the usage deviation of the software type with other usage times and the browsing deviation of the video type. Specifically, when the other usage times of the software type with usage deviation and the video type with usage deviation are greater than the preset usage times, the usage times are determined to be the deviation usage times.

5. The marketing management method for pan-entertainment products according to claim 3, characterized in that: When the deviation usage times is greater than a preset deviation times threshold, it is determined that there are multiple users using the IPTV.

6. The marketing management method for pan-entertainment products according to claim 1, characterized in that: The method for determining the user portrait matching coefficient between the current user and different users is as follows: Using the usage data of the current user to determine matching usage data with features of different dimensions of user profiles of different users; Determine the matching usage time of features in different dimensions using the matching usage data, and determine the matching features using the matching usage time; The user portrait matching coefficient between the current user and the using user is determined according to the ratio of the number of matching features.

7. The marketing management method for pan-entertainment products according to claim 1, characterized in that: The method for determining the marketing recommendation management of the current user's pan-entertainment products is: Determine the comprehensive correlation coefficient between the associated product and the current user based on the product correlation coefficients between different associated products and matching users and the user portrait matching coefficients between different matching users; Determine whether it is necessary to manage the marketing recommendation of the associated product as a pan-entertainment product for the current user according to the comprehensive correlation coefficient.

8. The marketing management method for pan-entertainment products according to claim 7, characterized in that: The comprehensive correlation coefficient between the associated product and the current user is determined based on the sum of the products of the product correlation coefficient between the associated product and the matching user and the user portrait matching coefficients of different matching users.

9. The marketing management method for pan-entertainment products according to claim 1, characterized in that: When the comprehensive correlation coefficient between the associated product and the current user is greater than a preset correlation coefficient limit value, it is determined that the associated product needs to be managed as a marketing recommendation of the current user's pan-entertainment product.

10. A computer system comprising: A memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a method for marketing and management of pan-entertainment products as described in any one of claims 1-9 is executed.

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