Television entitlement recommendation method and apparatus, and storage medium and server

Abstract

The present application relates to the technical field of the Internet. Disclosed are a television entitlement recommendation method and apparatus, and a storage medium and a server. The method comprises: on the basis of user feature data, a plurality of television entitlement packages and a package combination policy rule, performing entitlement package combination analysis processing, so as to obtain combination scores of a plurality of television entitlement package combinations; and selecting a television entitlement package combination having a combination score that meets a predetermined condition. By means of the present application, suitable television entitlement package combinations can be recommended to different users.

Description

Recommended methods, devices, storage media, and servers for TV access permissions

[0001] This application claims priority to Chinese Patent Application No. 202411480260.3, filed on October 22, 2024, entitled "Television Permission Recommendation Method, Apparatus, Storage Medium and Server", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of Internet technology, specifically to a method, apparatus, storage medium, and server for recommending TV permissions. Background Technology

[0003] As a device closely related to people's lives, recommending appropriate TV permissions has a significant impact on people's TV viewing experience. These permissions include various TV viewing memberships (such as monthly movie memberships) and various TV operation permissions.

[0004] Currently, in related technologies, the way TV permissions are recommended on TV is usually by uniformly recommending several TV permissions configured by the operations team according to fixed rules for users to choose from. For example, several membership packages may be recommended on TV for users to choose from. Technical issues

[0005] Under the current system, TV permission recommendations often fail to effectively meet users' actual needs, resulting in a need for further improvement in the TV user experience, and some TV permissions have a low user selection and usage rate. Technical solutions

[0006] This application provides a TV permission recommendation scheme that can effectively recommend suitable TV permissions, improve the TV user experience, and increase the user selection and usage rate of TV permissions.

[0007] The embodiments of this application provide the following technical solutions:

[0008] According to one embodiment of this application, a TV permission recommendation method includes: acquiring user feature data, multiple TV permission packages, and package combination strategy rules; performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations; selecting TV permission package combinations whose combination scores meet predetermined conditions from the multiple TV permission package combinations based on the combination scores corresponding to the multiple TV permission package combinations to obtain a combination to be recommended; and recommending the combination to be recommended.

[0009] In some embodiments of this application, the step of performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain the combination score corresponding to multiple TV permission package combinations includes: taking each TV permission package combination as a candidate individual, performing genetic solution based on a genetic algorithm using the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain the fitness corresponding to multiple candidate individuals; and obtaining the combination score of the TV permission package combination corresponding to each candidate individual based on the fitness corresponding to each candidate individual.

[0010] In some embodiments of this application, the step of using a genetic algorithm to perform genetic solving using the user feature data, the various TV permission packages, and the package combination strategy rules to obtain the fitness of multiple candidate individuals includes: generating an initial population based on the various TV permission packages, wherein the initial population includes multiple initial individuals, and each initial individual includes multiple TV permission packages; calculating the fitness of each initial individual using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules; and performing selection optimization processing on the multiple initial individuals based on the fitness of each initial individual to obtain the multiple candidate individuals and the fitness of each candidate individual.

[0011] In some embodiments of this application, the step of selecting and optimizing the plurality of initial individuals based on their fitness to obtain the plurality of candidate individuals and their corresponding fitness includes: selecting individuals from the plurality of initial individuals to obtain a plurality of parent individuals; performing crossover on the plurality of parent individuals to obtain a plurality of child individuals; performing mutation on the plurality of child individuals to obtain a plurality of mutated individuals; updating the initial population with the plurality of mutated individuals to obtain an updated population; and iteratively performing fitness calculation, individual selection, crossover, mutation, and individual updating on the updated population until the termination condition is met to obtain the plurality of candidate individuals and their corresponding fitness.

[0012] In some embodiments of this application, the step of calculating the fitness of each initial individual by using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules includes: for each initial individual, calculating the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rules; determining the proportion of TV permission packages included in the initial individual whose matching scores are higher than a predetermined threshold; and obtaining the fitness of the initial individual based on the proportion.

[0013] In some embodiments of this application, the step of performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations includes: inputting the user feature data, the multiple TV permission packages, and the package combination strategy rules into a preset package combination analysis model for permission package combination analysis processing to obtain combination scores corresponding to multiple TV permission package combinations.

[0014] In some embodiments of this application, the step of selecting a TV permission package combination whose combination score meets a predetermined condition from the plurality of TV permission package combinations to obtain a recommended combination includes: selecting a TV permission package combination whose combination score ranks higher than the predetermined score from the plurality of TV permission package combinations; and determining the TV permission package combination whose combination score ranks higher than the predetermined score as the recommended combination.

[0015] In some embodiments of this application, the multiple TV access packages include multiple different TV viewing membership packages and / or multiple different TV operation access packages.

[0016] In some embodiments of this application, the multiple TV permission packages are determined as follows: obtaining permission requirement information from the target TV; obtaining TV permission packages matching the permission requirement information from the permission pool to obtain the multiple TV permission packages.

[0017] According to one embodiment of this application, a TV permission recommendation device includes: an acquisition module for acquiring user feature data, multiple TV permission packages, and package combination strategy rules; an analysis module for performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations; a selection module for selecting TV permission package combinations whose combination scores meet predetermined conditions from the multiple TV permission package combinations based on the combination scores corresponding to the multiple TV permission package combinations to obtain a combination to be recommended; and a recommendation module for recommending the combination to be recommended.

[0018] In some embodiments of this application, the analysis module is configured to: take each of the TV permission package combinations as candidate individuals, perform genetic solving based on a genetic algorithm using the user feature data, the multiple TV permission packages, and the package combination strategy rules, to obtain the fitness corresponding to multiple candidate individuals; and obtain the combination score of the TV permission package combination corresponding to each candidate individual based on the fitness corresponding to each candidate individual.

[0019] In some embodiments of this application, the analysis module is configured to: generate an initial population based on the multiple TV permission packages, the initial population including multiple initial individuals, wherein each initial individual includes multiple TV permission packages; calculate the fitness of each initial individual by using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules; and perform selection optimization processing on the multiple initial individuals based on the fitness of each initial individual to obtain the multiple candidate individuals and the fitness corresponding to each candidate individual.

[0020] In some embodiments of this application, the analysis module is configured to: select individuals from the plurality of initial individuals to obtain a plurality of parent individuals; perform crossover processing on the plurality of parent individuals to obtain a plurality of child individuals; perform mutation operations on the plurality of child individuals to obtain a plurality of mutated individuals; update the initial population with the plurality of mutated individuals to obtain an updated population; and iteratively perform fitness calculation, individual selection, crossover processing, mutation operations, and individual updates on the updated population until the termination condition is met to obtain the plurality of candidate individuals and their corresponding fitness.

[0021] In some embodiments of this application, the analysis module is configured to: calculate, for each initial individual, the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rule; determine the proportion of TV permission packages included in the initial individual whose matching scores are higher than a predetermined threshold; and obtain the fitness of the initial individual based on the proportion.

[0022] In some embodiments of this application, the analysis module is used to: input the user feature data, the multiple TV permission packages, and the package combination strategy rules into a preset package combination analysis model for permission package combination analysis processing, and obtain combination scores corresponding to multiple TV permission package combinations.

[0023] In some embodiments of this application, the selection module is configured to: select from the plurality of TV permission package combinations the TV permission package combination corresponding to the combination score that ranks higher than the predetermined score; and determine the TV permission package combination corresponding to the combination score that ranks higher than the predetermined score as the combination to be recommended.

[0024] In some embodiments of this application, the multiple TV access packages include multiple different TV viewing membership packages and / or multiple different TV operation access packages.

[0025] In some embodiments of this application, the multiple TV permission packages are determined as follows: obtaining permission requirement information from the target TV; obtaining TV permission packages matching the permission requirement information from the permission pool to obtain the multiple TV permission packages.

[0026] According to another embodiment of this application, a storage medium stores a computer program thereon, which, when executed by a server processor, causes the computer to perform the methods described in the embodiments of this application.

[0027] According to another embodiment of this application, a server may include: a memory storing a computer program; and a processor reading the computer program stored in the memory to execute the methods described in the embodiments of this application.

[0028] According to another embodiment of this application, a computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor on the server reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the server to perform the methods provided in the various optional implementations described in the embodiments of this application. Beneficial effects

[0029] In this embodiment, user feature data, multiple TV permission packages, and package combination strategy rules are obtained; permission package combination analysis is performed based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations; based on the combination scores corresponding to the multiple TV permission package combinations, TV permission package combinations whose combination scores meet predetermined conditions are selected from the multiple TV permission package combinations to obtain recommended combinations; and the recommended combinations are recommended.

[0030] In this way, permission package combination analysis is performed based on user characteristic data, various TV permission packages, and package combination strategy rules to obtain combination scores for multiple TV permission package combinations. Then, TV permission package combinations that meet the predetermined conditions are selected from multiple TV permission package combinations to obtain the recommended combination. The recommended combination is then recommended, which can effectively recommend suitable TV permission package combinations for different users, improve the TV user experience, and increase the user selection and usage rate of TV permissions. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 shows a flowchart of a television permission recommendation method according to an embodiment of this application.

[0033] Figure 2 shows a flowchart of the genetic solution according to an embodiment of this application.

[0034] Figure 3 shows a selection optimization flowchart according to one embodiment of this application.

[0035] Figure 4 shows a block diagram of a television permission recommendation device according to an embodiment of this application.

[0036] Figure 5 shows a block diagram of a server according to an embodiment of this application.

[0037] Implementation methods of this application

[0038] The present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the embodiments provided herein are merely illustrative of the present disclosure and are not intended to limit the present disclosure. Furthermore, the embodiments provided below are some embodiments for implementing the present disclosure, and not all embodiments for implementing the present disclosure. Unless otherwise specified, the technical solutions described in the embodiments of the present disclosure can be implemented in any combination.

[0039] It should be noted that, in the embodiments of this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a method or apparatus that includes a list of elements includes not only the elements expressly described, but also other elements not expressly listed, or elements inherent to implementing the method or apparatus. Without further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of other related elements (e.g., steps in the method or units in the apparatus, such as portions of circuitry, processors, programs, or software, etc.) in the method or apparatus that includes that element.

[0040] For example, the TV permission recommendation method provided in this embodiment includes a series of steps, but the TV permission recommendation method provided in this embodiment is not limited to the steps described. Similarly, the TV permission recommendation device provided in this embodiment includes a series of units, but the device provided in this embodiment is not limited to the units explicitly described, and may also include units that need to be set up for obtaining relevant information or processing based on information.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of this disclosure.

[0042] It is understood that in the specific implementation of this application, user characteristic data and other related data are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0043] Figure 1 schematically illustrates a flowchart of a television permission recommendation method according to an embodiment of this application. The executing entity of this television permission recommendation method can be any server with processing capabilities, such as a terminal or a server. Terminals include televisions themselves, computers, mobile phones, smartwatches, and home appliances, while servers include cloud servers or physical servers.

[0044] As shown in Figure 1, the TV permission recommendation method may include steps S110 to S140.

[0045] Step S110: Obtain user feature data, multiple TV permission packages, and package combination strategy rules; Step S120: Perform permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations; Step S130: Based on the combination scores corresponding to the multiple TV permission package combinations, select TV permission package combinations whose combination scores meet predetermined conditions from the multiple TV permission package combinations to obtain recommended combinations; Step S140: Recommend the recommended combinations.

[0046] User characteristic data refers to the relevant characteristic data of users who use televisions. For example, for TV A, user characteristic data of users who use TV A can be obtained, and for TV B, user characteristic data of users who use TV B can be obtained. User characteristic data may include, but is not limited to, one or more of the following: user attributes (such as age, gender, etc.), permission selection and usage history data (such as previously purchased movie and TV memberships or TV operation permissions), behavioral data (such as movie and TV viewing history data), and tag information (such as user type tags).

[0047] TV access packages are packages of TV access rights. Multiple TV access packages can include various different TV viewing membership packages (such as TV membership package A, TV membership package B, etc.) and / or various different TV operation permission packages (such as game access package A, game access package B, face recognition unlocking access package, voice control access package, etc.).

[0048] Package combination strategy rules are the relevant combination strategy rules for TV access packages. Package combination strategy rules are usually some combination strategy rules related to TV access packages configured by the TV manufacturer's operations personnel according to some operational needs. Package combination strategy rules may include, but are not limited to, package distribution rules, package purchase limit rules, user traffic acquisition rules, etc.

[0049] The server can obtain user characteristic data, multiple TV permission packages, and package combination strategy rules from a specified location. Then, it performs permission package combination analysis based on the user characteristic data, multiple TV permission packages, and package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations. Each TV permission package combination includes multiple TV permission packages, and the multiple TV permission packages in different TV permission package combinations may be partially or completely different.

[0050] A higher combination score for a TV access package indicates a greater degree of compatibility between the package, the user, and the package strategy rules. Based on the combination scores of multiple TV access package combinations, the TV access package combination that meets the predetermined criteria is selected to obtain the recommended combination.

[0051] The recommended combinations are then presented. For example, multiple TV access packages included in the recommended combination can be combined into a package list and displayed on the target TV. The target TV refers to the TV logged into by the user whose user characteristic data matches the user's login data. For instance, if user characteristic data matches both user 1 and user 2, the package list can be recommended and displayed on TV 1 and TV 2, where user 1 and user 2 are logged in.

[0052] In this way, permission package combination analysis is performed based on user characteristic data, various TV permission packages, and package combination strategy rules to obtain combination scores for multiple TV permission package combinations. Then, TV permission package combinations that meet the predetermined conditions are selected from multiple TV permission package combinations to obtain the recommended combination. The recommended combination is then recommended, which can effectively recommend suitable TV permission package combinations for different users, improve the TV user experience, and increase the user selection and usage rate of TV permissions.

[0053] The following describes further optional embodiments of the steps performed when recommending TV permissions in the embodiment shown in Figure 1.

[0054] In one embodiment, the various TV access packages described in this application include a variety of different TV viewing membership packages and / or a variety of different TV operation access packages.

[0055] TV access packages are packages of TV access rights. Multiple TV access packages can include various TV viewing membership packages (such as TV membership package A, TV membership package B, etc.) and / or various TV operation access packages (such as game access package A, game access package B, face recognition unlock access package, voice control access package, etc.).

[0056] In one embodiment of this application, the multiple TV access packages include multiple different TV viewing membership packages. In another embodiment of this application, the multiple TV access packages only include multiple different TV operation access packages. In yet another embodiment of this application, the multiple TV access packages include both multiple different TV operation access packages and multiple different TV viewing membership packages.

[0057] Furthermore, in one embodiment, when obtaining the multiple TV permission packages, all preset TV permission packages in the permission pool can be used as the multiple TV permission packages.

[0058] In another embodiment, the multiple TV permission packages are determined as follows: obtaining permission requirement information from the target TV; obtaining TV permission packages matching the permission requirement information from the permission pool to obtain the multiple TV permission packages.

[0059] In this embodiment, for a target TV, the permission requirement information of the target TV is first obtained. Then, TV permission packages matching the permission requirement information are retrieved from the permission pool, resulting in multiple TV permission packages. Further, based on these multiple TV permission packages matching the permission requirement information, a recommended combination can be determined to further improve the TV permission recommendation effect. The permission requirement information of the target TV can be input by the user via voice or interface.

[0060] In one embodiment, the step of performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations may include: inputting the user feature data, the multiple TV permission packages, and the package combination strategy rules into a preset package combination analysis model for permission package combination analysis processing to obtain combination scores corresponding to multiple TV permission package combinations.

[0061] The preset package combination analysis model can be a model obtained by fine-tuning an existing large model or by training other networks such as convolutional upgrade networks. By inputting user feature data, various TV permission packages, and package combination strategy rules into the preset package combination analysis model for permission package combination analysis, the combination scores corresponding to multiple TV permission package combinations can be obtained efficiently and reliably.

[0062] Furthermore, in one embodiment, the step of performing permission package combination analysis based on the user characteristic data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations includes:

[0063] Each of the aforementioned TV permission package combinations is taken as a candidate individual. Based on a genetic algorithm, the user feature data, the various TV permission packages, and the package combination strategy rules are used to perform a genetic solution to obtain the fitness of multiple candidate individuals. Based on the fitness of each candidate individual, the combination score of the TV permission package combination corresponding to each candidate individual is obtained.

[0064] Genetic Algorithm (GA) is an evolutionary algorithm used for optimization problems. It seeks optimal solutions by simulating natural selection and genetic mechanisms. The applicant discovered that by treating various TV subscription packages as candidate individuals, and using a genetic algorithm with user feature data, multiple TV subscription packages, and package combination strategy rules, the fitness of multiple candidate individuals can be obtained at low cost and with high interpretability. The fitness of these multiple candidate individuals, as a combination score, can effectively reflect the quality of the multiple candidate individuals obtained through genetic optimization.

[0065] Based on fitness (i.e., a combination score), candidate individuals that meet predetermined conditions can be selected from multiple candidate individuals as recommended combinations. Combining and recommending TV permission packages included in these recommended combinations can further effectively recommend suitable TV permission package combinations to users, thereby improving the TV user experience and increasing the user selection and usage rate of TV permissions.

[0066] In one embodiment, referring to Figure 2, the genetic algorithm-based approach, using the user feature data, the various TV access packages, and the package combination strategy rules, performs a genetic solution to obtain the fitness of multiple candidate individuals. Specifically, this may include:

[0067] Step S210: Generate an initial population based on the multiple TV permission packages, the initial population including multiple initial individuals, wherein each initial individual includes multiple TV permission packages; Step S220: Calculate the fitness of each initial individual by using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules; Step S230: Perform selection optimization processing on the multiple initial individuals based on the fitness of each initial individual to obtain multiple candidate individuals and the fitness corresponding to each candidate individual.

[0068] An initial population can be generated by randomly combining various TV access packages. This initial population includes multiple initial individuals, and each initial individual includes multiple TV access packages. The TV access packages in different initial individuals may be partially or completely different.

[0069] Based on the preset fitness function, the fitness can be calculated using user feature data and the package attributes of the TV permission packages included in each initial individual, according to the package combination strategy rules. The fitness of each initial individual is also the degree of superiority or inferiority of the package combination formed by multiple TV permission packages in each initial individual.

[0070] Furthermore, based on the fitness of each initial individual, multiple initial individuals are selected and optimized. Finally, the optimal solution is found by simulating natural selection and genetic mechanisms, resulting in multiple candidate individuals and their corresponding fitness.

[0071] Furthermore, the step of calculating the fitness of each initial individual by using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules can specifically include:

[0072] For each initial individual, calculate the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rule; determine the proportion of TV permission packages included in the initial individual whose matching score is higher than a predetermined threshold; and obtain the fitness of the initial individual based on the proportion.

[0073] Based on a preset fitness function, for each initial individual, the matching score between user feature data and the package attributes of each TV access package included in the initial individual and the package combination strategy rules can be calculated. These attributes include, for example, membership type, distribution strategy, price, term type, and purchase limit rules. For instance, if initial individual A includes TV access packages 1, 2, ..., n, the matching score 1 between user feature data and TV access package 1 and the package combination strategy rules can be calculated, as can the matching score 2 between user feature data and TV access package 2 and the package combination strategy rules, ..., and the matching score n between user feature data and TV access package n and the package combination strategy rules.

[0074] Furthermore, the proportion of TV access packages with matching scores higher than a predetermined threshold included in the initial individual can be statistically determined, and the fitness of the initial individual can be obtained based on this proportion. For example, for initial individual A, the number of TV access packages with matching scores higher than the predetermined threshold in initial individual A can be determined as s, thus the proportion is s / n. Based on this proportion, the fitness of initial individual A can be further calculated by adding points, where different proportions predetermine corresponding fitness values.

[0075] Furthermore, referring to Figure 3, the step of selecting and optimizing the plurality of initial individuals based on their fitness to obtain the plurality of candidate individuals and the fitness of each candidate individual may specifically include:

[0076] Step S310: Select individuals from the plurality of initial individuals to obtain a plurality of parent individuals; Step S320: Perform crossover on the plurality of parent individuals to obtain a plurality of child individuals; Step S330: Perform mutation on the plurality of child individuals to obtain a plurality of mutated individuals; Step S340: Update the initial population with the plurality of mutated individuals to obtain an updated population; Step S350: Iterate the fitness calculation, individual selection, crossover, mutation, and individual update on the updated population until the termination condition is met to obtain the plurality of candidate individuals and their corresponding fitness.

[0077] Specifically, the roulette wheel selection method can be used to select individuals from multiple initial individuals, and the selected initial individuals can be used as multiple parent individuals. Furthermore, a multi-point crossover method can be used to perform crossover processing, exchanging some genes (i.e., TV access packages) between two parent individuals; the resulting parent individuals are the multiple mutated individuals. Further, mutation operations can be used to randomly change certain genes of mutated individuals with a certain probability, thereby obtaining multiple mutated individuals.

[0078] Multiple mutated individuals can be added to the initial population, and some low-fitness individuals can be removed from the initial population to update the population and obtain a more diverse updated population. The fitness calculation, individual selection, crossover, mutation operation, and individual update steps in the aforementioned steps are performed iteratively on this updated population until the termination condition is met (e.g., reaching a predetermined number of iterations or a fitness threshold). The final population can include multiple candidate individuals, and the fitness of each candidate individual can be obtained.

[0079] Furthermore, in one embodiment, the step of selecting a TV permission package combination whose combination score meets predetermined conditions from the plurality of TV permission package combinations to obtain a recommended combination includes:

[0080] Select the TV permission package combination that ranks higher than the predetermined score from the multiple TV permission package combinations; determine the TV permission package combination that ranks higher than the predetermined score as the recommended combination.

[0081] Each TV access package combination receives a corresponding score. The TV access package combination with the highest score among multiple combinations is selected. This pre-set score ranking can be set according to actual needs; for example, if the pre-set score ranking is 2, the highest-ranked TV access package combination will be selected. The TV access package combinations with the highest scores among the pre-set score combinations are identified as the recommended combinations and recommended to the target TV. This effectively recommends suitable TV access package combinations to users on the target TV, improving the TV user experience and increasing user selection and usage rates of TV access.

[0082] To facilitate better implementation of the television permission recommendation method provided in the embodiments of this application, this application also provides a television permission recommendation device based on the above-described television permission recommendation method. The meanings of the terms used are the same as in the television permission recommendation method described above, and specific implementation details can be found in the descriptions in the method embodiments. Figure 4 shows a block diagram of a television permission recommendation device according to an embodiment of this application.

[0083] As shown in Figure 4, the TV permission recommendation device 400 may include: an acquisition module 410 for acquiring user feature data, multiple TV permission packages, and package combination strategy rules; an analysis module 420 for performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations; a selection module 430 for selecting TV permission package combinations whose combination scores meet predetermined conditions from the multiple TV permission package combinations based on the combination scores corresponding to the multiple TV permission package combinations to obtain a combination to be recommended; and a recommendation module 440 for recommending the combination to be recommended.

[0084] In some embodiments of this application, the analysis module is configured to: take each of the TV permission package combinations as candidate individuals, perform genetic solving based on a genetic algorithm using the user feature data, the multiple TV permission packages, and the package combination strategy rules, to obtain the fitness corresponding to multiple candidate individuals; and obtain the combination score of the TV permission package combination corresponding to each candidate individual based on the fitness corresponding to each candidate individual.

[0085] In some embodiments of this application, the analysis module is configured to: generate an initial population based on the multiple TV permission packages, the initial population including multiple initial individuals, wherein each initial individual includes multiple TV permission packages; calculate the fitness of each initial individual by using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules; and perform selection optimization processing on the multiple initial individuals based on the fitness of each initial individual to obtain the multiple candidate individuals and the fitness corresponding to each candidate individual.

[0086] In some embodiments of this application, the analysis module is configured to: select individuals from the plurality of initial individuals to obtain a plurality of parent individuals; perform crossover processing on the plurality of parent individuals to obtain a plurality of child individuals; perform mutation operations on the plurality of child individuals to obtain a plurality of mutated individuals; update the initial population with the plurality of mutated individuals to obtain an updated population; and iteratively perform fitness calculation, individual selection, crossover processing, mutation operations, and individual updates on the updated population until the termination condition is met to obtain the plurality of candidate individuals and their corresponding fitness.

[0087] In some embodiments of this application, the analysis module is configured to: calculate, for each initial individual, the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rule; determine the proportion of TV permission packages included in the initial individual whose matching scores are higher than a predetermined threshold; and obtain the fitness of the initial individual based on the proportion.

[0088] In some embodiments of this application, the analysis module is used to: input the user feature data, the multiple TV permission packages, and the package combination strategy rules into a preset package combination analysis model for permission package combination analysis processing, and obtain combination scores corresponding to multiple TV permission package combinations.

[0089] In some embodiments of this application, the selection module is configured to: select from the plurality of TV permission package combinations the TV permission package combination corresponding to the combination score that ranks higher than the predetermined score; and determine the TV permission package combination corresponding to the combination score that ranks higher than the predetermined score as the combination to be recommended.

[0090] In some embodiments of this application, the multiple TV access packages include multiple different TV viewing membership packages and / or multiple different TV operation access packages.

[0091] In some embodiments of this application, the multiple TV permission packages are determined as follows: obtaining permission requirement information from the target TV; obtaining TV permission packages matching the permission requirement information from the permission pool to obtain the multiple TV permission packages.

[0092] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0093] Furthermore, this application also provides a server, as shown in FIG5. FIG5 shows a block diagram of a server according to an embodiment of this application, specifically:

[0094] The server may include one or more processors 501 with processing cores, one or more memory 502 with computer-readable storage media, power supply 503, and other components. Those skilled in the art will understand that the server structure shown in Figure 5 does not constitute a limitation on the server and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0095] Processor 501 is the control center of the server, connecting various parts of the computer device via various interfaces and lines. It performs various functions and processes data by running or executing software programs and / or modules stored in memory 502, and by calling data stored in memory 502, thereby providing overall monitoring of the server. Optionally, processor 501 may include one or more processing cores; preferably, processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user page, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 501.

[0096] The memory 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the memory 502. The memory 502 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 502 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 502 may also include a memory controller to provide the processor 501 with access to the memory 502.

[0097] The server also includes a power supply 503 that supplies power to the various components. Preferably, the power supply 503 can be logically connected to the processor 501 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 503 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0098] Although not shown, the server may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 501 in the server will load the executable files corresponding to the processes of one or more computer programs into the memory 502 according to the following instructions, and the processor 501 will run the computer programs stored in the memory 502 to realize the various functions in the foregoing embodiments of this application. For example, the processor 501 can perform the following steps:

[0099] The system acquires user characteristic data, various TV permission packages, and package combination strategy rules; it performs permission package combination analysis based on the user characteristic data, the various TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations; based on the combination scores corresponding to the multiple TV permission package combinations, it selects TV permission package combinations whose combination scores meet predetermined conditions from the multiple TV permission package combinations to obtain recommended combinations; and it combines and recommends the TV permission packages included in the recommended combinations.

[0100] In some embodiments of this application, the step of performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain the combination score corresponding to multiple TV permission package combinations includes: taking each TV permission package combination as a candidate individual, performing genetic solution based on a genetic algorithm using the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain the fitness corresponding to multiple candidate individuals; and obtaining the combination score of the TV permission package combination corresponding to each candidate individual based on the fitness corresponding to each candidate individual.

[0101] In some embodiments of this application, the step of using a genetic algorithm to perform genetic solving using the user feature data, the various TV permission packages, and the package combination strategy rules to obtain the fitness of multiple candidate individuals includes: generating an initial population based on the various TV permission packages, wherein the initial population includes multiple initial individuals, and each initial individual includes multiple TV permission packages; calculating the fitness of each initial individual using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules; and performing selection optimization processing on the multiple initial individuals based on the fitness of each initial individual to obtain the multiple candidate individuals and the fitness of each candidate individual.

[0102] In some embodiments of this application, the step of selecting and optimizing the plurality of initial individuals based on their fitness to obtain the plurality of candidate individuals and their corresponding fitness includes: selecting individuals from the plurality of initial individuals to obtain a plurality of parent individuals; performing crossover on the plurality of parent individuals to obtain a plurality of child individuals; performing mutation on the plurality of child individuals to obtain a plurality of mutated individuals; updating the initial population with the plurality of mutated individuals to obtain an updated population; and iteratively performing fitness calculation, individual selection, crossover, mutation, and individual updating on the updated population until the termination condition is met to obtain the plurality of candidate individuals and their corresponding fitness.

[0103] In some embodiments of this application, the step of calculating the fitness of each initial individual by using the user feature data and the package attributes of the TV permission packages included in each initial individual according to the package combination strategy rules includes: for each initial individual, calculating the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rules; determining the proportion of TV permission packages included in the initial individual whose matching scores are higher than a predetermined threshold; and obtaining the fitness of the initial individual based on the proportion.

[0104] In some embodiments of this application, the step of performing permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations includes: inputting the user feature data, the multiple TV permission packages, and the package combination strategy rules into a preset package combination analysis model for permission package combination analysis processing to obtain combination scores corresponding to multiple TV permission package combinations.

[0105] In some embodiments of this application, the step of selecting a TV permission package combination whose combination score meets a predetermined condition from the multiple TV permission package combinations based on the combination score corresponding to the multiple TV permission package combinations to obtain a recommended combination includes: selecting a TV permission package combination whose combination score ranks higher than the predetermined score from the multiple TV permission package combinations; and determining the TV permission package combination whose combination score ranks higher than the predetermined score as the recommended combination.

[0106] In some embodiments of this application, the multiple TV access packages include multiple different TV viewing membership packages and / or multiple different TV operation access packages.

[0107] In some embodiments of this application, the multiple TV permission packages are determined as follows: obtaining permission requirement information from the target TV; obtaining TV permission packages matching the permission requirement information from the permission pool to obtain the multiple TV permission packages.

[0108] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by a computer program, or by a computer program controlling related hardware. The computer program can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0109] Therefore, embodiments of this application also provide a storage medium storing a computer program that can be loaded by a processor to execute the steps in any of the methods provided in embodiments of this application.

[0110] The storage medium can be a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0111] Since the computer program stored in the storage medium can execute the steps of any of the methods provided in the embodiments of this application, the beneficial effects that the methods provided in the embodiments of this application can achieve can be realized. For details, please refer to the previous embodiments, which will not be repeated here.

[0112] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0113] It should be understood that this application is not limited to the embodiments described above and shown in the accompanying drawings, but various modifications and changes can be made without departing from its scope.

Claims

1. A method for recommending TV permissions, wherein, include: Acquire user characteristic data, various TV permission packages, and package combination strategy rules; Based on the user characteristic data, the various TV permission packages, and the package combination strategy rules, the permission package combination analysis and processing are performed to obtain the combination scores corresponding to multiple TV permission package combinations. From the multiple TV access package combinations, select the TV access package combination whose combination score meets the predetermined conditions to obtain the recommended combination; The proposed combinations will be recommended.

2. The method according to claim 1, wherein, The step involves performing permission package combination analysis based on the user characteristic data, the various TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations, including: Each of the aforementioned TV permission package combinations is taken as a candidate individual. Based on the genetic algorithm, the user feature data, the various TV permission packages, and the package combination strategy rules are used to perform genetic solution to obtain the fitness of multiple candidate individuals. Based on the fitness of each candidate individual, the combination score of the TV permission package combination corresponding to each candidate individual is obtained.

3. The method according to claim 2, wherein, The genetic algorithm, using the user feature data, the various TV access packages, and the package combination strategy rules, performs a genetic solution to obtain the fitness of multiple candidate individuals, including: An initial population is generated based on the various TV access packages. The initial population includes multiple initial individuals, and each initial individual includes multiple TV access packages. According to the package combination strategy rules, the fitness is calculated using the user feature data and the package attributes of the TV permission packages included in each initial individual to obtain the fitness of each initial individual; Based on the fitness of each initial individual, the initial individuals are selected and optimized to obtain the candidate individuals and the fitness of each candidate individual.

4. The method according to claim 3, wherein, The step of selecting and optimizing the plurality of initial individuals based on their fitness to obtain the plurality of candidate individuals and the fitness of each candidate individual includes: Multiple parent individuals are obtained by selecting individuals from the multiple initial individuals; The multiple parent individuals are cross-processed to obtain multiple child individuals; Mutation operations are performed on the multiple offspring individuals to obtain multiple mutated individuals; The initial population is updated by using the multiple mutated individuals to obtain an updated population. The updated population is iterated through fitness calculation, individual selection, crossover, mutation, and individual updates until the termination condition is met, resulting in the multiple candidate individuals and their corresponding fitness.

5. The method according to claim 3, wherein, The fitness calculation, performed according to the package combination strategy rules, using the user feature data and the package attributes of the TV access packages included in each initial individual, to obtain the fitness of each initial individual includes: For each of the initial individuals, calculate the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rule; Determine the proportion of TV access packages included in the initial individuals whose matching scores are higher than a predetermined threshold; The fitness of the initial individual is obtained based on the stated ratio.

6. The method according to claim 1, wherein, The step involves performing permission package combination analysis based on the user characteristic data, the various TV permission packages, and the package combination strategy rules to obtain combination scores corresponding to multiple TV permission package combinations, including: The user feature data, the various TV permission packages, and the package combination strategy rules are input into a preset package combination analysis model for permission package combination analysis and processing to obtain the combination scores corresponding to multiple TV permission package combinations.

7. The method according to claim 1, wherein, The step of selecting a TV access package combination that meets the predetermined criteria from the multiple TV access package combinations to obtain a recommended combination includes: Select the TV access package combination that ranks higher than the pre-determined score from the multiple TV access package combinations; The TV access package combination corresponding to the combination score that ranks higher than the predetermined score is determined as the recommended combination.

8. The method according to claim 1, wherein, The various TV access packages include a variety of different TV viewing membership packages and / or a variety of different TV operation access packages.

9. The method according to claim 8, wherein, The various TV access packages are determined in the following manner: Obtain permission request information from the target TV; The TV permission packages that match the permission requirement information are obtained from the permission pool, thus obtaining the various TV permission packages.

10. The method according to claim 9, wherein, The process of obtaining permission requirement information from the target TV includes: Receive permission request information from the user via voice or interface.

11. The method according to claim 1, wherein, The various TV permission packages are determined as follows: all preset TV permission packages in the permission pool are used as the various TV permission packages.

12. The method according to claim 1, wherein, The step of recommending the combination to be recommended includes: The various TV access packages included in the recommended combination will be displayed on the target TV through a package list.

13. The method according to claim 12, wherein, The methods for determining the target television include: The TV that the user who matches the user feature data logs into is identified as the target TV.

14. A television permission recommendation device, wherein, include: The acquisition module is used to acquire user characteristic data, various TV permission packages, and package combination strategy rules; The analysis module is used to perform permission package combination analysis processing based on the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain the combination scores corresponding to multiple TV permission package combinations. The selection module is used to select TV permission package combinations whose combination scores meet predetermined conditions from the multiple TV permission package combinations based on the combination scores corresponding to the multiple TV permission package combinations, so as to obtain the recommended combination; The recommendation module is used to recommend the combinations to be recommended.

15. The apparatus according to claim 14, wherein, The analysis module is used to: take each of the TV permission package combinations as candidate individuals, and perform genetic solving based on a genetic algorithm using the user feature data, the multiple TV permission packages, and the package combination strategy rules to obtain the fitness of multiple candidate individuals; and obtain the combination score of the TV permission package combination corresponding to each candidate individual based on the fitness of each candidate individual.

16. The apparatus according to claim 15, wherein, The analysis module is configured to: generate an initial population based on the multiple TV access packages, wherein the initial population includes multiple initial individuals, and each initial individual includes multiple TV access packages; calculate the fitness of each initial individual by using the user feature data and the package attributes of the TV access packages included in each initial individual according to the package combination strategy rules; and perform selection optimization processing on the multiple initial individuals based on the fitness of each initial individual to obtain multiple candidate individuals and the fitness of each candidate individual.

17. The apparatus according to claim 16, wherein, The analysis module is configured to: select individuals from the plurality of initial individuals to obtain a plurality of parent individuals; perform crossover processing on the plurality of parent individuals to obtain a plurality of child individuals; perform mutation operations on the plurality of child individuals to obtain a plurality of mutated individuals; update the initial population with the plurality of mutated individuals to obtain an updated population; and iteratively perform fitness calculation, individual selection, crossover processing, mutation operations, and individual updates on the updated population until the termination condition is met to obtain the plurality of candidate individuals and their corresponding fitness.

18. The apparatus according to claim 16, wherein, The analysis module is used to: calculate, for each initial individual, the matching score between the user feature data and the package attributes of each TV permission package included in the initial individual and the package combination strategy rules; determine the proportion of TV permission packages included in the initial individual whose matching scores are higher than a predetermined threshold; and obtain the fitness of the initial individual based on the proportion.

19. A storage medium, wherein, It stores a computer program that, when executed by the server's processor, causes the computer to perform the method described in any one of claims 1 to 13.

20. A server-side component, wherein, include: Memory, which stores computer programs; A processor reads a computer program stored in memory to perform the method described in any one of claims 1 to 13.

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