CN113794649A - Information flow distribution method and device, storage medium and computing equipment - Google Patents
Information flow distribution method and device, storage medium and computing equipment Download PDFInfo
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Abstract
The invention provides an information flow distribution method and device, a storage medium and a computing device, wherein the method comprises the following steps: acquiring network information provided by a content provider, and performing content quality evaluation on the network information to obtain content evaluation parameters corresponding to the network information; counting audience coverage information of the network information; and predicting flow distribution parameters corresponding to each network information based on the content evaluation parameters and the audience coverage information, and performing flow distribution on the network information by using the flow distribution parameters. The scheme provided by the invention judges the content quality of the network information provided by each content provider through a uniform standard, predicts reasonable flow distribution parameters for the corresponding network information, improves the flow efficiency satisfaction of the content providers to the platform, and realizes the consistency of the flow distribution platform target and the content provider target.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an information flow distribution method and device, a storage medium and computing equipment.
Background
The information traffic distribution system generally comprises a traffic platform side, a traffic consumer and a traffic content provider, wherein the traffic content provider provides high-quality material and content supply, the traffic consumer browses the content, and finally a part of browsing exposure completes conversion action. In addition, the flow distribution is intervened by some people, and the decision process of the flow distribution can damage the target of the platform under the condition of too many human interventions.
The traditional guarantee regulation and control system is used as a functional system, exposure is mainly regulated according to a target appointed by a content provider, and consistency of a guarantee result and a platform long-term target is not guaranteed; in addition, in a costless flow distribution system, the goal of the guarantee amount is difficult to reasonably specify, each content provider wants to take the most flow, the game process has no cost, and people can increase the exposure requirement upwards; in such an asymmetric competitive environment, there will be insufficient competition, many human-defined factors will appear, and the problem of unreasonable amount of guarantee will easily appear.
Disclosure of Invention
In view of the above problems, the present invention has been made to provide an information traffic distribution method and apparatus, a storage medium, and a computing device that overcome or at least partially solve the above problems.
According to a first aspect of the present invention, there is provided an information traffic distribution method, the method comprising:
acquiring network information provided by a content provider, and performing content quality evaluation on the network information to obtain content evaluation parameters corresponding to the network information;
counting audience coverage information of the network information;
and predicting flow distribution parameters corresponding to the network information based on the content evaluation parameters and the audience coverage information, and carrying out flow distribution on the network information by using the flow distribution parameters.
In another embodiment, the predicting, based on the content rating parameters and the audience coverage information, traffic distribution parameters corresponding to the network information further comprises:
issuing at least one incentive task to each of the content providers;
and acquiring the completion state of each content provider for the incentive task, and summarizing the flow incentive parameters obtained by each content provider for completing the incentive task.
In another embodiment, the predicting, based on the content rating parameters and the audience coverage information, traffic distribution parameters corresponding to each of the network information includes:
and predicting the flow distribution parameters corresponding to the network information by combining the content evaluation parameters corresponding to the network information, the audience coverage information and the flow excitation parameters corresponding to the content providers.
In another embodiment, the predicting, based on the content rating parameters and the audience coverage information, traffic distribution parameters corresponding to each of the network information includes:
sequencing the network information according to content evaluation parameters corresponding to the network information, and distributing initial flow parameters by combining the audience coverage information;
acquiring target flow parameters provided by each content provider aiming at the corresponding network information;
and adjusting the initial flow parameter according to the target flow parameter and the information characteristics corresponding to the network information to obtain a flow distribution parameter corresponding to the network information.
In another embodiment, the method further comprises:
in the flow distribution process, acquiring real-time flow information corresponding to each network information; judging whether each network information can reach a corresponding predicted flow value or not based on the real-time flow information;
if not, generating a regulation factor corresponding to each flow distribution parameter, adjusting the flow distribution parameters based on the regulation factor, and performing flow distribution on the network information by using the adjusted flow distribution parameters.
In another embodiment, the generating the control factor corresponding to each of the flow distribution parameters includes:
and identifying information characteristics corresponding to the network information and current time characteristics, and generating a regulation factor corresponding to each flow distribution parameter according to the information characteristics corresponding to the network information and the time characteristics.
In another embodiment, the performing content quality evaluation on the network information to obtain content evaluation parameters corresponding to the network information includes:
utilizing a pre-trained content quality evaluation model to evaluate the content quality of the network information to obtain content evaluation parameters corresponding to the network information; for any one of the network information, the content evaluation parameter corresponding to the network information is positively correlated with the conversion efficiency corresponding to the network information.
According to a second aspect of the present invention, there is also provided an information traffic distribution apparatus, the apparatus comprising:
the information acquisition module is used for acquiring network information provided by a content provider, and performing content quality evaluation on the network information to obtain content evaluation parameters corresponding to the network information;
the information counting module is used for counting audience coverage information of the network information;
and the distribution module is used for predicting flow distribution parameters corresponding to the network information based on the content evaluation parameters and the audience coverage information and carrying out flow distribution on the network information by using the flow distribution parameters.
In another embodiment, the apparatus further comprises: the incentive module is used for issuing at least one incentive task to each content provider; and acquiring the completion state of each content provider for the incentive task, and summarizing the flow incentive parameters obtained by each content provider for completing the incentive task.
In another embodiment, the assignment module is further configured to: and predicting the flow distribution parameters corresponding to the network information by combining the content evaluation parameters corresponding to the network information, the audience coverage information and the flow excitation parameters corresponding to the content providers.
In another embodiment, the assignment module is further configured to: sequencing the network information according to content evaluation parameters corresponding to the network information, and distributing initial flow parameters by combining the audience coverage information;
acquiring target flow parameters provided by each content provider aiming at the corresponding network information;
and adjusting the initial flow parameter according to the target flow parameter and the information characteristics corresponding to the network information to obtain a flow distribution parameter corresponding to the network information.
In another embodiment, the assignment module is further configured to:
in the flow distribution process, acquiring real-time flow information corresponding to each network information; judging whether each network information can reach a corresponding predicted flow value or not based on the real-time flow information;
and when the network information cannot reach the corresponding predicted flow value, generating a regulation factor corresponding to each flow distribution parameter, adjusting the flow distribution parameter based on the regulation factor, and performing flow distribution on the network information by using the adjusted flow distribution parameter.
In another embodiment, the assignment module is further configured to:
and identifying information characteristics corresponding to the network information and current time characteristics, and generating a regulation factor corresponding to each flow distribution parameter according to the information characteristics corresponding to the network information and the time characteristics.
In another embodiment, the information obtaining module is further configured to: utilizing a pre-trained content quality evaluation model to evaluate the content quality of the network information to obtain content evaluation parameters corresponding to the network information; for any one of the network information, the content evaluation parameter corresponding to the network information is positively correlated with the conversion efficiency corresponding to the network information. Characterized in that the computer readable storage medium is adapted to store program code for performing the method of any of the first aspects.
According to a fourth aspect of the present invention, there is also provided a computing device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method of any of the first aspect according to instructions in the program code.
In the scheme provided by the invention, the content quality of the network information provided by each content provider is judged according to a uniform standard, a reasonable flow distribution parameter is estimated for the corresponding network information, the flow efficiency of the content provider to the platform is improved, the viscosity of the content provider to the flow distribution platform can be improved at the same time, and the consistency of the platform target and the content provider target is realized.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flow chart illustrating an information traffic distribution method according to an embodiment of the present invention;
FIG. 2 illustrates an overall flow diagram of traffic distribution according to an embodiment of the invention;
FIG. 3 is a schematic structural diagram of an information traffic distribution apparatus according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an information traffic distribution apparatus according to another embodiment of the present invention;
FIG. 5 illustrates a schematic diagram of a computing device architecture, according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a schematic flow diagram of an information traffic distribution method according to an embodiment of the present invention, and as can be seen from fig. 1, the information traffic distribution method provided in the embodiment of the present invention at least includes the following steps S101 to S103, and the information traffic distribution method can be applied to information platforms such as an interaction platform and a transaction platform that can perform traffic distribution.
S101, network information provided by a content provider is obtained, content quality evaluation is carried out on the network information, and content evaluation parameters corresponding to the network information are obtained.
The content provider in this embodiment may be a content provider that provides material information such as news information, article information, multimedia information, and commodity information, where the network information is any piece of network information that can be browsed and viewed by a user or a consumer, such as the above-mentioned news information and commodity information. In general, the network information may be provided by the content providers, and the number of the network information provided by each content provider may be plural or one.
And for any piece of acquired network information, content quality evaluation can be carried out on the network information to obtain a corresponding content evaluation parameter.
In this optional embodiment, the content quality evaluation may be performed on the network information by using a content quality evaluation model trained in advance, so as to obtain a content evaluation parameter corresponding to the network information. For any network information, the content evaluation parameter corresponding to the network information is positively correlated with the conversion efficiency corresponding to the network information. That is, the content evaluation parameter is positively correlated with the natural distribution efficiency of the network information, and the higher the conversion efficiency of the network information is, the higher the corresponding content evaluation structure is.
The content quality evaluation model in the embodiment is an intelligent algorithm model, which can be built based on a neural network or a machine learning architecture and is obtained by training a large amount of collected sample data. Each sample in the sample data may include information content and content evaluation parameters for evaluating the content quality of the information content, and the content evaluation parameters corresponding to the content quality may be calculated according to the conversion rate (e.g., article browsing amount, commodity purchasing amount, etc.) of the information content. The input data of the content quality evaluation model may be network information, and the output data is corresponding content evaluation parameters, where the content evaluation parameters may be parameters which are formulated by a unified standard and can quantify content quality of the network information, and the specific form of the content evaluation model is not limited in the embodiments of the present invention. For each piece of acquired network information provided by the content provider, the acquired network information can be input to a uniform content quality evaluation model for quality evaluation to obtain corresponding content evaluation parameters.
And S102, counting audience coverage information of the network information.
The information receiver in this embodiment refers to an audience user who receives or views the network information based on the traffic distribution platform, and the audience coverage information may include characteristic information corresponding to the information receiver that is available for receiving the network information. The characteristic information corresponding to the information receiving party can be the basic characteristic information of the user, the characteristic information of the geographical position where the user is located and other characteristic information related to the information receiving party. The user characteristic information may include information such as the gender, age, information receiving preference, reading and browsing habits, and commodity purchasing habits of the user.
When audience coverage information of network information is counted, counting can be performed based on users in a certain time period, or counting can be performed based on users using a current flow distribution platform, specifically, in the counting, audience flow information in a certain time period in the future can be predicted by combining historical flow data of the flow distribution platform, or a specific counting mode is determined according to different application scenarios and requirements, which is not limited in the embodiment of the present invention.
And S103, predicting flow distribution parameters corresponding to each network information based on the content evaluation parameters and the audience coverage information, and carrying out flow distribution on the network information by using the flow distribution parameters.
After the content evaluation parameters corresponding to the network information and the counted audience coverage information are obtained, the flow distribution parameters corresponding to the network information can be predicted, and then the flow distribution parameters are utilized to distribute the flow of the network information so as to push the network information to different information receivers. The traffic distribution parameter is used to indicate audience information of the network information, and the audience information may include audience number, audience region distribution information, audience age, and the like.
In this embodiment, the step S103 may specifically include the following steps when predicting the traffic distribution parameter corresponding to each network information:
s103-1, ranking the network information according to content evaluation parameters corresponding to the network information, and distributing initial flow parameters by combining audience coverage information.
In this embodiment, in a natural situation, natural recommendation may be performed according to the goal of maximizing the current traffic value. In the above embodiment, the content evaluation parameter may be a parameter for quantifying the content quality of the network information, and therefore, in this embodiment, the network information may be ranked according to the content evaluation parameter obtained by evaluating the content quality, and an initial traffic parameter is allocated to each network information by combining the obtained audience coverage information, where the initial traffic parameter includes the number of audiences currently capable of receiving the network information and parameters such as the age of a user receiving the network information and the location of the user.
S103-2, acquiring target flow parameters provided by each content provider aiming at the corresponding network information.
For each piece of network information, when providing the network information, the corresponding content provider may provide the target traffic parameter that is expected to be achieved at the same time, and at this time, the initial traffic parameter may be adjusted according to the target traffic parameter provided by the content provider, so as to obtain the traffic distribution parameter corresponding to each piece of network information. Such as the amount of audience users that the content provider desires to reach, etc.
S103-3, adjusting the initial flow parameters according to the target flow parameters and the information characteristics corresponding to the network information to obtain flow distribution parameters corresponding to the network information. The information characteristics corresponding to the network information may include information category, timeliness of the information, and the like. For example, assuming that the network information is a network article, the information characteristics corresponding to the network information may include related information such as article categories (e.g., current news, entertainment news, or review articles), article authors, article providers, and the like. Assuming that the network information is commodity information, the corresponding information features may include commodity category, commodity price, commodity evaluation, and the like. In this embodiment, the flow distribution parameters are obtained by adjusting the initial flow parameters according to the target flow parameters of the content providers and the information characteristics of the information content itself, so that the information popularization requirements of the platform can be met, the conversion of each content provider is guaranteed to be expected as much as possible, the flow efficiency of the content provider on the flow distribution platform is improved to be satisfactory, high-quality supply can be continuously provided, the user stickiness of the platform can be improved, and the consistency of the platform target and the content provider target is realized.
In an optional embodiment of the present invention, before predicting the traffic distribution parameter corresponding to each network information based on the content evaluation parameter and the audience coverage information in step S103, at least one incentive task may be issued to each content provider; and acquiring the completion state of each content provider for the incentive tasks, and summarizing the flow incentive parameters obtained by each content provider for completing the incentive tasks. Furthermore, the flow distribution parameters corresponding to the network information can be predicted by combining the content evaluation parameters corresponding to the network information, the audience coverage information and the flow excitation parameters corresponding to the content providers.
In other words, the traffic distribution platform in this embodiment may also issue an incentive task to each content provider, and a content provider that completes the incentive task may obtain a corresponding traffic incentive according to a task completion state, which is denoted as a traffic incentive parameter in this embodiment. Furthermore, the flow distribution parameters corresponding to the network information can be predicted by combining the content evaluation parameters corresponding to the network information, the audience coverage information and the flow excitation parameters corresponding to the content providers. The incentive task issued to the content provider may be understood as a task executed by the content provider and capable of acquiring a traffic reward. For example, the content provider may obtain some traffic rewards after providing certain quality of network information, or may obtain certain traffic rewards when the conversion rate of multiple pieces of network information provided by the content provider exceeds a certain value, and so on.
Steps S103-1 to 103-3 in the above embodiment mention that the traffic distribution parameter corresponding to each network information may be obtained by adjusting based on the initial traffic parameter, and further, in this embodiment, the traffic distribution parameter and the traffic excitation parameter obtained in step S103-3 may be calculated according to a certain calculation rule to obtain a final traffic distribution parameter. Of course, in the calculation process, it is assumed that, for the network information a, a content provider corresponding to the network information a needs to be identified, and a final traffic distribution parameter of the network information a is obtained by calculating a traffic excitation parameter corresponding to the content provider and a traffic distribution parameter corresponding to the network information a. The specific calculation rule may be various, for example, it may be assumed that the audience numbers in the two parameters are directly added, or the audience numbers are added by allocating different ratios, and the like. According to the scheme provided by the embodiment, the content provider is stimulated to provide information by adopting a mode of distributing stimulation tasks, and meanwhile, the traffic distribution platform can also realize operation and guidance of traffic in a content stimulation mode.
In the optional embodiment of the present invention, during the flow distribution process, the real-time flow information corresponding to each network information may be obtained; judging whether each network information can reach a corresponding predicted flow value or not based on the real-time flow information; if not, generating a regulation factor corresponding to each flow distribution parameter, adjusting the flow distribution parameter based on the regulation factor, and performing flow distribution on the network information by using the adjusted flow distribution parameter. Wherein, generating the regulating and controlling factors corresponding to the flow distribution parameters comprises: and identifying information characteristics and current time characteristics corresponding to each network information, and generating a regulation factor corresponding to each flow distribution parameter according to the information characteristics and the time characteristics corresponding to each network information.
In the actual flow distribution process, whether the flow of each network information can reach a predicted flow value can be predicted by combining with the real-time flow state, and the predicted flow value can be the number of audiences obtained by prediction in the flow distribution parameters. If the flow distribution according to the predicted flow distribution parameters can not reach the predicted flow value, the regulating and controlling factors corresponding to the flow distribution parameters can be generated according to the information characteristics of the network information and the current corresponding time period.
The information characteristic of the network information may be a scene adapted by the network information or a scene characteristic that needs to be pushed, such as a scene category, a scene requirement, and the like. In addition, different types of network information may be suitable for different pushed time periods, so in the embodiment of the present invention, a current corresponding time period may also be obtained, and a regulation factor corresponding to each flow distribution parameter is generated in combination with the current time period. For example, for news-like network information, the corresponding timeliness is strong, the emergency degree is also high, and a regulation factor for increasing traffic can be generated, so that the exposure rate of the network information is increased after the traffic distribution parameter corresponding to the network information is adjusted by the regulation factor.
Based on the scheme provided by the present embodiment, it is assumed that each content provider provides
Providing corresponding network information
Wherein each network information has a corresponding translation target
For example, the browsing volume of articles, the selling volume of goods, etc., each content provider wants to achieve a conversion goal corresponding to the network information, and from the perspective of the content provider, the optimization goal is:
wherein,
gain i representing a transformation objective of the content provider, assuming an eventiOccur, thenI i Equal to 1, and others equal to 0.
There are two goals for the traffic distribution platform, one is to maximize the user conversion gain for the current traffic, and the other is to increase the long-term user size of the traffic.
Goal 1, maximizing conversion yield for current flow, where
Is a target of content providers, such as exposure/click/sale, etc.:
target 2, the long-term user scale of the promoted traffic, the user scale cannot be directly modeled, and generally, it is considered that the better the supply, the better the user will remain, therefore, it can be assumed here that the current scenario is relatively related to the supplied scale and the supplied quality, and therefore, the number of high-quality supplies needs to be increased by the platform-side target:
wherein,
representing a content rating parameter, i.e. the quality of the information content.
In this embodiment, it is assumed that the motivation for the content provider to be willing to provide a better premium offer next time is related to the historical achievement rate, i.e. the higher the traffic conversion delivered by the historical platform achieves, the more likely the content provider is to provide the next better offer next time.
At the same time, the platform may have a tendency to the direction of feed of the content, e.g., to favor a particular feed type at a particular time, giving content producers traffic-sustaining incentives
Thus:
in combination with the above theoretical mechanism, fig. 2 shows an overall flow diagram of traffic allocation according to an embodiment of the present invention, and overall, the scheme provided by this embodiment may include:
1. for a piece of network information, the content quality of the network information is evaluated to obtain a content evaluation parameter, the content evaluation parameter is positively correlated with the natural distribution efficiency of the content, and the higher the conversion efficiency of the content is, the higher the content quality parameter is.
2. And issuing an excitation task to the content provider, and generating a flow excitation parameter according to the completion state of the excitation task.
3. And acquiring audience coverage information, predicting reasonable flow distribution parameters based on the audience coverage information, and simultaneously carrying out flow distribution after adjusting the flow distribution parameters by combining with the flow excitation parameters to obtain final flow distribution parameters.
4. When the flow distribution is carried out, whether each network information can reach a predicted flow value or not is judged by combining with the real-time flow information, and if the network information cannot reach the standard, the network information is adjusted according to the information characteristics and the time characteristics. The predicted traffic value may be the number of audiences predicted from the traffic distribution parameter, and different types of network information may be suitable for different periods of time for pushing, for example, for news-like network information, the corresponding timeliness is strong, the emergency degree is high, and a regulation factor for increasing traffic can be generated, so that the exposure rate of the network information is increased after the traffic distribution parameter corresponding to the network information is adjusted by the regulation factor.
Based on the scheme provided by this embodiment, a strong content competitor in an asymmetric competition environment also needs to acquire traffic according to rules set by the platform, the platform determines the content quality of network information provided by each content provider according to a uniform standard, and predicts a reasonable traffic distribution parameter for the corresponding network information, so that the traffic efficiency of the content provider on the platform is improved, the viscosity of the content provider on the traffic distribution platform can also be improved, and the consistency of the platform target and the content provider target is realized.
Based on the unified inventive concept, an embodiment of the present invention further provides an information traffic distribution apparatus, as shown in fig. 3, the information traffic distribution apparatus provided in the embodiment of the present invention may include:
the information obtaining module 310 is configured to obtain network information provided by a content provider, perform content quality evaluation on the network information, and obtain content evaluation parameters corresponding to the network information;
an information statistics module 320, configured to count audience coverage information of the network information;
the distribution module 330 is configured to predict traffic distribution parameters corresponding to each piece of network information based on the content evaluation parameters and the audience coverage information, and perform traffic distribution on the network information by using the traffic distribution parameters.
In an alternative embodiment of the present invention, as shown in fig. 4, the apparatus may further include:
the incentive module 340 is configured to issue at least one incentive task to each content provider;
and acquiring the completion state of each content provider for the incentive tasks, and summarizing the flow incentive parameters obtained by each content provider for completing the incentive tasks.
In an optional embodiment of the present invention, the allocating module 330 may further be configured to:
and predicting the flow distribution parameters corresponding to the network information by combining the content evaluation parameters corresponding to the network information, the audience coverage information and the flow excitation parameters corresponding to the content providers.
In an optional embodiment of the present invention, the allocating module 330 may further be configured to:
sequencing the network information according to content evaluation parameters corresponding to the network information, and distributing initial flow parameters by combining audience coverage information;
acquiring target flow parameters provided by each content provider aiming at the corresponding network information;
and adjusting the initial flow parameters according to the target flow parameters and the information characteristics corresponding to the network information to obtain flow distribution parameters corresponding to the network information.
In an optional embodiment of the present invention, the allocating module 330 may further be configured to:
in the flow distribution process, acquiring real-time flow information corresponding to each network information; judging whether each network information can reach a corresponding predicted flow value or not based on the real-time flow information;
and when the network information cannot reach the corresponding predicted flow value, generating a regulation factor corresponding to each flow distribution parameter, regulating the flow distribution parameter based on the regulation factor, and carrying out flow distribution on the network information by using the regulated flow distribution parameter.
In an optional embodiment of the present invention, the allocating module 330 may further be configured to:
and identifying information characteristics and current time characteristics corresponding to each network information, and generating a regulation factor corresponding to each flow distribution parameter according to the information characteristics and the time characteristics corresponding to each network information.
In an optional embodiment of the present invention, the information obtaining module 310 may further be configured to:
utilizing a pre-trained content quality evaluation model to evaluate the content quality of the network information to obtain content evaluation parameters corresponding to the network information; for any network information, the content evaluation parameter corresponding to the network information is positively correlated with the conversion efficiency corresponding to the network information.
In an alternative embodiment of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the information traffic distribution method.
In an exemplary embodiment, referring to fig. 5, an embodiment of the present invention further provides a computing device, where the computing device includes a communication bus, a processor, a memory, and a communication interface, and may further include an input/output interface and a display device, where the functional units may complete communication with each other through the bus. The memory stores computer programs, and the processor is used for executing the programs stored in the memory and executing the steps of the information flow distribution method in the embodiment.
It is clear to those skilled in the art that the specific working processes of the above-described systems, devices, modules and units may refer to the corresponding processes in the foregoing method embodiments, and for the sake of brevity, further description is omitted here.
In addition, the functional units in the embodiments of the present invention may be physically independent of each other, two or more functional units may be integrated together, or all the functional units may be integrated in one processing unit. The integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.
Those of ordinary skill in the art will understand that: the integrated functional units, if implemented in software and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computing device (e.g., a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention when the instructions are executed. And the aforementioned storage medium includes: u disk, removable hard disk, Read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disk, and other various media capable of storing program code.
Alternatively, all or part of the steps of implementing the foregoing method embodiments may be implemented by hardware (such as a computing device, e.g., a personal computer, a server, or a network device) associated with program instructions, which may be stored in a computer-readable storage medium, and when the program instructions are executed by a processor of the computing device, the computing device executes all or part of the steps of the method according to the embodiments of the present invention.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments can be modified or some or all of the technical features can be equivalently replaced within the spirit and principle of the present invention; such modifications or substitutions do not depart from the scope of the present invention.
Claims (10)
1. An information traffic distribution method, characterized in that the method comprises:
acquiring network information provided by a content provider, and performing content quality evaluation on the network information to obtain content evaluation parameters corresponding to the network information;
counting audience coverage information of the network information;
and predicting flow distribution parameters corresponding to the network information based on the content evaluation parameters and the audience coverage information, and carrying out flow distribution on the network information by using the flow distribution parameters.
2. The method of claim 1, wherein predicting the traffic distribution parameter for each of the network information based on the content rating parameters and the audience coverage information further comprises:
issuing at least one incentive task to each of the content providers;
and acquiring the completion state of each content provider for the incentive task, and summarizing the flow incentive parameters obtained by each content provider for completing the incentive task.
3. The method of claim 2, wherein predicting the traffic distribution parameter for each of the network information based on the content rating parameters and the audience coverage information comprises:
and predicting the flow distribution parameters corresponding to the network information by combining the content evaluation parameters corresponding to the network information, the audience coverage information and the flow excitation parameters corresponding to the content providers.
4. The method of claim 1, wherein predicting traffic distribution parameters corresponding to each of the network information based on the content rating parameters and the audience coverage information comprises:
sequencing the network information according to content evaluation parameters corresponding to the network information, and distributing initial flow parameters by combining the audience coverage information;
acquiring target flow parameters provided by each content provider aiming at the corresponding network information;
and adjusting the initial flow parameter according to the target flow parameter and the information characteristics corresponding to the network information to obtain a flow distribution parameter corresponding to the network information.
5. The method according to any one of claims 1-4, further comprising:
in the flow distribution process, acquiring real-time flow information corresponding to each network information; judging whether each network information can reach a corresponding predicted flow value or not based on the real-time flow information;
if not, generating a regulation factor corresponding to each flow distribution parameter, adjusting the flow distribution parameters based on the regulation factor, and performing flow distribution on the network information by using the adjusted flow distribution parameters.
6. The method of claim 5, wherein generating a regulatory factor corresponding to each of the flow distribution parameters comprises:
and identifying information characteristics corresponding to the network information and current time characteristics, and generating a regulation factor corresponding to each flow distribution parameter according to the information characteristics corresponding to the network information and the time characteristics.
7. The method according to any one of claims 1 to 4, wherein the performing content quality evaluation on the network information to obtain a content evaluation parameter corresponding to the network information comprises:
utilizing a pre-trained content quality evaluation model to evaluate the content quality of the network information to obtain content evaluation parameters corresponding to the network information; for any one of the network information, the content evaluation parameter corresponding to the network information is positively correlated with the conversion efficiency corresponding to the network information.
8. An information traffic distribution apparatus, characterized in that the apparatus comprises:
the information acquisition module is used for acquiring network information provided by a content provider, and performing content quality evaluation on the network information to obtain content evaluation parameters corresponding to the network information;
the information counting module is used for counting audience coverage information of the network information;
and the distribution module is used for predicting flow distribution parameters corresponding to the network information based on the content evaluation parameters and the audience coverage information and carrying out flow distribution on the network information by using the flow distribution parameters.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium is configured to store a program code for performing the method of any of claims 1-7.
10. A computing device, the computing device comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method of any of claims 1-7 according to instructions in the program code.
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