CN112543346B - Information distribution method and device, computer storage medium and electronic equipment - Google Patents

Information distribution method and device, computer storage medium and electronic equipment Download PDF

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CN112543346B
CN112543346B CN202110061880.3A CN202110061880A CN112543346B CN 112543346 B CN112543346 B CN 112543346B CN 202110061880 A CN202110061880 A CN 202110061880A CN 112543346 B CN112543346 B CN 112543346B
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video
time
published
information
release
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CN112543346A (en
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褚天颖
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Zhejiang Koubei Network Technology Co Ltd
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Zhejiang Koubei Network Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
    • H04N21/234—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs
    • H04N21/23418—Processing of video elementary streams, e.g. splicing of video streams, manipulating MPEG-4 scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
    • H04N21/26208—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints
    • H04N21/26241—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints involving the time of distribution, e.g. the best time of the day for inserting an advertisement or airing a children program
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
    • H04N21/26283—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists for associating distribution time parameters to content, e.g. to generate electronic program guide data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4662—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms
    • H04N21/4666—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms using neural networks, e.g. processing the feedback provided by the user

Abstract

The application discloses an information publishing method and device, a computer storage medium and an electronic device, wherein the method comprises the following steps: acquiring information to be published, wherein the information to be published comprises a video to be published; responding to the time selection prompt information as an activated state, and outputting the release recommendation time of the video to be released on the information release page; in response to the selection of the release recommendation time and the association operation of the video to be released and the entity object, determining the target release time of the video to be released, and associating the video to be released and the entity object; issuing the video to be issued and the associated entity object according to the target issuing time, and displaying the video and the associated entity object on an application service platform of the terminal device, wherein the displaying on the application service platform at least comprises: and the method is used for displaying the page of the application service platform of the user terminal equipment related to the entity object, so that the conversion rate can be improved after the video to be published is published based on the publishing recommendation time.

Description

Information distribution method and device, computer storage medium and electronic equipment
Technical Field
The present application relates to the field of computer technologies, and in particular, to an information publishing method and apparatus. The application also relates to a computer storage medium and an electronic device.
Background
Video has been widely used in various industries as an information recording method. With the development of various application software, short videos have become a very popular information acquisition mode. For the short video publisher, the short video publisher only adopts an instant publishing mode after the short video is produced, namely the short video publisher completes production and immediately publishes the short video.
The distribution mode based on instant distribution can result in the reduction of the conversion rate of the short video.
Disclosure of Invention
The application provides an information issuing method, which aims to solve the problem of low information conversion rate caused by improper information issuing time in the prior art.
The application provides an information issuing method, which comprises the following steps:
acquiring information to be published, wherein the information to be published comprises a video to be published;
responding to the time selection prompt information as an activated state, and outputting the release recommendation time of the video to be released on an information release page, wherein the release recommendation time is automatically determined according to the data characteristics corresponding to the video to be released;
in response to the selection of the release recommending time and the association operation of the video to be released and the entity object, determining the target release time of the video to be released, and associating the video to be released and the entity object, wherein the entity object is provider information capable of providing related resources in the video to be released;
issuing the video to be issued and the associated entity object according to the target issuing time, and displaying the video and the associated entity object on an application service platform of the terminal device, wherein the displaying on the application service platform at least comprises: and the page is used for displaying the page of the application service platform of the user terminal equipment related to the entity object.
In some embodiments, the automatically determining the release recommendation time of the video to be released according to the acquired data characteristics of the video to be released includes:
extracting data characteristics of the video to be published;
inputting the data characteristics into a machine learning model for learning to obtain the release recommendation time corresponding to the video to be released; the machine learning model is a neural network model trained by taking data characteristics of published videos as input parameters.
In some embodiments, further comprising:
extracting content characteristics and release time characteristics which describe the content of the released video and behavior data characteristics which describe operation behaviors aiming at the released video from the data characteristics of the released video; wherein the content features comprise at least one of type features of the video content, industry features of the video content, style features of the video content, and identification features of the video content; the behavior data characteristics comprise operation behavior type characteristics;
and inputting the content characteristics, the release time characteristics and the behavior data characteristics as training parameters into the neural network model for training to obtain the trained neural network model.
In some embodiments, further comprising:
and responding to the activation operation of the time selection prompt information when the time selection prompt information is in the non-activation state, and changing the time selection prompt information into the activation state.
In some embodiments, the responding to the activated state of the time selection prompt message or in the inactivated state of the time selection prompt message further comprises:
providing self-selection prompt information of self-defined release time;
responding to the operation of the self-selection prompt message, and outputting the self-defined release time selection range;
and responding to the selection operation of the user-defined release time in the selection range, and determining the selected user-defined release time as the target release time of the video to be released.
In some embodiments, the publishing the video to be published and the associated entity object according to the target publishing time and for displaying on an application service platform includes:
monitoring the system time of a storage medium where the video to be published is located;
determining whether the system time is the same as the target release time;
and if so, displaying the video to be published and the associated entity object on the application service platform.
In some embodiments, further comprising:
determining whether the position information of the entity object associated with the video to be published meets the requirement of displaying a recommended video to an application service platform of the terminal equipment;
and if so, displaying the video to be issued as the recommended video in a recommendation column of the application service platform.
The application provides an information issuing device, includes:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring information to be published, and the information to be published comprises a video to be published;
the output unit is used for responding to the time selection prompt information as an activated state and outputting the release recommendation time of the video to be released on an information release page, wherein the release recommendation time is automatically determined according to the data characteristics corresponding to the video to be released;
the determining unit is used for responding to the selection of the release recommending time and the association operation of the video to be released and the entity object, determining the target release time of the video to be released, and associating the video to be released and the entity object, wherein the entity object is provider information capable of providing related resources in the video to be released;
a publishing unit, configured to publish the video to be published and the associated entity object according to the target publishing time and to display the video and the associated entity object on an application service platform of a terminal device, where the displaying on the application service platform at least includes: and the page is used for displaying the page of the application service platform of the user terminal equipment related to the entity object.
The application also provides a computing storage medium for storing the data generated by the network platform and a program for processing the data generated by the network platform;
when being read and executed, the program performs the steps of the information distribution method as described.
The present application further provides an electronic device, comprising:
a processor;
a memory for storing a program for processing network platform generated data, said program, when read and executed by said processor, performing the steps of the information distribution method as described.
Compared with the prior art, the method has the following advantages:
according to the information publishing method, when the information to be published is published, the extracted data characteristics of the information to be published are input into the neural network model so as to obtain the publishing recommendation time aiming at the information to be published, and the publishing recommendation time is displayed on a publishing platform. The release recommendation time can be multiple, the selection can be performed according to the multiple displayed release recommendation times, the selected release recommendation time is the target release time, and the information to be released is released and displayed on the release platform according to the target release time. In fact, the publishing platform may not only be a platform generated for the information to be published, but also be another platform, so as to publish the information to be published to a plurality of platforms synchronously. According to the embodiment, the release recommendation time can be obtained according to the data characteristics and the like of the information to be released, so that the conversion rate of the information to be released after release is improved. Because the release recommendation time is obtained by the neural network model after the training based on the related data characteristics of the published video, the conversion rate of the information to be released can be improved according to the related data of the published video, namely, the operation probability of which type of release information is concerned, clicked, browsed or collected at which release time is higher, and the conversion rate can be improved after the information to be released is released based on the release recommendation time. In addition, the issuing recommended time can be determined according to the activation state of the time selection prompt information, the issuing recommended time is determined in the activation state, and the issuing recommended time is issued in other modes in the non-activation state, so that the calculation can be carried out in a self-adaptive mode, and the waste of calculation resources is avoided.
Drawings
FIG. 1 is a flow chart of an embodiment of an information distribution method provided by the present application;
fig. 2 is a schematic view of an application scenario of an embodiment of an information publishing method provided by the present application;
FIG. 3 is a schematic structural diagram of an embodiment of an information distribution apparatus provided in the present application;
fig. 4 is a schematic structural diagram of an embodiment of an electronic device provided in the present application.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of implementation in many different ways than those herein set forth and of similar import by those skilled in the art without departing from the spirit of this application and is therefore not limited to the specific implementations disclosed below.
The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The description used in this application and in the appended claims is for example: the terms "a," "an," "first," and "second," etc., are not intended to be limiting in number or order, but rather are used to distinguish one type of information from another.
In the context of the above background, it is known that in the application scenario of short video distribution. The issue of low short video conversion is caused because the short video is distributed as a single instant distribution. For example: the number of people watching on line at the current time of the instant publishing time is small, and the publishing of a large number of short videos can extrude the short videos published before to the bottom layer, so that the problem of low conversion rate can be caused; or the viewer's interest level of the distributed short video content is low at the current distribution time, which also causes a problem of low conversion rate. Based on the above thought, the application contemplates a method for providing a distribution time suitable for distribution of short videos according to information such as the distribution time and content of the short videos, so as to improve the conversion rate of the short videos.
The above is the description of the original idea of the technical solution of the present application, and the application scenario is not limited to the information distribution of such short videos, and any application scenario with production and distribution requirements is applicable. The following describes an embodiment of an information distribution method provided by the present application in detail.
Referring to fig. 1, fig. 1 is a flowchart illustrating an embodiment of an information distribution method according to the present application. The embodiment of the method comprises the following steps:
step S101: acquiring information to be published, wherein the information to be published comprises a video to be published;
the information to be published in step S101 may be audio and video information to be published, and the audio and video information may be short audio and video information or may also be referred to as short video. The short video is a video in short film, which is an internet content transmission mode, and is a video which is generally transmitted on a new internet medium within 5 minutes; with the popularization of mobile terminals and the acceleration of networks, content is spread on large platforms in short and fast large flow and is in the dominant position of information distribution. Of course, the information to be published may also be other information that can be displayed through the network platform, and is not limited to the form of video.
In this embodiment, the video to be published may be a short video related to food, such as: the short video may be a short video covering commodity information, such as a short video for food processing, and a short video for food introduction.
The video to be published can be produced on the terminal equipment of the video to be published, and can also be a received video produced by other terminal equipment, and the specific obtaining mode is not limited, and the source is not limited. The video to be published can be made through a video making application or can be made through a making function of a third-party application service platform.
Step S102: responding to the time selection prompt information as an activated state, and outputting the release recommendation time of the video to be released on an information release page, wherein the release recommendation time is automatically determined according to the data characteristics corresponding to the video to be released;
the release recommending time may be a time when the information to be released is displayed and output through a network platform. Of course, different information to be published may have one or more different publication recommendation times. The publishing recommendation time in this embodiment may be a reference time determined based on the content of the video to be published and capable of increasing a conversion rate of the information to be published, where the conversion rate may be an operation behavior of the information to be published, for example: and performing behavior operations such as comment, forwarding, browsing, sharing and the like.
In this embodiment, time selection prompt information is provided, and in a state where the time selection prompt information is activated, calculation processing for issuing recommended time of a current video to be issued is performed, so that corresponding calculation can be completed according to a demand for issuing recommended time. When the time selection prompt message is in an inactive state, the calculation processing of issuing the recommended time is not needed, so that the waste of calculation resources is avoided, and the calculation cost is increased without any reason. The activation status of the time selection prompt is described in detail in the following step S103, which is only an overview and can be referred to the following description.
In this embodiment, the determining of the release recommendation time may include:
step S102-1: extracting data characteristics of the video to be published;
step S102-2: inputting the data characteristics into a machine learning model for learning to obtain the release recommendation time corresponding to the video to be released; the machine learning model is a neural network model trained by taking data characteristics of published videos as input parameters.
In this embodiment, the neural network model may be selected according to different types of videos to be published, for example: when the video to be released is image information, a Convolutional Neural Network (CNN) can be adopted; when the video to be released is video information, a Recurrent Neural Network (RNN) can be adopted; of course, hybrid neural network models may also be employed, and there is no particular limitation as to which type of neural network model is specifically employed.
The step S102-1 is to extract content features describing the content of the video to be distributed from the data features of the video to be distributed;
in this embodiment, the content feature of the video content to be distributed in the step S102-1 may include: the video publishing method comprises at least one of content type characteristics of the video to be published, industry type characteristics of the video to be published, duration or length characteristics of the video to be published, style characteristics of the video to be published, content identification characteristics and the like. The content identification feature may be an information feature added to the video to be published for highlighting, for example: and identification information of at least one of text and graphics added to the short video.
In this embodiment, the publishing recommendation time in the step 102-2 may be learned by inputting the data characteristics of the video to be published into a neural network model, so as to obtain the publishing recommendation time for the video to be published. And the neural network model may be a model obtained after a published video training, that is: through model training of data features in published videos, corresponding publishing recommendation time capable of being used for outputting videos to be published is obtained.
Therefore, the present embodiment may further include:
extracting content characteristics and release time characteristics which describe the content of the released video and behavior data characteristics which describe operation behaviors aiming at the released video from the data characteristics of the released video;
and inputting the content characteristics, the release time characteristics and the behavior data characteristics as the input parameters into the neural network model for training to obtain the trained neural network model.
Both the video to be distributed and the distributed video (historical distribution information) have data characteristics. The data characteristics of the published video are adopted during training of the neural network model, can adopt a plurality of different dimensional characteristic combinations, and can comprise publishing time characteristics, operation behavior characteristics aiming at the published video and content characteristics of published video content; wherein the content feature of the published video content may be at least one of a type feature of the video content, an industry feature of the video content, a style feature of the video content, a duration or length feature of the video content, an identification feature of the video content, and the like. That is, the selection of the content features of the published video and the selection of the content features of the video to be published may be the same or similar. The operation behavior characteristics may also include operation behavior type characteristics, and the like.
In terms of specific application scenarios, that is, when the published video is a short video, the content type may be that the content features described by the short video are food, living goods (of course, type subdivision may also be continued), sports goods, or travel, and the like; the industry type can be that the content of the short video description is catering industry, tourism industry, education industry, movie industry and the like; the style characteristics can be picture tone characteristics, background music characteristics, picture light effect characteristics, adopted character characteristics and the like adopted by the short video; the length may be the duration of a short video, etc.
In this embodiment, model training may be performed on the published video through multiple feature selection dimensions. It should be noted that the published video may be from information published by different publishers or the same publisher on the same platform, or may be from information published by different publishers or the same publisher on different platforms, that is, the obtaining range and form of the historical publishing information are not limited.
The extracted data features of the published video are input into a neural network model for training, and classification of the publishing time, content and conversion rate of the published short video is obtained, wherein the conversion rate can understand the operation behaviors of the published short video, such as: and at least one of browsing, clicking, forwarding, downloading, storing, paying attention to and the like. The operation behavior characteristics may also include operation behavior type sub-characteristics, such as a concerned group gender sub-characteristic, a concerned group age sub-characteristic, and the like, as described above, but may also be sub-characteristics of other operation behaviors. For example: the short video A is a food category, the release time comprises 15:00, 8:00, 18:00 and the like, the conversion rate corresponding to each release time is 30%, 10% and 50%, and then the trained neural network model classifies the short video similar to the short video A into a category with the release recommendation time of 18: 00.
After the training of the neural network model is completed, the data characteristics of the video to be published, such as at least one of the content type characteristics of the video to be published, the industry type characteristics of the video to be published, the duration or length characteristics of the video to be published, the style characteristics of the video to be published, and the content identification characteristics, can be extracted and input into the neural network model to obtain the publishing recommendation time for the video to be published.
In order to further ensure or improve the conversion rate of the video to be published, and also improve the accuracy of the publication recommendation time through the weight, the embodiment may include the following two implementation manners:
the first method is as follows:
and adding weight information during model training to improve the accuracy of the issuing recommendation time. For example: and taking the conversion ratio of the conversion rate of the published video as the weight, outputting the publishing time corresponding to the publishing information of different types, and further, publishing the video to be published according to the obtained publishing recommendation time to obtain higher conversion rate. The conversion ratio may be the number of operation behaviors for the published video, and the number of operation behaviors may be a statistical number of all operation behaviors for the published video, or may be a statistical number of operation behaviors of a certain class, for example: number of forwards, number of clicks, number of comments, etc.
The second method is as follows:
determining the type of the video to be published, determining the weight of the video to be published publishing recommendation time according to the operation behavior number of the type (the conversion ratio of the conversion rate of the published video can be used as the weight), and determining the final publishing recommendation time according to the weight and the publishing recommendation time.
The two modes are different in that the first mode determines and outputs the final publishing recommendation time determined according to the weight and other information in the training process of the neural network model, and the second mode determines the final publishing push time according to the determined weight after the publishing push time of the video to be published is output through the neural network model. Therefore, the determined publishing time of the video to be published is more accurate.
Following the above example of the short video a, when the short video a has the same conversion rate, for example: the short video A is a food category, the distribution time comprises 15:00, 8:00, 18:00 and the like, the conversion rate corresponding to each distribution time is 50%, 10% and 50%, and the distribution time of the short video A can be classified according to the behavior operation number corresponding to the conversion rate of the distribution time 15:00 and the conversion rate of the distribution time 18: 00. If the number of forwarding or attention occurrences of 18:00 is greater than the 15:00, 18:00 is given a greater weight so that the model can better output classification information.
The above is merely an example of the weight, and is not limited to the above. Certainly, the determined issuing recommendation time can be multiple, a range is selected according to different issuing times of the issued short videos, and the issuing time in the range can be used as a reference for outputting the issuing recommendation time of the short videos to be issued subsequently.
It can be understood that the data feature of the video to be published can also be automatically determined in combination with the industry to which the video belongs, for example, the industry feature corresponding to the video to be published can be automatically determined according to the industry to which a publisher (such as a merchant publishing the video) belongs, further, for example, can be automatically determined according to store information associated with the publisher, further, for example, can be automatically determined according to marking of the publisher, further, for example, can be determined according to image recognition performed after the video is intercepted, for example, the data feature of the video to be published is determined, for example: the image recognition result is barbecue appliance, barbecue food, etc., so the industry characteristic is barbecue.
Step S103: in response to the selection of the release recommending time and the association operation of the video to be released and the entity object, determining the target release time of the video to be released, and associating the video to be released and the entity object, wherein the entity object is provider information capable of providing related resources in the video to be released;
the purpose of said step S103 is to determine a target publication time.
In the step S103, the target publishing time of the video to be published may be determined according to the publishing recommendation time obtained in the step S102 and the association operation between the video to be published and the entity object. The specific implementation can be that a selectable publishing recommendation time and a selectable entity object are displayed on a publishing processing page of the video to be published. The entity object may be provider information capable of providing the related resource in the video to be published. For example: store or store information. The entity object can be determined according to the geographical position, namely, the peripheral shop information is determined according to the current position information of the terminal equipment for selection.
The entity object can also determine the entity object capable of being associated with the video to be published by setting the entity object in a specific position range and at least one or more combinations of the entity object which is overlapped with the business category according to the preference of the video viewer (the video to be published can be distributed and pushed to the corresponding video viewer).
It should be noted that the publisher of the video to be published may be a shop or a customer. In this embodiment, the publisher is not particularly limited.
After the entity object is selected for the video to be published, a service link between the video to be published and the entity object can be established, after the video to be published is published, the starting of the service link can be triggered through the operation of commodity prompt information in the video, and then the video is skipped to a resource service provider providing page of the video providing commodities for detailed viewing or purchasing.
In the embodiment, the target release time is determined, and meanwhile, the corresponding or associated store can be determined through selection of the information of the commodity provider related to the video to be released, so that the reading amount of the video to be released can be increased, and the efficiency of acquiring audience group information can be improved.
As shown in fig. 2, fig. 2 is a schematic view of an application scenario of an embodiment of an information publishing method provided by the present application. In this embodiment, the output of the release recommendation time may adopt a release interface displayed on the to-be-released video release platform, specifically, for example, a short video, and when the short video recording process is completed and enters the release interface, the release recommendation time is displayed on the release interface. Or the release recommendation time can be displayed in a form of popup after the short video recording process is finished.
As can be seen from the description in step S103, in this embodiment, the release recommendation time may include a plurality of times. The presentation of the release recommendation time can be presented in a sequencing manner according to the time sequence, and the sequencing can be in an ascending or descending manner. And displaying related description information aiming at different release recommendation time, wherein the description information can comprise at least one of probability value of at least one of operation behaviors of clicking, forwarding, downloading and the like of the short video according to the corresponding release recommendation time, the number of released videos similar to the short video to be released, and data of operation behaviors of clicking, forwarding, downloading and the like of the released videos similar to the short video to be released in the corresponding release recommendation time. And then the time selection is used for reference when the recommendation time selection is issued. In other words, a recommendation statement for each publication recommendation time.
And marking preferred recommendation corresponding to the release recommendation time or setting the preferred recommendation time in the release recommendation time area in order to strengthen the display of the release recommendation time on a release interface. Marking or topping may be a more likely generation of operational behavior based on information published at the time of publication recommendation. Marking can be embodied in a grade mode of highlighting the recommended issuing time, for example, the recommended grade is high, and can be marked as star grade, and besides marking, the recommended issuing time can also be displayed in different font colors or font sizes to highlight the recommended grade, for example: color deep indicates a high recommendation level, or font large indicates a high recommendation level.
It can be understood that when the publication interface displays the publication recommendation time, a custom publication time can also be displayed, and the custom publication time can be understood as a time sorted according to a natural time.
It should be noted that, in addition to instant sending, when there are multiple selectable types of distribution time, the video to be distributed may be distributed according to the set time by triggering the time selection prompt message, the distribution recommended time or the custom distribution time. Therefore, in this embodiment, a set time selection prompt message for the set distribution time may also be provided, which will be described in detail later and only be summarized here.
And the dynamic time of the distance between the release recommendation time and the current time for playing can be displayed.
Entity object information related to the video to be published can also be displayed, for example: when the video to be published is a restaurant short video, merchant information related to the restaurant can be displayed, and the method comprises the following steps: the service information of the short video playing terminal comprises at least one of the name of a merchant, the information of food, the off-line position information of the merchant, the preferential information of the merchant, the on-line service information of the merchant and the like, wherein the position information of the store can be determined according to the position information of the short video playing terminal. When a plurality of food items are involved in the catering short video and come from different merchants, the information of the merchants can be displayed. The operation of providing the entering prompt information entering the merchant in the online service information of the merchant can enter an interface providing the service of the merchant to select and purchase the food in the short video, and of course, the operation of entering the prompt information can also directly enter the service interface of the food in the short video, namely, a service link.
The above is only the description of the publishing recommendation time in step S103, and it can be understood that information related to the publishing recommendation time may also be output on the publishing interface that outputs the publishing recommendation time, and other information related to the video to be published may also be output.
It is to be understood that the presented publication recommendation time may include: date and time.
In step S103, according to the release recommendation time, determining that the target release time of the video to be released may be a selection operation of the release recommendation time, determining the target release time, and when selecting the release recommendation time, referring to an association operation of the video to be released and an entity object, that is, selecting the entity object and selecting the release recommendation time are performed sequentially, and may not be performed sequentially before and after the selection operation.
In this embodiment, the output of the release recommendation time may be output when the time selection prompt information is in an activated state; and determining the target release time of the video to be released according to the selection range provided by the output release recommendation time.
The time selection prompt information can be a control displayed on the publishing interface, the control can be in an activated or inactivated state through operation, and the publishing recommendation time is output in the activated state, namely the machine learning model is triggered to calculate the publishing recommendation time in the activated state. So that the target publication time can be determined by selecting the provided publication recommendation time.
As can be seen from the above description in step S103, the output publication recommended time may include a plurality of publication recommended times, each publication recommended time is different, and a selection may be made within the output publication recommended time selection range to determine the target publication time.
When the mobile terminal is in the inactive state, the mobile terminal can issue according to the implementation of instant issue; or, performing activation operation on the release recommendation time, and then selecting the release recommendation time.
In other embodiments, the target release time may also be determined by selecting a custom set time in an active state or an inactive state. That is, when the activation state of the time selection prompting information for determining the release recommendation time is no matter yes, no, the following may be further included:
providing self-selection prompt information of self-defined release time;
responding to the operation of the self-selection prompt message, and outputting the self-defined release time selection range;
and responding to the selection operation of the user-defined release time in the selection range, and determining the selected user-defined release time as the target release time of the video to be released.
The custom release time may also include a plurality of time selection ranges.
Step S104: issuing the video to be issued and the associated entity object according to the target issuing time, and displaying the video and the associated entity object on an application service platform of the terminal device, wherein the displaying on the application service platform at least comprises: the page of the application service platform of the user terminal equipment related to the entity object is used for displaying;
the step S104 is to display the video to be published according to the target publishing time. Namely, if the target publishing time is selected, the video to be published is published when the target publishing time is reached.
The specific implementation process of step S104 may include:
step S104-1: monitoring the system time of a storage medium where the video to be published is located;
step S104-2: determining whether the system time is the same as the target release time;
step S104-3: and if so, releasing the video to be released.
The system time in step S104-1 may be a time of a system in which the medium storing the video to be distributed is located. The system time here may be the system time of the terminal device or the system time of the server.
And if the system time is not the same as the target release time, continuing to monitor the system time.
The specific implementation process of the step S104-3 may include two ways, one way includes:
step S104-31: distributing the video to be published to a preset application service platform;
step S104-32: and the application service platform displays the video to be published.
In the step S104-31, the video to be published may be published to a plurality of application service platforms.
The second mode comprises the following steps:
step S104-33: and displaying the video to be published generated by the application service platform on the application service platform.
The difference between the first mode and the second mode can be understood as that the first mode is as follows: the video to be published can be generated through an application service platform a or obtained through other channels, or can also be understood as being stored on a server, and the video to be published can be distributed to application service platforms needing to be published, wherein the number of the application service platforms can be multiple and is preset according to the requirements of publishers. The second mode is as follows: the video to be published is generated through the platform A and published on the application service platform A, and when the target publishing time is reached, the application service platform A publishes the video to be published.
In order to further implement accurate publishing of the video to be published, the method may further include:
determining whether the position information of the entity object associated with the video to be published meets the requirement of displaying a recommended video to an application service platform of the terminal equipment; and if so, displaying the video to be issued as the recommended video in a recommendation column of the application service platform. That is to say, whether the distance between the entity object and the terminal device meets the distance requirement or not can be judged, so that the video to be published can be displayed. Therefore, the dimension of the video to be published can be reduced or converged through the position dimension, and the problem that the video to be published is cross-regional during publishing is avoided.
The above is a specific description of an embodiment of an information publishing method according to the present application, and in combination with a specific application scenario, when a short video to be published is published, data features of the short video need to be extracted first and input into a neural network model to obtain a publishing recommendation time for the short video, and the publishing recommendation time is displayed on a publishing platform. The release recommendation time can be multiple, the release recommendation times can be selected according to the multiple displayed release recommendation times, the selected release recommendation time is the target release time, and when the monitored system time reaches the target sending time, the short video is released on the release platform. In fact, the publishing platform may not only be a platform that generates short videos, but may also be other platforms to publish short videos to multiple platforms simultaneously. According to the embodiment, the distribution recommendation time can be obtained according to the data characteristics and the like of the short video, so that the conversion rate of the short video after distribution is improved. Because the release recommendation time is obtained by the neural network model after training based on the related data features of the short videos which are released in the past, the conversion rate of the short videos to be released can be improved according to the related data of the short videos which are released, namely, the operation probability of which type of short videos are concerned, clicked, browsed or collected at which release time is higher, and the conversion rate can be further improved according to the release recommendation time.
The above is a detailed description of an embodiment of an information distribution method provided in the present application, and corresponds to the aforementioned embodiment of an information distribution method, and the present application also discloses an embodiment of an information distribution apparatus, please refer to fig. 3, since the apparatus embodiment is basically similar to the method embodiment, the description is relatively simple, and related points can be referred to partial description of the method embodiment. The device embodiments described below are merely illustrative.
As shown in fig. 3, fig. 3 is a schematic structural diagram of an example of an information distribution apparatus provided in the present application, where the apparatus includes:
an obtaining unit 301, configured to obtain information to be published, where the information to be published includes a video to be published;
for specific content of the obtaining unit 301, reference is made to the description of step S101, which is only a summary description here and is not repeated.
The output unit 302 is configured to output, in response to the time selection prompt information being in an activated state, the release recommendation time of the video to be released on the information release page, where the release recommendation time is automatically determined according to the data characteristics corresponding to the video to be released;
the output unit 302 is mainly configured to automatically determine the release recommendation time and output and display the release recommendation time.
The output unit 302 may include: a state operation subunit and a determination subunit.
The state operation subunit is configured to operate the time selection prompt information of the release recommendation time to enable the time selection prompt information to be in an activated state;
and the determining subunit is configured to determine the release recommendation time of the video to be released when the time selection prompt information is in an activated state.
The output unit 302 outputs the determined distribution recommended time to the information distribution page.
The determining the sub-unit may include: an extraction subunit and an input subunit;
the extraction subunit is used for extracting the data characteristics of the video to be published;
the input subunit is configured to input the data features extracted in the extraction subunit to a machine learning model for learning, and obtain the release recommendation time corresponding to the video to be released; the machine learning model is a neural network model trained by taking data characteristics of published videos as input parameters.
The method can also comprise the following steps: the device comprises a historical data extraction unit and a training unit, wherein the historical data extraction unit is used for extracting content characteristics and release time characteristics which describe the content of released videos and behavior data characteristics which describe operation behaviors aiming at the released videos from the data characteristics of the released videos; wherein the content features comprise at least one of type features of the video content, industry features of the video content, style features of the video content, and identification features of the video content; the behavior data characteristics comprise operation behavior type characteristics; and the training unit is used for inputting the content characteristics, the release time characteristics and the behavior data characteristics extracted by the historical data extraction unit into the neural network model as training parameters for training to obtain the trained neural network model.
The time selection prompt information can be a control displayed on the publishing interface, the control can be in an activated or inactivated state through operation, and the publishing recommendation time is output in the activated state, namely the machine learning model is triggered to calculate the publishing recommendation time in the activated state.
For specific content of the output unit 302, reference may be made to the description of step S102, and details are not repeated here.
A determining unit 303, configured to determine, in response to selection of the release recommendation time and an association operation between the video to be released and an entity object, a target release time of the video to be released, and associate the video to be released and the entity object, where the entity object is provider information capable of providing related resources in the video to be released;
the determining unit 303 selects, according to the release recommended time output by the output unit 302, an output release recommended time in a state where the time selection prompting information is activated, or an output custom time in a state where the time selection prompting information is inactivated.
For the specific implementation process of the determining unit 303, reference may be made to the content of step S103, which is not described herein again.
It should be noted here that, when in the inactive state, the distribution can be performed according to the implementation of the instant distribution; or, performing activation operation on the release recommendation time, and then selecting the release recommendation time.
In other embodiments, providing a custom time in either the active or inactive state may also be included. That is, when the activation state of the time selection prompting information for determining the release recommendation time is no matter yes, no, the following may be further included:
the user-defined time providing unit is used for providing self-selected prompt information of user-defined release time;
the user-defined time output unit is used for responding to the operation of the self-defined prompt message and outputting the user-defined release time selection range;
and responding to the selection operation of the user-defined release time in the selection range, and determining the selected user-defined release time as the target release time of the video to be released.
The custom release time may also include a plurality of time selection ranges.
A publishing unit 304, configured to publish the video to be published and the associated entity object according to the target publishing time and to display the video and the associated entity object on an application service platform of a terminal device, where the displaying on the application service platform at least includes: and the page is used for displaying the page of the application service platform of the user terminal equipment related to the entity object.
The publishing unit 304 is intended to publish the video to be published when the target publishing time is reached.
The issue unit 304 may include: a monitoring subunit and a determining subunit, and a publishing subunit.
The monitoring subunit is used for monitoring the system time of the storage medium where the video to be issued is located;
the determining subunit is configured to determine whether the system time is the same as the target release time;
and the issuing subunit is configured to issue the video to be issued according to the fact that the determination result of the determining subunit is yes.
The issue subunit may include two ways, one way includes: a distribution subunit and a presentation subunit;
the distribution subunit is configured to distribute the video to be published to a preset application service platform;
and the display subunit is used for displaying the video to be published by the application service platform.
The second mode comprises the following steps: and the display subunit is used for displaying the video to be published generated by the application service platform on the application service platform.
In order to further implement accurate publishing of the video to be published, the method may further include:
the recommendation determining unit is used for determining whether the position information of the entity object associated with the video to be published meets the requirement of displaying the recommended video on an application service platform of the terminal equipment; and the recommending unit is used for displaying the video to be issued as the recommended video on a recommending column of the application service platform if the determination result of the recommending determining unit is positive. That is to say, whether the distance between the entity object and the terminal device meets the distance requirement or not can be judged, so that the video to be published can be displayed. Therefore, the dimension reduction and convergence of the video to be published can be carried out through the position dimension, and the problem that the video to be published is across regions in the publishing process is avoided.
For a specific implementation process of the issuing unit 304, reference may be made to the content of the step S104, and details are not repeated here.
Based on the above, the present application further provides a computing storage medium for storing data generated by a network platform and a program for processing the data generated by the network platform;
the program, when read and executed, performs the steps of the information distribution method as described above.
Based on the above, as shown in fig. 4, the present application further provides an electronic device, including:
a processor 401;
a memory 402 for storing a program for processing network platform generated data, which when read and executed by the processor performs the steps of the information distribution method as described above.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
1. Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
2. As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application, therefore, the scope of the present application should be determined by the claims that follow.

Claims (10)

1. An information distribution method, comprising:
acquiring information to be published, wherein the information to be published comprises a video to be published;
responding to the time selection prompt information as an activated state, and outputting the release recommendation time of the video to be released on an information release page, wherein the release recommendation time is automatically determined according to the data characteristics corresponding to the video to be released;
in response to the selection of the release recommending time and the association operation of the video to be released and the entity object, determining the target release time of the video to be released, and associating the video to be released and the entity object, wherein the entity object is provider information capable of providing related resources in the video to be released;
issuing the video to be issued and the associated entity object according to the target issuing time, and displaying the video and the associated entity object on an application service platform of the terminal device, wherein the displaying on the application service platform at least comprises: and the page is used for displaying the page of the application service platform of the user terminal equipment related to the entity object.
2. The information distribution method according to claim 1, wherein the distribution recommendation time is automatically determined according to the data characteristics corresponding to the video to be distributed, and the method comprises the following steps:
extracting data characteristics of the video to be published;
inputting the data characteristics into a machine learning model for learning to obtain the release recommendation time corresponding to the video to be released; the machine learning model is a neural network model trained by taking data characteristics of published videos as input parameters.
3. The information distribution method according to claim 2, further comprising:
extracting content characteristics and release time characteristics which describe the content of the released video and behavior data characteristics which describe operation behaviors aiming at the released video from the data characteristics of the released video; wherein the content features comprise at least one of type features of the video content, industry features of the video content, style features of the video content, and identification features of the video content; the behavior data characteristics comprise operation behavior type characteristics;
and inputting the content characteristics, the release time characteristics and the behavior data characteristics as training parameters into the neural network model for training to obtain the trained neural network model.
4. The information distribution method according to claim 1, further comprising:
and responding to the activation operation of the time selection prompt information when the time selection prompt information is in the non-activation state, and changing the time selection prompt information into the activation state.
5. The information distribution method according to claim 1 or 4, further comprising:
providing self-selection prompt information of self-defined release time;
responding to the operation of the self-selection prompt message, and outputting the self-defined release time selection range;
and responding to the selection operation of the user-defined release time in the user-defined release time selection range, and determining the selected user-defined release time as the target release time of the video to be released.
6. The information distribution method according to claim 1, wherein the distributing the video to be distributed and the associated entity object according to the target distribution time and for displaying on an application service platform of a terminal device includes:
monitoring the system time of a storage medium where the video to be published is located;
determining whether the system time is the same as the target release time;
and if so, displaying the video to be published and the associated entity object on the application service platform.
7. The information distribution method according to claim 6, further comprising:
determining whether the position information of the entity object associated with the video to be published meets the requirement of displaying a recommended video to an application service platform of the terminal equipment;
and if so, displaying the video to be issued as the recommended video in a recommendation column of the application service platform.
8. An information distribution apparatus, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring information to be published, and the information to be published comprises a video to be published;
the output unit is used for responding to the time selection prompt information as an activated state and outputting the release recommendation time of the video to be released on an information release page, wherein the release recommendation time is automatically determined according to the data characteristics corresponding to the video to be released;
the determining unit is used for responding to the selection of the release recommending time and the association operation of the video to be released and the entity object, determining the target release time of the video to be released, and associating the video to be released and the entity object, wherein the entity object is provider information capable of providing related resources in the video to be released;
a publishing unit, configured to publish the video to be published and the associated entity object according to the target publishing time and to display the video and the associated entity object on an application service platform of a terminal device, where the displaying on the application service platform at least includes: and the page is used for displaying the page of the application service platform of the user terminal equipment related to the entity object.
9. A computer storage medium for storing data generated by a network platform and a program for processing the data generated by the network platform;
the program, when read and executed by a processor, performs the steps of the information distribution method of any one of claims 1 to 7.
10. An electronic device, comprising:
a processor;
a memory for storing a program for processing network platform generated data, which when read and executed by the processor, performs the steps of the information distribution method of any one of claims 1 to 7.
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