CN112235636A - Method and device for calculating browsing value attribute of video with goods - Google Patents

Method and device for calculating browsing value attribute of video with goods Download PDF

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Publication number
CN112235636A
CN112235636A CN202010928029.1A CN202010928029A CN112235636A CN 112235636 A CN112235636 A CN 112235636A CN 202010928029 A CN202010928029 A CN 202010928029A CN 112235636 A CN112235636 A CN 112235636A
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goods
video
time period
target
historical
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CN112235636B (en
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孔晓晴
李百川
劳晓敏
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Youmi Technology Co ltd
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Youmi 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]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/44204—Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0623—Item investigation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0641—Shopping interfaces
    • G06Q30/0643—Graphical representation of items or shoppers
    • 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
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/44213—Monitoring of end-user related data
    • H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
    • 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
    • H04N21/47—End-user applications
    • H04N21/475—End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data
    • H04N21/4756—End-user interface for inputting end-user data, e.g. personal identification number [PIN], preference data for rating content, e.g. scoring a recommended movie
    • 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]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
    • H04N21/845—Structuring of content, e.g. decomposing content into time segments
    • H04N21/8456—Structuring of content, e.g. decomposing content into time segments by decomposing the content in the time domain, e.g. in time segments

Abstract

The invention discloses a method and a device for calculating browsing value attributes of videos with goods, wherein the method comprises the following steps: acquiring historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in a target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data; calculating a target relation coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical commodity browsing data of target commodities in a historical time period; and calculating the browsing value attribute of each video with goods to the target commodity in the target time period according to the target relation coefficient corresponding to each video with goods and the current commodity browsing data of the target commodity in the target time period. Therefore, the method and the device for determining the browsing value attribute of the video with goods can provide a determination mode of the browsing value attribute of the video with goods so as to accurately and quickly determine the browsing value attribute of each video with goods for selling goods with goods.

Description

Method and device for calculating browsing value attribute of video with goods
Technical Field
The invention relates to the technical field of internet, in particular to a method and a device for calculating a browsing value attribute of a video with goods.
Background
With the rapid development of the internet, more and more internet users are provided. In order to expand the audience scope and influence of the goods, the marketing mode of the goods introduces video marketing based on the internet besides the traditional advertisement marketing, for example: the advertiser may select multiple video bloggers to publish videos for a certain item or items, which may also be referred to as in-stock videos.
In practical applications, a plurality of videos released by a plurality of video bloggers exist for the same commodity, the styles of videos released by different video bloggers are various, the browsing amount of videos released by each video blogger for commodities is different, and the browsing amount of videos for commodities influences the contribution of videos to the commodity sales to a certain extent. In order to gradually improve the cost performance of commodity video marketing, videos with higher contribution to commodity browsing need to be determined from a plurality of videos of a plurality of video bloggers. Therefore, how to accurately determine the browsing value attribute of each video to the commodity is very important.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method and a device for calculating the browsing value attribute of the videos with goods, which can provide a mode for determining the browsing value attribute of the goods so as to accurately determine the browsing value attribute of each video with goods for selling the goods with goods to the goods.
In order to solve the technical problem, the invention discloses a method for calculating the browsing value attribute of a video with goods in a first aspect, which comprises the following steps:
acquiring historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in a target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data;
calculating a target relation coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
and calculating the browsing value attribute of each video with goods to the target goods in the target time period according to the target relation coefficient corresponding to each video with goods and the current goods browsing data of the target goods in the target time period, wherein the browsing value attribute of the video with goods to the target goods in the target time period is used for representing the contribution condition of the video with goods to the browsing amount of the target goods in the target time period.
As an optional implementation manner, in the first aspect of the present invention, the calculating a target relationship coefficient corresponding to each taken video according to historical interaction data corresponding to each taken video and historical product browsing data of the target product in the historical time period includes:
calculating a stable relation coefficient corresponding to each video with goods in the historical time period according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
and for any one of the videos with goods, determining a stable relation coefficient corresponding to the video with goods in the historical time period as a target relation coefficient corresponding to the video with goods.
As an optional implementation manner, in the first aspect of the present invention, before determining, as the target relationship coefficient corresponding to any of the videos with goods, a stable relationship coefficient corresponding to the video with goods in the historical time period, the method further includes:
calculating a corresponding current relation coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period;
calculating a coefficient difference value between a current relation coefficient corresponding to each video with goods in the target time period and a stable relation coefficient corresponding to the video with goods in the historical time period;
for any one taken video, judging whether the absolute value of the coefficient difference value corresponding to the taken video is smaller than or equal to a predetermined difference threshold value, and if so, triggering and executing the operation of determining the stable relation coefficient corresponding to the taken video in the historical time period as the target relation coefficient corresponding to the taken video; and if not, correcting the current relation coefficient corresponding to the video with the goods according to the stable relation coefficient corresponding to the video with the goods to obtain the target relation coefficient corresponding to the video with the goods.
As an optional implementation manner, in the first aspect of the present invention, the calculating a target relationship coefficient corresponding to each taken video according to historical interaction data corresponding to each taken video and historical product browsing data of the target product in the historical time period includes:
calculating a stable relation coefficient corresponding to each video with goods in the historical time period according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
calculating a corresponding current relation coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period;
and for any one of the video with goods, correcting the corresponding current relation coefficient of the video with goods in the target time period according to the corresponding stable relation coefficient of the video with goods in the historical time period to obtain the target relation coefficient corresponding to each video with goods.
As an optional implementation manner, in the first aspect of the present invention, the calculating, according to the historical interaction data corresponding to each taken-good video and the historical product browsing data of the target product in the historical time period, a stable relationship coefficient corresponding to each taken-good video in the historical time period includes:
acquiring historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
constructing an independent equation corresponding to each historical time period according to the historical interaction data increment of each video with goods in each historical time period and the historical goods browsing data increment of the target goods in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relationship coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relationship between the historical interaction data increment of all the videos with goods and the historical goods browsing data increment of the target goods in the historical time period;
and establishing an equation set by using the independent equations corresponding to all the historical time periods, and calculating the solution of the equation set according to an iterative method to obtain a corresponding stable relation coefficient of each video with goods in the historical time period.
As an optional implementation manner, in the first aspect of the present invention, the calculating a solution of the equation system according to an iterative method to obtain a corresponding stable relationship coefficient of each of the videos with goods in the historical time period includes:
continuously calculating a solution to the system of equations according to an iterative method;
judging whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the precision condition, the newly calculated solution of the equation set is respectively determined as a stable relation coefficient corresponding to each video with goods in the historical time period;
and triggering and executing the operation of continuously calculating the solution of the equation set according to an iterative method when judging that the newly calculated solution of the equation set does not meet the precision condition.
As an optional implementation manner, in the first aspect of the present invention, before the calculating, according to the current interaction data of each of the videos with goods in the target time period and the current product browsing data of the target product in the target time period, a corresponding current relationship coefficient of each of the videos with goods in the target time period, the method further includes:
judging whether the goods-carrying videos with incomplete interactive data exist in all the goods-carrying videos or not according to the acquired current interactive data of each goods-carrying video in the target time period;
when judging that no pickup video with incomplete interactive data exists in all the pickup videos, triggering and executing the operation of calculating the corresponding current relation coefficient of each pickup video in the target time according to the current interactive data of each pickup video in the target time period and the current commodity browsing data of the target commodity in the target time period;
when the fact that the video with the incomplete interactive data exists in all the videos with the goods is judged, performing data supplement operation on the video with the incomplete interactive data in all the videos with the goods, and triggering and executing the operation of calculating a corresponding current relation coefficient of each video with the goods in the target time according to the current interactive data of each video with the goods in the target time period and the current goods browsing data of the target goods in the target time period;
wherein the performing data supplement operations on the shipped videos with incomplete interactive data in all the shipped videos comprises:
counting the number of the loaded videos with incomplete interactive data in all the loaded videos, and acquiring an interactive data supplement algorithm matched with the number;
and according to the acquired interactive data supplement algorithm, performing data supplement operation on the loaded video with incomplete interactive data in all the loaded videos.
The invention discloses a computing device for browsing value attributes of a video with goods in a second aspect, which comprises:
the acquisition module is used for acquiring historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in a target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data;
the first calculation module is used for calculating a target relation coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
and the second calculation module is used for calculating the browsing value attribute of each taken video to the target commodity in the target time period according to the target relation coefficient corresponding to each taken video and the current commodity browsing data of the target commodity in the target time period, wherein the browsing value attribute of the taken video to the target commodity in the target time period is used for representing the browsing amount contribution condition of the taken video to the target commodity in the target time period.
As an optional implementation manner, in the second aspect of the present invention, the first computing module includes:
the first calculation submodule is used for calculating a corresponding stable relation coefficient of each video with goods in the historical time period according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period;
and the determining submodule is used for determining a stable relation coefficient corresponding to the taken video in the historical time period as a target relation coefficient corresponding to the taken video.
As an optional implementation manner, in the second aspect of the present invention, before the determining sub-module determines, as the target relationship coefficient corresponding to any one of the videos with goods, the stable relationship coefficient corresponding to the video with goods in the historical time period of the video with goods in the historical time period, the first calculating sub-module is further configured to calculate, according to current interaction data of each video with goods in the target time period and current product browsing data of the target product in the target time period, a current relationship coefficient corresponding to each video with goods in the target time period, and calculate a coefficient difference between the current relationship coefficient corresponding to each video with goods in the target time period and the stable relationship coefficient corresponding to the video with goods in the historical time period;
the first computing module further comprises:
the judging submodule is used for judging whether the absolute value of the coefficient difference value corresponding to any taken video is smaller than or equal to a predetermined difference threshold value or not, and when the judgment result is yes, the determining submodule is triggered to execute the operation of determining the stable relation coefficient corresponding to the taken video in the historical time period as the target relation coefficient corresponding to the taken video;
and the first correction submodule is used for correcting the current relation coefficient corresponding to the taken video according to the stable relation coefficient corresponding to the taken video when the judgment result of the judgment submodule is negative, so as to obtain the target relation coefficient corresponding to the taken video.
As an optional implementation manner, in the second aspect of the present invention, the first computing module includes:
the second calculation submodule is used for calculating a corresponding stable relation coefficient of each video with goods in the historical time period according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period;
the second calculating submodule is further configured to calculate a corresponding current relationship coefficient of each video with goods in the target time according to current interaction data of each video with goods in the target time period and current goods browsing data of the target goods in the target time period;
and the second correction submodule is used for correcting the corresponding current relation coefficient of the video with goods in the target time period according to the corresponding stable relation coefficient of the video with goods in the historical time period to obtain the corresponding target relation coefficient of the video with goods.
As an optional implementation manner, in the second aspect of the present invention, the specific manner of calculating the stable relationship coefficient corresponding to each taken video in the historical time period according to the historical interaction data corresponding to each taken video and the historical product browsing data of the target product in the historical time period by the first calculating sub-module is as follows:
acquiring historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
constructing an independent equation corresponding to each historical time period according to the historical interaction data increment of each video with goods in each historical time period and the historical goods browsing data increment of the target goods in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relationship coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relationship between the historical interaction data increment of all the videos with goods and the historical goods browsing data increment of the target goods in the historical time period;
and establishing an equation set by using the independent equations corresponding to all the historical time periods, and calculating the solution of the equation set according to an iterative method to obtain a corresponding stable relation coefficient of each video with goods in the historical time period.
As an optional implementation manner, in the second aspect of the present invention, the specific manner of calculating the solution of the equation set by the first calculation sub-module according to an iterative method to obtain the corresponding stable relationship coefficient of each video with goods in the historical time period is as follows:
continuously calculating a solution to the system of equations according to an iterative method;
judging whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the precision condition, the newly calculated solution of the equation set is respectively determined as a stable relation coefficient corresponding to each video with goods in the historical time period;
and triggering and executing the operation of continuously calculating the solution of the equation set according to an iterative method when judging that the newly calculated solution of the equation set does not meet the precision condition.
As an optional implementation manner, in the second aspect of the present invention, the determining sub-module is further configured to determine, according to the obtained current interaction data of each taken video in the target time period, whether there is a taken video with incomplete interaction data in all the taken videos; when the judging submodule judges that no pickup video with incomplete interactive data exists in all the pickup videos, the first calculating submodule is triggered to execute the operation of calculating a current relation coefficient corresponding to each pickup video in the target time according to the current interactive data of each pickup video in the target time period and current commodity browsing data of the target commodity in the target time period, and calculating a coefficient difference value between the current relation coefficient corresponding to each pickup video in the target time period and a stable relation coefficient corresponding to the pickup video in the historical time period;
the first correction submodule is further configured to, when the judgment submodule judges that there are pickup videos with incomplete interactive data in all the pickup videos, perform data supplement operation on the pickup videos with incomplete interactive data in all the pickup videos, and trigger the first calculation submodule to perform the operation of calculating a current relationship coefficient corresponding to each pickup video in the target time period according to current interactive data of each pickup video in the target time period and current product browsing data of the target product in the target time period, and calculating a coefficient difference value between a current relationship coefficient corresponding to each pickup video in the target time period and a stable relationship coefficient corresponding to the pickup video in the historical time period;
the specific way of performing data supplement operation on the loaded video with incomplete interactive data in all the loaded videos by the first revision submodule is as follows:
counting the number of the loaded videos with incomplete interactive data in all the loaded videos, and acquiring an interactive data supplement algorithm matched with the number;
and according to the acquired interactive data supplement algorithm, performing data supplement operation on the loaded video with incomplete interactive data in all the loaded videos.
In a third aspect, the invention discloses another apparatus for calculating browsing value attribute of a video with goods, the apparatus comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute part or all of the steps of the method for calculating the browsing value attribute of the video with goods disclosed by the first aspect of the embodiment of the invention.
A fourth aspect of the present invention discloses a computer storage medium, where the computer storage medium stores computer instructions, and the computer instructions, when called, are used to perform part or all of the steps in the method for calculating a browsing value attribute of a video with goods disclosed in the first aspect of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in a target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data are obtained; calculating a target relation coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical commodity browsing data of target commodities in a historical time period; and calculating the browsing value attribute of each video with goods to the target goods in the target time period according to the target relation coefficient corresponding to each video with goods and the current goods browsing data of the target goods in the target time period, wherein the browsing value attribute of the video with goods to the target goods in the target time period is used for representing the contribution condition of the video with goods to the browsing amount of the target goods in the target time period. Therefore, the method and the device can provide a mode for determining the browsing value attribute of the commodities so as to accurately and quickly determine the browsing value attribute of each video with the commodities to the commodities for selling the commodities with the commodities, and further provide objective and accurate reference for determining the sales contribution condition of each video with the commodities to the commodities and/or the goods taking capability of the video blogger of each video with the commodities.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for calculating a browsing value attribute of a video with goods according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating another method for calculating a browsing value attribute of a videos of goods according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a computing device for browsing value attributes of videos of goods according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of another computing device for browsing value attributes of videos of cargos, according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a computing device for displaying a browsing value attribute of a video tape cargo according to an embodiment of the present invention;
FIG. 6 is a block diagram of a computing device for browsing value attributes of videos of different stocks according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or article.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention discloses a method and a device for calculating browsing value attributes of videos with goods, which can provide a mode for determining the browsing value attributes of goods so as to accurately and quickly determine the browsing value attributes of each video with goods for selling goods with goods, and further can provide objective and accurate reference basis for determining the selling contribution condition of each video with goods to goods and/or the goods taking capability of a video blogger of each video with goods. The following are detailed below.
Example one
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a method for calculating a browsing value attribute of a video with goods according to an embodiment of the present invention. The method described in fig. 1 may be applied to a computing device, where the computing device may be a computing terminal, a computing device, or a server, and the server may be a local server or a cloud server, and the embodiment of the present invention is not limited thereto. As shown in fig. 1, the method for calculating the browsing value attribute of the videos with goods may include the following operations:
101. the calculation device obtains historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in the target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data.
In the embodiment of the invention, each in-stock video is also used for in-stock selling the target commodity in the historical time period. The method includes the steps that a commodity link video corresponding to a target commodity is taken as a commodity link video, the commodity link video corresponding to the target commodity is taken as a commodity link video, the video content is taken as a promotion content of the target commodity, the commodity link video corresponding to the target commodity and the commodity link video corresponding to the target commodity are taken as a commodity link video, and the video content is taken as a promotion content of the target commodity.
In the embodiment of the present invention, after the step 101 is executed, the step 102 may be directly triggered to be executed. In other optional embodiments, after the computing device obtains the historical interaction data corresponding to each of all videos of shipment for the target item of shipment sales within the target time period, the method may further include the following operations:
the calculation device judges whether the goods-carrying videos with incomplete historical interaction data exist in all the goods-carrying videos according to the historical interaction data corresponding to each goods-carrying video in all the goods-carrying videos for selling the target goods with goods in the obtained target time period;
when the judgment result is negative, the computing device executes subsequent operation;
and when the judgment result is yes, the computing device performs data supplement operation on the loaded videos with incomplete historical interactive data in all the loaded videos, and after the historical interactive data missing from the loaded videos with incomplete historical interactive data are completely supplemented, the computing device performs subsequent operation.
The subsequent operation may be the operation in step 102, or the operation of acquiring historical commodity browsing data in a historical time period, and there is no sequential relationship between the acquired historical interaction data and the acquired historical commodity browsing data.
Further optionally, the computing device performs data supplement operations on the shipped videos with incomplete historical interaction data in all shipped videos, and may include:
counting the number of loaded videos (also called as 'first type loaded videos') with incomplete historical interactive data in all the loaded videos by the computing device, and acquiring an interactive data supplement algorithm matched with the number;
and the computing device performs data supplement operation on the first type of videos with goods in which the historical interactive data in all the videos with goods is incomplete according to the acquired interactive data supplement algorithm.
When the number of the first type of loaded videos is small (such as less than or equal to the determined number threshold), the interactive data supplement algorithm matched with the number is a big data supplement algorithm; when the number is large, the interaction data supplement algorithm matched with the number is a crawler supplement algorithm. It should be noted that, if the number of the first type of videos with goods is large, or the total time period (for example, the total number of days) in which the historical interaction data of all the first type videos with goods is incomplete in the historical time period is large, the computing device may also directly output a prompt to prompt that the browsing value attribute of the videos with goods to the target goods is not computed. Optionally, when the total time period (e.g., total number of days) during which the historical interaction data is incomplete in the historical time period is more for all of the first type of videos with goods, the computing device may further perform the following operations before outputting the prompt:
the calculation device calculates the interactive data increment ratio of the goods-carrying videos with more historical interactive data or complete historical interactive data in each time period according to the existing historical interactive data of all the goods-carrying videos in the historical time period, and if the interactive data increment ratio is smaller than or equal to a preset ratio threshold value, the output prompt is executed to prompt the operation of not calculating the browsing value attribute of the goods-carrying videos to the target goods;
if the incremental proportion of the interaction data is larger than the preset proportion threshold value, the computing device can supplement the lacked historical interaction data according to a crawler supplement algorithm.
The total time period during which the historical interaction data is incomplete in the historical time period is exemplified as follows:
assuming that the cargo carrying video comprises a cargo carrying video A, a cargo carrying video B and a cargo carrying video C, the historical time period is 5 days, the time period is 1 day, the cargo carrying video with incomplete historical interactive data is the cargo carrying video A and the cargo carrying video B, 3 days of history interactive data of the cargo carrying video A in the historical time period is incomplete, 2 days of history interactive data of the cargo carrying video B in the historical time period is incomplete, and the total time of the incomplete history interactive data in the historical time period is equal to 2 days plus 3 days. Namely: the total time period of incomplete historical interaction data of all the first type videos with cargos in the historical time period is equal to the sum of the time periods of incomplete historical interaction data of all the first type videos with cargos in the historical time period.
As a further alternative, when the number is less than or equal to the predetermined number threshold, the computing device performs a data supplement operation on the loaded video with incomplete interactive data in all the loaded videos according to the obtained interactive data supplement algorithm, and the data supplement operation may include:
the computing device determines a video delivery platform of each first type of video with goods;
for each determined video delivery platform, the computing device collects sample cargo videos delivered on the video delivery platform, which meet preset conditions and reach corresponding orders of magnitude (such as 1000), calculates the interaction data variation of each sample cargo video in each time period (such as every day) in a predetermined time period, calculates the cycle interaction data increment ratio corresponding to the video delivery platform according to the interaction data variation of each sample cargo video in each time period in the predetermined time period, and determines the average value or median of all the cycle interaction data increment ratios as the platform interaction data increment percentage corresponding to the video delivery platform in each time period;
and for each determined first type of video with goods, the computing device supplements historical interaction data lacking in the video with goods in the corresponding time period in the target time period according to the platform interaction data increment percentage corresponding to the video delivery platform where the first type of video with goods is located in each time period and the existing historical interaction data of the first type of video with goods. That is, when the historical interaction data of the first type of loaded video in the nth time period is supplemented, the computing device needs to use the platform interaction data increment percentage corresponding to the video delivery platform in the nth time period, where the start time of the nth time period is the start release time of the loaded video.
The amount of change in the interactive data of the sample tape video in a time period (e.g., every day) is specifically equal to the total amount of the interactive data of the sample tape video at the ending time of the time period minus the total amount of the interactive data of the sample tape video at the ending time of the time period immediately adjacent to the time period.
Optionally, for any determined video delivery platform, the sample pickup video delivered on the video delivery platform and meeting the preset condition is specifically a pickup video delivered on the video delivery platform and having complete interaction data in each time period within the determined time period, and further optionally, the determined time period may be greater than or equal to the maximum delivery time length in the delivery time lengths of all the first type pickup videos up to the end time of the target time period. Still further optionally, for any determined video delivery platform, the commodities sold in the sample taken-good video and the target commodity belong to the same category, and/or the initial release time of all the sample taken-good videos is the same.
For example, if a first type of loaded video D lacks historical interaction data for 3 days 1-3 from the release time, and the increment percentages of the interaction data of 3 sample loaded videos released on the video delivery platform where the first type of loaded video D is released on day 1 are 60%, 50% and 55%, respectively, the increment percentage of the interaction data of the video delivery platform on day 1 is 55% (i.e., the average of the three); the interactive data increment percentages of the 3 sample loaded videos released by the video delivery platform where the first type of loaded video D is located on the 2 nd day are 30%, 20% and 20%, respectively, and then the periodic interactive data increment percentage of the video delivery platform on the 2 nd day is 23.33% (i.e. the average of the three); the increment percentages of the interactive data of the 3 sample loaded videos released by the video delivery platform where the first type of loaded video D is located on the 2 nd day are 30%, 20% and 20%, respectively, and then the increment percentage of the periodic interactive data of the video delivery platform on the 2 nd day is 23.33% (i.e. the average of the three), and similarly, the increment percentage of the periodic interactive data of the video delivery platform on the 3 rd day can also be calculated.
It should be noted that, since the total amount of the interactive data of most of the loaded videos does not substantially change after the release duration reaches the preset duration (for example, reaches 7 days), the incremental amount of the interactive data of each time period after the release duration of the loaded videos reaches the preset duration may be determined to be 0.
As a further alternative, when the number is greater than the predetermined number threshold, the computing device performs, according to the acquired interactive data supplement algorithm, a data supplement operation on the loaded video with incomplete interactive data in all the loaded videos, and may include:
the calculation device repeatedly captures historical interaction data corresponding to each first type of cargo-carrying video in a historical time period for one time or a plurality of times according to a predetermined crawler algorithm, and supplements the historical interaction data corresponding to the first type of cargo-carrying video in the historical time period, which is acquired aiming at the first type of cargo-carrying video, according to the corresponding historical interaction data of each newly captured first type of cargo-carrying video in the historical time period.
It should be noted that, no matter how many videos with goods whose historical interaction data is incomplete, the computing device may perform data supplementation on videos with goods whose historical interaction data is incomplete through the same interaction data supplementation algorithm (such as a big data supplementation algorithm or a crawler algorithm).
It should be further noted that, when the total time period (e.g., total number of days) during which the historical interactive data of all the first type videos with goods is incomplete is large, the computing device may supplement the historical interactive data missing during a part of the time period by using the crawler algorithm, and when the total time period during which the historical interactive data is incomplete is reduced to a certain number, supplement the historical interactive data missing during the remaining time period by using the big data supplement algorithm.
Therefore, when the historical interaction data corresponding to the video with goods is not available, the optional embodiment can supplement the lacking historical interaction data, the integrity of the historical interaction data corresponding to the video with goods is guaranteed, and the accuracy of the subsequently calculated browsing value attribute of each video with goods to the target commodity in the target time period is improved. In addition, the optional embodiment can also adaptively select a proper interactive data supplement algorithm according to the quantity of the loaded videos lacking the historical interactive data, and when the lacking historical interactive data is less, the historical interactive data is supplemented through a big data supplement algorithm, so that the accuracy of the supplemented historical interactive data is improved, and the data supplement efficiency is ensured to a certain extent; when the missing historical interaction data is more, the data is grabbed again through the crawler algorithm, and compared with a big data supplement algorithm, the data calculation amount is reduced.
102. And the calculating device calculates the target relation coefficient corresponding to each video with goods according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period.
In the embodiment of the invention, the end time of the historical time period is earlier than the starting time of the target time period.
103. And the calculating device calculates the browsing value attribute of each video with goods to the target commodity in the target time period according to the target relation coefficient corresponding to each video with goods and the current commodity browsing data of the target commodity in the target time period.
In the embodiment of the present invention, the current product browsing data of the target product in the target time period may specifically be the visitor volume or browsing volume of the target product in the target time period, which is not limited in the embodiment of the present invention. The browsing value attribute of the pickup video to the target commodity in the target time period is used for representing the browsing volume contribution condition of the pickup video to the target commodity in the target time period, and may be a browsing volume contribution ratio, a specific browsing volume, or a corresponding browsing volume contribution level.
In this embodiment of the present invention, optionally, the browsing value attribute of each pickup video to the target product in the target time period may be determined by a product of a target relationship coefficient corresponding to the pickup video and current product browsing data of the target product in the target time period, and the computing device may directly determine the calculated product of the pickup video and the current product browsing data of the target product in the target time period as the browsing value attribute of the pickup video to the target product in the target time period, or may finally determine the browsing value attribute of the pickup video to the target product in the target time period according to the product of the pickup video and a predetermined browsing contribution correction parameter, which is not limited in the embodiment of the present invention.
It should be noted that, if the required browsing value attribute is the browsing contribution ratio, the computing device may directly determine the browsing value attribute of the pickup video to the target product according to the target relationship coefficient corresponding to each pickup video.
In an alternative embodiment, before performing step 101, the method may further include the following operations:
the computing device determining a time length of the target time period;
the calculating device judges whether the time length of the target time period meets a predetermined length condition or not;
when the time length of the target time period is judged to meet the predetermined length condition, step 101 is triggered to be executed.
Optionally, the determining, by the computing device, whether the time length of the target time period satisfies a predetermined length condition may include:
the calculation device judges whether the time length of the target time period is greater than or equal to the time length corresponding to the predetermined minimum time period and less than or equal to the predetermined maximum time length;
and when the time length of the target time period is judged to be greater than or equal to the time length corresponding to the minimum time period and less than or equal to the maximum time length, determining that the time length of the target time period meets a predetermined length condition.
Therefore, in the optional embodiment, after the time period corresponding to the browsing value attribute is determined to be required to be determined, whether the time length of the time period meets the predetermined length condition is judged, and then the subsequent operation is performed under the condition that the time length of the time period meets the predetermined length condition, so that unnecessary operations of the computing device can be reduced, and the accuracy and the reliability of the computing device in executing the subsequent operation are facilitated.
In another alternative embodiment, after performing finish step 103, the method further comprises the operations of:
the computing device screens at least one target cargo carrying video which has browsing value attributes meeting browsing contribution conditions (such as the visitor volume is greater than or equal to a visitor volume threshold value or the visitor volume ratio is greater than or equal to a visitor volume ratio threshold value) for the target commodity from all the cargo carrying videos;
the calculation device determines relevant video parameters of each target video with goods according to the video identification uniquely corresponding to each target video with goods;
the calculating device counts at least one video parameter with goods, the frequency of occurrence of which exceeds a preset frequency threshold value, according to the related video parameters of all the target video with goods.
The relevant video parameters of the target pickup video may include one or more combinations of a release platform of the target pickup video, a vermicelli amount of a video blogger of the target pickup video, a video style of the target pickup video, a release duration of the target pickup video, and the like, and the embodiment of the present invention is not limited.
In this alternative embodiment, all the in-stock video parameters counted by the computing device are used as an analysis model to analyze video parameters (such as a publishing platform, a video style, and the like) with a large influence degree of the in-stock video on the browsing value attribute of the target product, and may also be used to output the video parameters to the advertiser of the target product, so that the advertiser of the target product knows the relevant video parameters of the in-stock video that contribute a large amount to the browsing of the target product, so that the advertiser of the target product can make effective decisions on the in-stock video, such as selecting an appropriate publishing platform, selecting an appropriate video style, and the like.
Therefore, the optional embodiment can intelligently count the video parameters with larger influence degree of the videos with the goods on the browsing value attribute of the target commodity after the browsing value attribute of each video with the goods on the target commodity is determined, so that the intelligent function of the computing device can be further enriched, and an effective reference basis can be provided for an advertiser to make a decision on the videos with the goods better.
In yet another alternative embodiment, after performing finish step 103, the method further comprises the operations of:
the calculation device screens a target delivery video with the highest browsing value attribute on the target commodity from all the delivery videos, and determines related video parameters of the target delivery video, wherein the related video parameters can include one or a combination of more of the release duration of the target delivery video, the release platform of the target delivery video, the video style of the target delivery video, the number of fans of the video bloggers of the target delivery video, and the like.
Therefore, the optional embodiment can also automatically determine the relevant video parameters of the target delivery video with the highest browsing value attribute of the target commodity in the target time period, so as to provide an effective reference basis for formulating an advertisement delivery strategy (for example, selecting a suitable video blogger to release the delivery video with a certain video style on a suitable release platform, and the like).
In yet another optional embodiment, the calculating, by the calculating device, the target relationship coefficient corresponding to each item-taking video according to the historical interaction data corresponding to each item-taking video and the historical product browsing data of the target product in the historical time period may include:
the calculating device calculates a corresponding stable relation coefficient of each video with goods in a historical time period according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period;
the calculation device calculates a corresponding current relation coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period;
for any one video with goods, the calculating device corrects the corresponding current relation coefficient of the video with goods in the target time period according to the corresponding stable relation coefficient of the video with goods in the historical time period, and the target relation coefficient corresponding to each video with goods is obtained.
In this optional embodiment, further optionally, the calculating device calculates a stable relationship coefficient corresponding to each item-taking video in the historical time period according to the historical interaction data corresponding to each item-taking video and the historical product browsing data of the target product in the historical time period, and may include:
the calculation device acquires historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
the calculation device constructs an independent equation corresponding to each historical time period according to the historical interactive data increment of each video with goods in each historical time period and the historical commodity browsing data increment of the target commodity in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relation coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relation between the historical interactive data increment of all the videos with goods and the historical commodity browsing data increment of the target commodity in the historical time period;
and the calculation device constructs an equation set by using independent equations corresponding to all historical time periods, and calculates the solution of the equation set according to an iterative method to obtain the corresponding stable relation coefficient of each video with cargos in the historical time period.
Still further optionally, the calculating device calculates the solution of the equation set according to an iterative method to obtain the corresponding stable relationship coefficient of each video with goods in the historical time period, and may include:
the calculation device continuously calculates the solution of the equation set according to an iteration method;
the calculation device judges whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the accuracy condition, the calculating device respectively determines the newly calculated solution of the equation set as a corresponding stable relation coefficient of each video with cargos in a historical time period;
when the newly calculated solution of the equation set is judged not to satisfy the precision condition, the calculation device triggers the execution of the above-mentioned operation of continuously calculating the solution of the equation set according to the iterative method.
And when the newly calculated solution of the equation set is judged to meet the precision condition, the sub-solution corresponding to the video with the goods is the corresponding stable relation coefficient of the video with the goods in the historical time period.
Still further optionally, the determining, by the computing device, whether the newly computed solution of the equation set satisfies a preset accuracy condition may include:
the computing device determines a preset number (e.g., 6) of solutions (also referred to as historical solutions) of the system of equations that are successively computed before the most recently computed solution (also referred to as the most recent solution) of the system of equations;
the calculating device respectively calculates the difference value of the latest solution and each historical solution;
if the absolute value of the difference between the latest solution and each historical solution is less than or equal to the difference threshold, or if the ratio of the number of the historical solutions with the absolute value of the difference between the latest solution and the historical solution is less than or equal to the difference threshold to the preset number is greater than or equal to the corresponding threshold, the calculating device determines that the newly calculated solution of the equation set meets the preset precision condition.
It should be noted that the process of determining whether the newly calculated solution of the equation set satisfies the predetermined accuracy condition by the calculation device may also be understood as a process of determining whether the calculated solution of the equation set tends to be stable. And because the solution of the equation set comprises the sub-solution corresponding to each video with goods, when the difference value of the two solutions of the equation set is calculated, the calculating device respectively calculates the difference value of the sub-solutions corresponding to the same video with goods in the two solutions to obtain the sub-difference value corresponding to each video with goods, and if the sub-difference values corresponding to all the videos with goods are less than or equal to the difference threshold value or the ratio of the number of the videos with goods, the corresponding sub-difference values of which are less than or equal to the difference threshold value, to the number of the videos with goods is greater than or equal to the ratio threshold value, the calculating device determines that the difference value of the two solutions is less than or equal to the difference.
It should be noted that, if the historical interactive data increment of a certain loaded video in a certain historical time period is negative, the computing device may directly determine the sub-relationship coefficient (or the historical sub-relationship coefficient) corresponding to the loaded video in the historical time period as zero, or may directly correct the negative historical interactive data increment as zero.
For example, assuming that the historical time periods are 2020.1.1-2020.1.5, all the determined videos with goods include video a, video b and video c, the duration of the historical time period is 1 day, the commodity browsing data of 2020.1.1-2020.1.5 per day are 100, 250 (increment is 150), 450 (increment is 200), 600 (increment is 150), 700 (increment is 100), the amount of praise for capturing only video b on 2020.1.1 day is 50, the amount of praise for the rest of videos can be 0, that is, the amount of praise for capturing video a, video b and video c on 2020.1.1 day is 0, 50 and 0, respectively, the amount of praise for capturing video a, video b and video c on 2020.1.2 day is 100 (increment is 100), 100 (increment is 50) and 0 (increment is 0), the amount of praise for capturing video a, video b and video c on 2020.1.3 day is 200 (increment is 100), 300 (increment is 200) and 100 (increment is 100), the amounts of praise captured on the day 2020.1.4 for video a, video b, and video c are 350 (increments of 150), 500 (increments of 200), and 200 (increments of 100), respectively, and on the day 2020.1.5 for video a, video b, and video c are 300 (increments of 0), 500 (increments of 0), and 300 (increments of 100), respectively, then the independent equations for each day are:
2020.1.1 50*Tb＝100；
2020.1.2 100*Ta+50*Tb＝150；
2020.1.3 100*Ta+200*Tb+100*Tc＝200；
2020.1.4 150*Ta+200*Tb+100*Tc＝150；
2020.1.5 100*Tc＝100；
in practical application, in consideration of time attenuation, video heat attenuation, recommendation mechanism change and other factors, Ta, Tb and Tc of each day may be different, because various influencing factors are difficult to quantify, Ta, Tb and Tc need to be calculated once each day, and the values of Ta, Tb and Tc calculated in combination with the history are optimized, after multiple times of calculation are accumulated, the values of Ta, Tb and Tc tend to be stable, and the values of Ta, Tb and Tc which tend to be stable can be regarded as the values closest to the correct values. It should be noted that the specific numerical value is not an actual numerical value, but is only used for explaining the construction process of the independent equation, and the specific data does not have an actual reference meaning, so that the data actually crawled is used as the standard.
It should be noted that, assuming that the data of the first day or the first historical time period exceeds two coefficients, each coefficient may be assumed to be equal first, and the calculation is optimized based on the subsequent historical time period.
Therefore, the optional embodiment can determine the stable relation coefficient of each video with goods in the historical time period based on the historical interaction data increment and the historical commodity browsing data increment, and further determine the target relation coefficient according to the determined stable relation coefficient, namely the target relation coefficient is not fixed and is changed along with the change of some factors, so that the accuracy and the reliability of the determined target relation coefficient are improved.
Example two
Referring to fig. 2, fig. 2 is a flowchart illustrating another method for calculating a browsing value attribute of a video with goods according to an embodiment of the present invention. The method described in fig. 2 may be applied to a computing device, where the computing device may be a computing terminal, a computing device, or a server, and the server may be a local server or a cloud server, and the embodiment of the present invention is not limited thereto. As shown in fig. 2, the method for calculating the browsing value attribute of the videos with goods may include the following operations:
201. the calculation device obtains historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in the target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data.
202. And the calculating device calculates the corresponding stable relation coefficient of each video with goods in the historical time period according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period.
In the embodiment of the present invention, after the step 202 is completed, the step 203 is triggered to be executed, it should be noted that, after the step 202 is completed, the step 206 may also be directly triggered to be executed for any shipped video, which is not limited in the embodiment of the present invention.
203. And the calculating device calculates the corresponding current relation coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period.
204. The calculating device calculates the coefficient difference value of the current relation coefficient corresponding to each video with goods in the target time period and the stable relation coefficient corresponding to the video with goods in the historical time period.
205. For any video with goods, the computing device judges whether the absolute value of the coefficient difference corresponding to the video with goods is less than or equal to a predetermined difference threshold value, and if the judgment result of the step 205 is yes, the step 206 is triggered; when the judgment result of the step 205 is negative, the step 207 is triggered to be executed.
206. And the calculating device determines the stable relation coefficient corresponding to the video with the goods in the historical time period as the target relation coefficient corresponding to the video with the goods.
In the embodiment of the present invention, it should be noted that the loaded video in step 206 is identical to the loaded video in step 205.
207. And the calculating device corrects the current relation coefficient corresponding to the video with the goods according to the stable relation coefficient corresponding to the video with the goods to obtain the target relation coefficient corresponding to the video with the goods.
In the embodiment of the present invention, it should be noted that the loaded video in step 207 is identical to the loaded video in step 205.
208. And the calculating device calculates the browsing value attribute of each video with goods to the target commodity in the target time period according to the target relation coefficient corresponding to each video with goods and the current commodity browsing data of the target commodity in the target time period.
In an optional embodiment, the calculating device calculates a stable relationship coefficient corresponding to each video with goods in the historical time period according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period, and may include:
the calculation device acquires historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
the calculation device constructs an independent equation corresponding to each historical time period according to the historical interactive data increment of each video with goods in each historical time period and the historical commodity browsing data increment of the target commodity in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relation coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relation between the historical interactive data increment of all the videos with goods and the historical commodity browsing data increment of the target commodity in the historical time period;
and the calculation device constructs an equation set by using independent equations corresponding to all historical time periods, and calculates the solution of the equation set according to an iterative method to obtain the corresponding stable relation coefficient of each video with cargos in the historical time period.
In this optional embodiment, further optionally, the calculating device calculates a solution of the equation set according to an iterative method to obtain a corresponding stable relationship coefficient of each video with goods in the historical time period, and may include:
the calculation device continuously calculates the solution of the equation set according to an iteration method;
the calculation device judges whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the accuracy condition, the calculating device respectively determines the newly calculated solution of the equation set as a corresponding stable relation coefficient of each video with cargos in a historical time period;
when the newly calculated solution of the equation set is judged not to satisfy the precision condition, the calculation device triggers the execution of the above-mentioned operation of continuously calculating the solution of the equation set according to the iterative method.
In another alternative embodiment, before step 203, the method may further comprise the operations of:
the calculation device judges whether the goods-carrying videos with incomplete interaction data exist in all the goods-carrying videos or not according to the acquired current interaction data of each goods-carrying video in the target time period;
when judging that no goods-taking video with incomplete interactive data exists in all the goods-taking videos, the computing device executes the operation of computing the corresponding current relation coefficient of each goods-taking video in the target time according to the current interactive data of each goods-taking video in the target time period and the current goods browsing data of the target goods in the target time period;
when the fact that the goods-taking videos with incomplete interactive data exist in all the goods-taking videos is judged, the computing device performs data supplement operation on the goods-taking videos with incomplete interactive data in all the goods-taking videos, and triggers and executes the operation of computing the corresponding current relation coefficient of each goods-taking video in the target time according to the current interactive data of each goods-taking video in the target time period and the current goods browsing data of the target goods in the target time period;
optionally, the computing device performs data supplementation operation on the shipped videos with incomplete interactive data in all the shipped videos, including:
counting the number of the loaded videos with incomplete interactive data in all the loaded videos by the computing device, and acquiring an interactive data supplement algorithm matched with the number;
and the computing device performs data supplement operation on the video with the goods in which the interactive data are incomplete in all the videos with the goods according to the acquired interactive data supplement algorithm.
It should be noted that, for other detailed descriptions of the interaction data supplement in the alternative embodiment, reference is made to the detailed description of the missing historical interaction data in the first embodiment, and the supplement principle is the same, and the embodiment of the present invention is not described again.
Therefore, when the current interactive data corresponding to the video with goods is not available, the optional embodiment can supplement the current interactive data which are not available, the integrity of the current interactive data corresponding to the video with goods is ensured, and the accuracy of the subsequently calculated current relation coefficient corresponding to each video with goods is improved. In addition, the optional embodiment can also adaptively select a proper interactive data supplement algorithm according to the quantity of the loaded videos lacking the current interactive data, and when the current interactive data is less, the current interactive data is supplemented through a big data supplement algorithm, so that the accuracy of the supplemented current interactive data is improved, and the data supplement efficiency is ensured to a certain extent; when the current interactive data is more, the current interactive data is grabbed again through the crawler algorithm, and compared with a big data supplement algorithm, the data calculation amount is reduced.
Therefore, the method described by the embodiment of the invention can provide a method for determining the commodity browsing value attribute based on historical related data and further combined with current related data, so as to accurately and quickly determine the browsing value attribute of each video with goods for selling commodities with goods, and further provide objective and accurate reference for determining the sales contribution condition of each video with goods to commodities and/or the goods taking capability of the video blogger of each video with goods.
EXAMPLE III
Referring to fig. 3, fig. 3 is a schematic structural diagram of another computing device for browsing value attributes of videos of freights according to an embodiment of the present invention. The apparatus described in fig. 3 may be applied to a computing terminal, a computing device, or a server, and the server may be a local server or a cloud server, which is not limited in the embodiment of the present invention. As shown in fig. 3, the apparatus may include:
the obtaining module 301 is configured to obtain historical interaction data corresponding to each taken video in all taken videos for selling the target product with goods in the target time period, and historical product browsing data of the target product in the historical time period corresponding to the historical interaction data.
The first calculating module 302 is configured to calculate a target relationship coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical commodity browsing data of a target commodity in a historical time period.
The second calculating module 303 is configured to calculate a browsing value attribute of each pickup video to the target product in the target time period according to the target relationship coefficient corresponding to each pickup video and the current product browsing data of the target product in the target time period.
In an alternative embodiment, as shown in fig. 4, the first calculation module 302 may include:
the first calculating submodule 3021 is configured to calculate a stable relationship coefficient corresponding to each video with goods in a historical time period according to historical interaction data corresponding to each video with goods and historical product browsing data of a target product in the historical time period.
The determining submodule 3022 is configured to, for any one of the taken videos, determine a stable relationship coefficient corresponding to the taken video in a historical time period as a target relationship coefficient corresponding to the taken video.
Further optionally, the first calculating sub-module 3021 is further configured to, before the determining sub-module 3022 determines, for any one of the pickup videos, a stable relationship coefficient corresponding to the pickup video in the historical time period as a target relationship coefficient corresponding to the pickup video, calculate a current relationship coefficient corresponding to each pickup video in the target time period according to current interaction data of each pickup video in the target time period and current product browsing data of the target product in the target time period, and calculate a coefficient difference between the current relationship coefficient corresponding to each pickup video in the target time period and the stable relationship coefficient corresponding to the pickup video in the historical time period.
As shown in fig. 4, the first calculating module 302 may further include:
the determining submodule 3023 is configured to, for any one of the pickup videos, determine whether an absolute value of a coefficient difference corresponding to the pickup video is smaller than or equal to a predetermined difference threshold, and if the determination result is yes, trigger the determining submodule 3022 to perform the operation of determining the stable relationship coefficient corresponding to the pickup video in the historical time period as the target relationship coefficient corresponding to the pickup video.
The first correcting submodule 3024 is configured to, for any one of the taken videos, correct the current relationship coefficient corresponding to the taken video according to the stable relationship coefficient corresponding to the taken video when the determination result of the determining submodule 3023 is negative, and obtain a target relationship coefficient corresponding to the taken video.
Still further optionally, the determining sub-module 3023 is further configured to determine whether a good video with incomplete interactive data exists in all the good videos according to the obtained current interactive data of each good video in the target time period; when the judging submodule 3023 judges that there is no pickup video with incomplete interactive data in all pickup videos, the first calculating submodule 3021 is triggered to perform the above-described operation of calculating the current relationship coefficient corresponding to each pickup video in the target time according to the current interactive data of each pickup video in the target time period and the current product browsing data of the target product in the target time period, and calculating the coefficient difference between the current relationship coefficient corresponding to each pickup video in the target time period and the stable relationship coefficient corresponding to the pickup video in the historical time period.
The first modification submodule 3024 is further configured to, when the determining submodule 3023 determines that there is a pickup video with incomplete interactive data in all pickup videos, perform a data supplement operation on the pickup video with incomplete interactive data in all pickup videos, and trigger the first calculating submodule 3021 to perform the above-mentioned operation of calculating a current relationship coefficient corresponding to each pickup video in a target time according to the current interactive data of each pickup video in the target time period and current product browsing data of a target product in the target time period, and calculating a coefficient difference between the current relationship coefficient corresponding to each pickup video in the target time period and a stable relationship coefficient corresponding to the pickup video in a historical time period.
Further optionally, the specific way of performing the data supplement operation on the loaded video with incomplete interactive data in all the loaded videos by the first modification submodule 3024 is as follows:
counting the number of the loaded videos with incomplete interactive data in all the loaded videos, and acquiring an interactive data supplement algorithm matched with the number;
and according to the acquired interactive data supplement algorithm, performing data supplement operation on the loaded video with incomplete interactive data in all the loaded videos.
Still further optionally, the specific way of calculating the stable relationship coefficient corresponding to each item-taking video in the historical time period by the first calculating submodule 3021 according to the historical interaction data corresponding to each item-taking video and the historical product browsing data of the target product in the historical time period may be:
acquiring historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
constructing an independent equation corresponding to each historical time period according to the historical interactive data increment of each video with goods in each historical time period and the historical commodity browsing data increment of the target commodity in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relation coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relation between the historical interactive data increment of all the videos with goods and the historical commodity browsing data increment of the target commodity in the historical time period;
and constructing an equation set by using independent equations corresponding to all historical time periods, and calculating the solution of the equation set according to an iterative method to obtain a corresponding stable relation coefficient of the video with the goods in the historical time period.
Further optionally, the specific manner of obtaining the corresponding stable relationship coefficient of each video with goods in the historical time period by the first calculating submodule 3021 calculating the solution of the equation set according to the iterative method may be:
continuously calculating the solution of the equation set according to an iteration method;
judging whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the accuracy condition, respectively determining the newly calculated solution of the equation set as a corresponding stable relation coefficient of each video with cargos in a historical time period;
and when judging that the newly calculated solution of the equation set does not meet the precision condition, continuously triggering and executing the operation of continuously calculating the solution of the equation set according to the iteration method.
In another alternative embodiment, as shown in fig. 5, the first calculation module 302 may include:
the second calculating submodule 3025 is configured to calculate a stable relationship coefficient corresponding to each video with goods in a historical time period according to the historical interaction data corresponding to each video with goods and the historical product browsing data of the target product in the historical time period.
The second calculating submodule 3025 is further configured to calculate a current relationship coefficient corresponding to each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current product browsing data of the target product in the target time period.
The second modification submodule 3026 is configured to, for any one of the pickup videos, modify the current relationship coefficient corresponding to the pickup video in the target time period according to the stable relationship coefficient corresponding to the pickup video in the historical time period, and obtain the target relationship coefficient corresponding to each pickup video.
Further optionally, the specific manner of calculating the stable relationship coefficient corresponding to each video with goods in the historical time period according to the historical interaction data corresponding to each video with goods and the historical commodity browsing data of the target commodity in the historical time period by the second calculating submodule 3025 is as follows:
acquiring historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
constructing an independent equation corresponding to each historical time period according to the historical interactive data increment of each video with goods in each historical time period and the historical commodity browsing data increment of the target commodity in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relation coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relation between the historical interactive data increment of all the videos with goods and the historical commodity browsing data increment of the target commodity in the historical time period;
and (3) establishing an equation set by using independent equations corresponding to all historical time periods, and calculating the solution of the equation set according to an iterative method to obtain a corresponding stable relation coefficient of each video with cargos in the historical time period.
Further optionally, the specific manner of obtaining the corresponding stable relationship coefficient of each video with goods in the historical time period by the second calculating submodule 3025 calculating the solution of the equation set according to the iterative method may be:
continuously calculating the solution of the equation set according to an iteration method;
judging whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the accuracy condition, respectively determining the newly calculated solution of the equation set as a corresponding stable relation coefficient of each video with cargos in a historical time period;
and when judging that the newly calculated solution of the equation set does not meet the precision condition, continuously triggering and executing the operation of continuously calculating the solution of the equation set according to the iteration method.
Therefore, the device described by the embodiment of the invention can accurately and quickly determine the browsing value attribute of each taken-goods video for selling goods with goods based on historical related data and by further combining the determination mode of the goods browsing value attribute of the current related data, and further can provide objective and accurate reference basis for determining the sales contribution condition of each taken-goods video to goods and/or the goods taking capability of the video blogger of each taken-goods video.
Example four
Referring to fig. 6, fig. 6 is a schematic structural diagram of another computing device for browsing value attributes of videos of freights according to an embodiment of the present invention. As shown in fig. 6, the apparatus may include:
a memory 401 storing executable program code;
a processor 402 coupled with the memory 401;
the processor 402 calls the executable program code stored in the memory 401 to execute part or all of the steps of the method for calculating the browsing value attribute of the videos with goods disclosed in the first embodiment or the second embodiment of the present invention.
EXAMPLE five
The embodiment of the invention discloses a computer storage medium, which stores computer instructions, and when the computer instructions are called, the computer instructions are used for executing part or all of the steps of the method for calculating the browsing value attribute of the video with goods disclosed in the first embodiment or the second embodiment of the invention.
The above-described embodiments of the apparatus are merely illustrative, and the modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, where the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc-Read-Only Memory (CD-ROM), or other disk memories, CD-ROMs, or other magnetic disks, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
Finally, it should be noted that: the method and apparatus for calculating browsing value attribute of videos with goods disclosed in the embodiments of the present invention are only the preferred embodiments of the present invention, and are only used for illustrating the technical solutions of the present invention, not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for calculating browsing value attributes of videos with goods is characterized by comprising the following steps:
acquiring historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in a target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data;
calculating a target relation coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
and calculating the browsing value attribute of each video with goods to the target goods in the target time period according to the target relation coefficient corresponding to each video with goods and the current goods browsing data of the target goods in the target time period, wherein the browsing value attribute of the video with goods to the target goods in the target time period is used for representing the contribution condition of the video with goods to the browsing amount of the target goods in the target time period.
2. The method for calculating the browsing value attribute of the videos with goods according to claim 1, wherein calculating the target relation coefficient corresponding to each video with goods according to the historical interaction data corresponding to each video with goods and the historical browsing data of the target goods in the historical time period comprises:
calculating a stable relation coefficient corresponding to each video with goods in the historical time period according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
and for any one of the videos with goods, determining a stable relation coefficient corresponding to the video with goods in the historical time period as a target relation coefficient corresponding to the video with goods.
3. The method for calculating the browsing value attribute of the videos with goods according to claim 2, wherein before determining the stable relationship coefficient corresponding to any one of the videos with goods in the historical time period as the target relationship coefficient corresponding to the video with goods, the method further comprises:
calculating a corresponding current relation coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period;
calculating a coefficient difference value between a current relation coefficient corresponding to each video with goods in the target time period and a stable relation coefficient corresponding to the video with goods in the historical time period;
for any one taken video, judging whether the absolute value of the coefficient difference value corresponding to the taken video is smaller than or equal to a predetermined difference threshold value, and if so, triggering and executing the operation of determining the stable relation coefficient corresponding to the taken video in the historical time period as the target relation coefficient corresponding to the taken video; and if not, correcting the current relation coefficient corresponding to the video with the goods according to the stable relation coefficient corresponding to the video with the goods to obtain the target relation coefficient corresponding to the video with the goods.
4. The method for calculating the browsing value attribute of the videos with goods according to claim 1, wherein calculating the target relation coefficient corresponding to each video with goods according to the historical interaction data corresponding to each video with goods and the historical browsing data of the target goods in the historical time period comprises:
calculating a stable relation coefficient corresponding to each video with goods in the historical time period according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
calculating a corresponding current relation coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period;
and for any one of the video with goods, correcting the corresponding current relation coefficient of the video with goods in the target time period according to the corresponding stable relation coefficient of the video with goods in the historical time period to obtain the corresponding target relation coefficient of the video with goods.
5. The method for calculating the browsing value attribute of the videos with goods according to any one of claims 2 to 4, wherein the calculating the corresponding stable relationship coefficient of each video with goods in the historical time period according to the historical interaction data corresponding to each video with goods and the historical browsing data of the target goods in the historical time period comprises:
acquiring historical interaction data increment of each video with goods in each historical time period of the historical time period and historical commodity browsing data increment of the target commodity in each historical time period of the historical time period;
constructing an independent equation corresponding to each historical time period according to the historical interaction data increment of each video with goods in each historical time period and the historical goods browsing data increment of the target goods in each historical time period, wherein the independent equation corresponding to each historical time period takes the historical relationship coefficient corresponding to each video with goods in the historical time period as an unknown number and is used for expressing the relationship between the historical interaction data increment of all the videos with goods and the historical goods browsing data increment of the target goods in the historical time period;
and establishing an equation set by using the independent equations corresponding to all the historical time periods, and calculating the solution of the equation set according to an iterative method to obtain a corresponding stable relation coefficient of each video with goods in the historical time period.
6. The method for calculating the browsing value attribute of the videos with cargos according to claim 5, wherein the calculating the solution of the equation system according to the iterative method to obtain the corresponding stable relationship coefficient of each video with cargos in the historical time period comprises:
continuously calculating a solution to the system of equations according to an iterative method;
judging whether the newly calculated solution of the equation set meets a preset precision condition or not;
when the newly calculated solution of the equation set is judged to meet the precision condition, the newly calculated solution of the equation set is respectively determined as a stable relation coefficient corresponding to each video with goods in the historical time period;
and triggering and executing the operation of continuously calculating the solution of the equation set according to an iterative method when judging that the newly calculated solution of the equation set does not meet the precision condition.
7. The method for calculating the browsing value attribute of the videos with goods according to claim 3, wherein before calculating the corresponding current relationship coefficient of each video with goods in the target time according to the current interaction data of each video with goods in the target time period and the current goods browsing data of the target goods in the target time period, the method further comprises:
judging whether the goods-carrying videos with incomplete interactive data exist in all the goods-carrying videos or not according to the acquired current interactive data of each goods-carrying video in the target time period;
when judging that no pickup video with incomplete interactive data exists in all the pickup videos, triggering and executing the operation of calculating the corresponding current relation coefficient of each pickup video in the target time according to the current interactive data of each pickup video in the target time period and the current commodity browsing data of the target commodity in the target time period;
when the fact that the video with the incomplete interactive data exists in all the videos with the goods is judged, performing data supplement operation on the video with the incomplete interactive data in all the videos with the goods, and triggering and executing the operation of calculating a corresponding current relation coefficient of each video with the goods in the target time according to the current interactive data of each video with the goods in the target time period and the current goods browsing data of the target goods in the target time period;
wherein the performing data supplement operations on the shipped videos with incomplete interactive data in all the shipped videos comprises:
counting the number of the loaded videos with incomplete interactive data in all the loaded videos, and acquiring an interactive data supplement algorithm matched with the number;
and according to the acquired interactive data supplement algorithm, performing data supplement operation on the loaded video with incomplete interactive data in all the loaded videos.
8. A computing device for a value attribute for video browsing in cargo, the device comprising:
the acquisition module is used for acquiring historical interaction data corresponding to each video with goods in all videos with goods for selling the target goods with goods in a target time period and historical goods browsing data of the target goods in the historical time period corresponding to the historical interaction data;
the first calculation module is used for calculating a target relation coefficient corresponding to each video with goods according to historical interaction data corresponding to each video with goods and historical goods browsing data of the target goods in the historical time period;
and the second calculation module is used for calculating the browsing value attribute of each taken video to the target commodity in the target time period according to the target relation coefficient corresponding to each taken video and the current commodity browsing data of the target commodity in the target time period, wherein the browsing value attribute of the taken video to the target commodity in the target time period is used for representing the browsing amount contribution condition of the taken video to the target commodity in the target time period.
9. A computing device for a value attribute for video browsing in cargo, the device comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the method of calculating a value attribute for browsing a video in tape cargo according to any of claims 1 to 6.
10. A computer storage medium storing computer instructions which, when invoked, perform the method of calculating a value attribute for video browsing in tape shipments according to any of claims 1-6.
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