JP2023070653A - Video consumption scoring method, computer apparatus, and computer program - Google Patents

Abstract

To provide a video consumption scoring method, a computer apparatus, and a computer program.SOLUTION: A video consumption scoring method includes: a step of collecting logs on a video post out of posts held by an account on a social platform; and a step of calculating scores of video consumption time based on video duration using the logs, to calculate a video score of the account.SELECTED DRAWING: Figure 7

Description

The following description relates to techniques for scoring video consumption.

Social platforms such as social network services (SNS) and messengers form relationships between users and support interaction through posting.

Social platforms provide information to users in different contexts. For example, social platforms provide updates on user relationships, post updates, content recommendations, and various other information items.

For example, Patent Literature 1 (registered on June 29, 2017) discloses a technique for managing posted messages using SNS.

Some social platforms provide a timeline as a form of view for viewing posts. The timeline post display method is mainly used for content updates and private communication where consumption fluctuates.

Korean Patent No. 10-1754373

One object of the present invention is to provide a method, a computer device and a computer program for scoring video consumption.

A method performed by a computing device, said computing device comprising at least one processor configured to execute computer readable instructions contained in a memory, said method comprising: , collecting a log of video posts among posts held by an account on the social platform; and wherein the at least one processor scores video consumption time based on video duration according to the log, and said A method is provided that includes calculating a video score for an account.

According to one aspect, calculating the video score may calculate the video score using a ratio of the video duration of the account to the total video duration of all accounts.

According to another aspect, the step of calculating the video score includes video consumption rate, which is the ratio of the time actually consumed by the user to the playing time of the video owned by the account, and the total video duration of all accounts. The video score may be calculated using a video duration contribution ratio, which is the percentage of the account's video duration relative to .

According to another aspect, the step of collecting logs for the video post may include collecting logs for a video consumption time of the user over a predetermined time period for the video post.

According to another aspect, the step of collecting logs for video posts may include collecting logs for video consumption times of second hours or longer by users for video posts whose video playing times are longer than first hours. good.

According to yet another aspect, the at least one processor processes the videos into at least one of recommendation logic and ranking logic within a timeline service, which is one of the functions included in the social platform. It may further comprise using the score.

According to yet another aspect, the method further includes the at least one processor recommending at least one account through a timeline service that is one of the functions included in the social platform, wherein the recommending The step may include selecting a popularity-based recommended account using the video score.

According to yet another aspect, the step of recommending includes selecting a follow-based recommended account based on follow relevance indicating a registration relationship in the timeline service, and selecting the follow-based recommended account and the follow-based recommended account. to form a recommendation pool.

A method performed by a computing device, said computing device comprising at least one processor configured to execute computer readable instructions contained in a memory, said method comprising: calculating a video score for each account using the ratio of the video duration of each account to the total video duration of all accounts of the video platform; and making recommendations within the video platform by the at least one processor. A method is provided including utilizing the video score in at least one of logic and ranking logic.

A computer program recorded on a computer-readable recording medium for causing a computer device to execute the method is provided.

A computing device, comprising at least one processor configured to execute computer-readable instructions contained in a memory, wherein the at least one processor is adapted to process videos among posts held by an account on the social platform. A computer device is provided that collects logs for posts, scores the video consumption time based on the video duration according to the logs, and calculates the video score of the account.

According to the present invention, a method, computer apparatus and computer program for scoring video consumption can be provided.

1 is a diagram showing an example of a network environment in one embodiment of the present invention; FIG. 1 is a block diagram illustrating an example of a computing device, in accordance with one embodiment of the present invention; FIG. FIG. 2 illustrates an example of components that a processor of a computing device may include in one embodiment of the present invention; 1 is a flowchart illustrating an example method that may be performed by a computing device in accordance with an embodiment of the present invention; 4 is a flow chart showing an example of a process for calculating an author score in one embodiment of the present invention; It is a figure showing an example of the process of calculating a click score in one embodiment of the present invention. FIG. 4 is a flow chart showing an example of the process of calculating a video score in one embodiment of the present invention; FIG. FIG. 4 is a flow chart illustrating an example process of configuring an account recommendation pool in accordance with an embodiment of the present invention; FIG. FIG. 4 is a diagram showing a configuration example of an account recommendation pool in one embodiment of the present invention; FIG. 10 is a diagram showing an example of a screen displaying an account recommendation list in one embodiment of the present invention; FIG. 10 is a diagram showing an example of a screen displaying an account recommendation list in one embodiment of the present invention;

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Embodiments of the present invention relate to techniques for providing recommendation information on social platforms.

Embodiments, including those specifically disclosed herein, provide a timeline service that utilizes follow-based subscription relationships, a feature included in social platforms. At this time, the account on the timeline service can be scored and used for various recommendation logics.

An account scoring system according to an embodiment of the present invention may be implemented by at least one computer device, and an account scoring method according to an embodiment of the present invention may be implemented by at least one computer device included in the account scoring system. may be performed by At this time, a computer program according to an embodiment of the present invention may be installed and executed on the computer device, and the computer device may calculate the account points according to the embodiment of the present invention under the control of the executed computer program. method may be implemented. The computer program described above may be recorded in a computer-readable recording medium in order to combine with a computer device and cause the computer to execute the account scoring method.

FIG. 1 is a diagram showing an example of a network environment in one embodiment of the present invention. The network environment of FIG. 1 illustrates an example including multiple electronic devices 110 , 120 , 130 , 140 , multiple servers 150 , 160 , and a network 170 . Such FIG. 1 is merely an example for explaining the invention, and the number of electronic devices and the number of servers are not limited as in FIG. Also, the network environment in FIG. 1 is merely an example of the environment applicable to this embodiment, and the environment applicable to this embodiment is not limited to the network environment in FIG. .

The plurality of electronic devices 110, 120, 130, 140 may be fixed terminals or mobile terminals implemented by computing devices. Examples of the plurality of electronic devices 110, 120, 130, and 140 include smart phones, mobile phones, navigation systems, PCs (personal computers), notebook PCs, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia). Player), tablets, etc. As an example, FIG. 1 shows a smartphone as an example of the electronic device 110, but in embodiments of the present invention, the electronic device 110 substantially utilizes a wireless or wired communication scheme and communicates with other devices via the network 170. may refer to one of a variety of physical computing devices capable of communicating with the electronic equipment 120, 130, 140 and/or the server 150, 160.

The communication method is not limited, and not only the communication method using the communication network that can be included in the network 170 (eg, mobile communication network, wired Internet, wireless Internet, broadcasting network), but also the short distance between devices. Wireless communication may be included. For example, the network 170 includes a PAN (personal area network), a LAN (local area network), a CAN (campus area network), a MAN (metropolitan area network), a WAN (wide area network), a BBN (broadband network). k), such as the Internet It may include any one or more of the networks. Additionally, network 170 may include any one or more of network topologies including, but not limited to, bus networks, star networks, ring networks, mesh networks, star-bus networks, tree or hierarchical networks, and the like. will not be

Each of servers 150, 160 is implemented by one or more computing devices that communicate with a plurality of electronic devices 110, 120, 130, 140 over network 170 to provide instructions, code, files, content, services, etc. good too. For example, server 150 may be a system that provides services (eg, social services) to multiple electronic devices 110 , 120 , 130 , 140 connected via network 170 .

FIG. 2 is a block diagram illustrating an example computing device, in accordance with one embodiment of the present invention. Each of the plurality of electronic devices 110, 120, 130, 140 and each of the servers 150, 160 described above may be realized by the computer device 200 shown in FIG.

Such a computing device 200 may include memory 210, processor 220, communication interface 230, and input/output interface 240, as shown in FIG. The memory 210 is a computer-readable storage medium and may include random access memory (RAM), read only memory (ROM), and permanent mass storage devices such as disk drives. Here, a permanent mass storage device such as a ROM or disk drive may be included in computer device 200 as a separate permanent storage device separate from memory 210 . Also stored in memory 210 may be an operating system and at least one program code. Such software components may be loaded into memory 210 from a computer-readable medium separate from memory 210 . Such other computer-readable recording media may include computer-readable recording media such as floppy drives, disks, tapes, DVD/CD-ROM drives, memory cards, and the like. In other embodiments, software components may be loaded into memory 210 through communication interface 230 that is not a computer-readable medium. For example, software components may be loaded into memory 210 of computing device 200 based on computer programs installed by files received over network 170 .

Processor 220 may be configured to process computer program instructions by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to processor 220 by memory 210 or communication interface 230 . For example, processor 220 may be configured to execute received instructions according to program code stored in a storage device, such as memory 210 .

Communication interface 230 may provide a function for computer device 200 to communicate with another device (eg, the recording device described above) via network 170 . As an example, processor 220 of computing device 200 can transmit requests, commands, data, files, etc. generated according to program code recorded in a recording device such as memory 210 to other devices via network 170 under the control of communication interface 230 . device. Conversely, signals, instructions, data, files, etc. from other devices may be received by computing device 200 through communication interface 230 of computing device 200 over network 170 . Signals, instructions, data, etc. received through the communication interface 230 may be transmitted to the processor 220 and the memory 210, and files, etc. may be stored in a recording medium (the permanent recording device described above) that the computing device 200 may further include. may be recorded in

Input/output interface 240 may be a means for interfacing with input/output device 250 . For example, input devices may include devices such as a microphone, keyboard, or mouse, and output devices may include devices such as displays, speakers, and the like. As another example, input/output interface 240 may be a means for interfacing with a device that integrates functionality for input and output, such as a touch screen. Input/output device 250 may be one device with computing device 200 .

Also, in other embodiments, computing device 200 may include fewer or more components than the components of FIG. However, most prior art components need not be explicitly shown in the figures. For example, computing device 200 may be implemented to include at least some of the input/output devices 250 described above, and may also include other components such as transceivers, databases, and the like.

Specific embodiments of methods and apparatus for scoring accounts on the timeline service are described below.

In this specification, a social platform means a communication space on the Internet based on relationships like a messenger. For example, the social platform provides a chat service, a timeline service, a call service, etc. to a user who has established a friend relationship through a process of adding a friend using a phone number or an ID. However, it should not be limited to this, and can be expanded to any space where communication is possible through various communication functions based on a kind of relationship.

FIG. 3 is a block diagram illustrating exemplary components that a processor of a computing device may include in accordance with one embodiment of the present invention, and FIG. 4 illustrates components executed by the computing device in accordance with one embodiment of the present invention. 10 is a flow chart showing an example of an account scoring method that can be used.

The computing device 200 according to the present embodiment may provide social services through dedicated applications or connection to web/mobile sites. The computing device 200 may be configured with a computer-implemented account scoring system. For example, the account scoring system may be implemented in the form of an independently operating program, or may be configured in the form of an in-app of a specific application to operate on the specific application. It may be implemented as

The processor 220 of the computer device 200 may include an account score calculation unit 310 and an account recommendation unit 320 as shown in FIG. 3 as components for executing the account scoring method described below. Depending on the embodiment, components of processor 220 may be selectively included or excluded from processor 220 . Also, depending on the embodiment, the components of processor 220 may be separated or merged to represent the functionality of processor 220 .

Such processor 220 and components of processor 220 may control computing device 200 to perform the steps involved in the account scoring method described below. For example, processor 220 and components of processor 220 may be implemented to execute instructions according to the code of an operating system and the code of at least one program contained in memory 210 .

Here, the components of processor 220 may be representations of different functions performed by processor 220 according to instructions provided by program code recorded in computing device 200 . For example, the account scoring unit 310 may be used as a functional representation of the processor 220 controlling the computing device 200 according to the above-described instructions so that the computing device 200 scores accounts on the timeline service.

Processor 220 may read the necessary instructions from memory 210 loaded with instructions associated with the control of computing device 200 . In this case, the read instructions may include instructions for controlling processor 220 to perform the account scoring method described below.

The steps involved in the account scoring method described below may be performed in a different order than shown in the figures, some of the steps may be omitted, or additional steps may be included. may

The steps involved in the account scoring method may be performed, for example, on a client with Messenger installed, and some of the steps may be performed on the server 150 in some embodiments.

Referring to FIG. 4, in step 410, the account score calculation unit 310 calculates the account score for each account on the social platform by using the follower increase/decrease for the account and the user reaction to the posts owned by the account. may As used herein, account score means the score for an author who has an account on the social platform. Below, the author score and the account score are described as synonyms.

In this embodiment, author scores are used for recommendation logic applicable to the timeline service. This embodiment is not a logic that continuously recommends guaranteed accounts (accounts that are already popular), but accounts that are attracting attention recently and have many posts that users seem to like. Realize the recommendation logic for advancing in the direction of recommendation.

The account score calculation unit 310 scores the degree of recent follower increase/decrease of the account and the user reaction to the post held by the account as factors influencing the judgment of the popular account. Calculate points.

In operation 420, the account recommending unit 320 may select a popularity-based recommended account based on the author score of each account. The account recommendation unit 320 may use author scores as criteria for selecting popular accounts within the timeline service. In other words, the account recommendation unit 320 may recommend accounts with high author scores as popular accounts when recommending accounts in the timeline service.

The author score can be used for various recommendation logics applicable to the timeline service, such as recommendation of accounts as well as recommendation of posts.

FIG. 5 is a flow chart showing an example of a process for calculating an author score in one embodiment of the present invention.

As an example, the process of FIG. 5 may be included in step 410 described above, and some of the steps may be performed on server 150 in some embodiments.

Referring to FIG. 5, in step 501, the account score calculator 310 may calculate the follower score for the account. The account score calculation unit 310 may calculate the degree of follower increase/decrease for the account as a measure. At this time, the account score calculation unit 310 may determine the degree of follower increase/decrease using a predetermined period as a cycle. For example, the account score calculator 310 may calculate the follower score for the account on a weekly basis. Also, the account score calculation unit 310 may calculate the follower score from the time when the number of followers of the account reaches or exceeds a predetermined number. For accounts with less than a certain number of followers, a default score (e.g. 0) is applied without calculating the score, and a certain number will be applied from the time the cumulative number of followers reaches a certain number. The follower score may be calculated by comparing with the previous number of followers on a periodic basis.

In operation 502, the account score calculation unit 310 may calculate a user reaction score for all posts owned by the account. Posts are created and registered with various types of contents such as text, image, video, and audio. The account score calculator 310 may collect logs for posts and score user reactions based on the collected logs.

In particular, the account score calculation unit 310 may calculate the user reaction score by adding the click score obtained by scoring the click log in which the user reaction is input and the video score obtained by scoring the video consumption time of the video post. . At this time, the account score calculation unit 310 may give one of the click score and the video score a higher weight than the other one. For example, a video score may be given a weight of 1 and a click score may be given a weight of 2.

In step 503, the account score calculation unit 310 may calculate an author score by summing the follower score and the user reaction score of the account. At this time, the account score calculation unit 310 may give one of the follower score and the user reaction score a higher weight than the other. For example, a follower score may be given a weight of 1 and a user reaction score may be given a weight of 2.

Since the user reaction score is the sum of the click score and the video score, accounts that do not have a video post will have a relatively lower author score than accounts that have a video post. .

FIG. 6 is a diagram showing an example of the process of calculating click scores in one embodiment of the present invention.

The account score calculation unit 310 may collect click logs for posts that are open to the public among the posts held by the account, and may collect click logs during a predetermined period of time. A click score may be calculated by scoring the click log obtained.

The click log may include click types that indicate actual reaction and click types that indicate degree of interest. The click score can be calculated by assigning a predetermined score to each click type.

Referring to FIG. 6, a score 620 is predefined for each click type 610, and the account score calculator 310 accumulates the corresponding score 620 for each click type 610 included in the click log. A click score may be calculated.

The click type 610 may be classified into a positive type with additional points and a negative type with negative points. For example, history of viewing comment screens, history of entering comments, history of entering "likes", history of viewing profile screens, history of viewing or saving content such as videos and images attached to posts, posts 620 points may be added to the history of sharing, the history of viewing thumbnail screens, etc., the history of deleting likes, the history of deleting comments, the history of reporting accounts or posts, and the history of reporting accounts or posts. A score of 620 may be subtracted from the history of concealment processing. As an example, the subtraction points may be given a higher number than the addition points, so that when the subtraction points are given, the accumulated points will be significantly reduced. Here, the score 620 for each click type 610 is merely an example, and can be changed and operated as desired.

FIG. 7 is a flow chart showing an example of the process of calculating a video score in one embodiment of the invention.

As an example, the process of FIG. 7 may be included in step 502 described above, and some of the steps may be performed on server 150 in some embodiments.

Referring to FIG. 7, in step 701, the account score calculator 310 may collect logs for video posts among posts owned by the account. At this time, the account score calculator 310 may collect a video consumption log to score the time the user has consumed the video. For the above video posts, only logs whose video consumption time is equal to or longer than the second time (T2, T2≦T1) may be collected. For example, in order to eliminate logs that are automatically played at the same time as being displayed, a video post with a playback time of 3 seconds or longer may be used with a video consumption time of 3 seconds or longer.

In step 702, the account score calculation unit 310 may calculate the video consumption rate and the video duration contribution rate of the corresponding account based on the account's video post log.

When calculating an account's Video Score, the account's video consumption rate must be reflected. The video consumption rate is the rate at which the user actually plays the video, and means the rate of the time that the user actually consumes the video with respect to the video playback time. The account score calculator 310 may calculate a video consumption rate for each video log, or may use a final value obtained by averaging video consumption rates calculated for all video logs.

Accounts on social platforms are divided into author-type accounts that only create short-format video posts of less than a certain length, and author-type accounts that only create long-format video posts of a certain length or more. type accounts, and author type accounts that create both short and long format video posts.

When calculating an account's video score, we need to reflect the contribution of the video duration aspect by the account's author type. Video duration contribution rate is the video consumption of a user consuming videos of a specific account in a certain period of time (e.g., one month) compared to the video consumption time of the user consuming videos of the entire account. It means the proportion of time. Considering only video consumption time, long format videos have disadvantages, so after considering the video duration contribution rate, which is the ratio of the total video consumption time of one account to the total video consumption time of all accounts may be used to calculate the video score.

Author-type accounts that have both short-format and long-format video posts have a higher video duration contribution than other accounts, and have more video posts regardless of short-format or long-format. It can be said that the more uploading an account is, the more active and popular the account is.

The account score calculation unit 310 may obtain a video score reflecting a uniform score considering the author type of the account according to the video duration contribution rate.

In step 703, the account score calculator 310 may calculate the video score of the corresponding account using the account's video consumption rate and video duration contribution rate. The account score calculator 310 multiplies the video consumption rate of the account by the video duration contribution rate to calculate the video score. may additionally be multiplied by the maximum video score. Also, the account score calculation unit 310 may apply a final value obtained by adding a predetermined value to the video duration contribution rate as the video duration contribution rate. If there are many accounts whose video duration contribution is less than 1%, a certain value (eg, 1) may be added when calculating the score to prevent the video duration contribution rate from approaching zero.

Therefore, the account score calculator 310 may calculate the author score of the corresponding account by adding up the follower score, click score, and video score of the account. At this time, the account score calculator 310 may apply scaling, for example, robust scaling, to match the units of the scores.

The above-mentioned video score may be used together with other scores to calculate the author score, which is the criteria for selecting popular accounts, and may be used alone for recommendation logic and ranking logic applicable to social platforms, depending on the embodiment. may be Besides social platforms, video scores can also be applied to recommendation logic and ranking logic applicable to video platforms that provide video sharing services.

FIG. 8 is a flowchart illustrating an example process of configuring an account recommendation pool in accordance with one embodiment of the present invention.

As an example, the process of FIG. 8 may be included in step 420 described above, and some of the steps may be performed on server 150 in some embodiments.

Referring to FIG. 8, in step 801, the account recommendation unit 320 selects at least one recommended account (hereinafter referred to as a 'follow-based recommended account') based on follow relevance indicating a registration relationship in the timeline service. You may For example, the account recommending unit 320 may select following accounts that the user is following as follow-based recommended accounts. For example, when user A follows user B and user C, user B follows user D and user E, and user C follows user F, user A follows user D and user E. , may recommend user F. As another example, the account recommending unit 320 may select an account similar to the account followed by the user as a follow-based recommended account. For example, user A follows user B and user C, user B has many followers (co-follows) in common with user D, and user C has many followers in common with user E. D and user E may be recommended. As another example, the account recommending unit 320 may select an account that the user does not follow among the accounts that follow the user, that is, an account that does not have a mutual follow relationship with the user as a follow-based recommended account. good. For example, when user A does not follow user D who follows user A, user D may be recommended to user A.

In step 802, the account recommending unit 320 may select at least one recommended account (hereinafter referred to as a 'popularity-based recommended account') using the author score of each account in the timeline service. The account recommending unit 320 may select a popularity-based recommended account based on the author's score, which is obtained by scoring the degree of follower increase/decrease for the account and the user reaction to the post of the account. The account recommendation unit 320 may select an account with a high author score as a popularity-based recommended account. In other words, the account recommending unit 320 selects an account active in the timeline service that has received many positive responses from other users and has recently rapidly increased the number of followers as a popularity-based recommended account. You may

In step 803, the account recommending unit 320 may construct a recommendation pool including follow-based recommended accounts and popularity-based recommended accounts as relationship-based recommendations for users. Referring to FIG. 9 , the account recommending unit 320 may configure a recommendation pool 900 by mixing a follow-based recommended account 910 and a popularity-based recommended account 920 in order to induce follow of a new account. For example, the account recommending unit 320 may configure the recommendation pool 900 by mixing the follow-based recommended accounts 910 and the popularity-based recommended accounts 920 at a predetermined fixed ratio. For example, the account recommending unit 320 may maintain the follow-based recommended account 910 and the popularity-based recommended account 920 at a ratio of 50:50. At this time, following accounts belonging to the follow-based recommended account 910, accounts similar to the following accounts, and accounts not in a mutual follow relationship are divided at a ratio of approximately 16.6:16.6:16.6, The general accounts, official accounts, and influencer accounts belonging to the popularity-based recommended accounts 920 may be divided at a ratio of 15:15:20. As another example, the account recommending unit 320 may form the recommendation pool 900 by mixing the follow-based recommended accounts 910 and the popularity-based recommended accounts 920 at a controlled ratio. A ratio for configuring the recommendation pool 900 may be determined according to the ratio of each type of account. Assuming that there are 100 total selected accounts, 40 follow-based recommended accounts 910, and 60 popularity-based recommended accounts 920, the follow-based recommended accounts 910 and popularity-based recommended accounts 920 are divided 40:60. The recommendation pool 900 may be configured by adjusting the ratio. The sub-accounts belonging to follow-based recommended account 910 and popularity-based recommended account 920 may likewise be mixed in proportions adjusted by the proportions accounted for by each type of account.

The account recommendation unit 320 includes a recommendation pool including not only popular accounts but also personalized recommended accounts based on the degree of follow-up relevance to the user in order to continuously induce new accounts to be followed within the timeline service. Configure and provide The account recommendation unit 320 may display the accounts of the recommended pool through various channels such as a service screen that provides posts of the following accounts in a timeline type.

10 and 11 are diagrams showing examples of screens displaying the account recommendation list in one embodiment of the present invention.

Referring to FIG. 10, the processor 220 displays an account recommendation list 1020 for inducing follow of a new account on a timeline service screen 1000 in which posts 1010 of accounts followed by the user are displayed in chronological order. may be displayed.

The account recommendation list 1020 may include follow-based recommended accounts and popularity-based recommended accounts, and the processor 220 may sequentially display accounts included in the recommended pool by scrolling the account recommendation list 1020 .

The processor 220 may preferentially fix and display the popularity-based recommended accounts among the accounts included in the recommended pool in upper slots of the account recommendation list 1020 . Popularity-based recommended accounts are expected to have a higher CTR (click through rate) than other accounts, and are preferentially arranged to promote interest. At this time, if there is no follow-based recommended account in the recommendation pool, the popularity-based recommended account may monopolize the account recommendation list 1020 .

For each account included in the account recommendation list 1020, a 'Follow' menu 1021 for follow setting, which is a registration relationship, may be provided.

Also, for each account included in the account recommendation list 1020, a 'remove' menu 1022 may be provided for removing the corresponding account from the recommendation pool. Accounts removed from the recommendation pool using the “Remove” menu 1022 may be excluded from recommendations for a certain period of time (eg, seven days).

A “more” menu 1023 may be provided in connection with the account recommendation list 1020, and when the “more” menu 1023 is selected, an account recommendation list page 1100 may be navigated to, as shown in FIG. .

The processor 220 may provide the entire account recommendation list 1110 included in the recommendation pool through the account recommendation list page 1100 . Each account included in the overall account recommendation list 1110 of the recommended account list page 1100 has a 'Follow' menu 1021 for setting follow-up, which is a registration relationship, and a 'Remove' menu for removing the corresponding account from the recommendation pool. 1022 and the like may be included.

The processor 220 recommends accounts that the user is likely to follow through various paths within the timeline service. At this time, the recommended accounts may include popularity-based recommended accounts selected based on the author's score.

In this embodiment, in order to induce new accounts to follow within the timeline service, it is possible to provide personalized relationship-based recommendations according to follow relevance. An author score obtained by converting the degree of increase or decrease and the user reaction score into points may be applied as an additional recommendation condition.

In addition to selecting popular accounts, the account author score can be used for various recommendation logic such as post recommendation and friend recommendation in the timeline service.

As described above, according to the embodiment of the present invention, in the timeline service, an account can be scored based on the degree of follower increase/decrease for the account and the user's reaction to the account's post, and can be used for various recommendation logics. . In particular, according to an embodiment of the present invention, video consumption time can be scored according to user reactions to video posts held by accounts for account ranking. That is, in a social platform, an account can be scored by using the degree of increase or decrease of followers for the account and user reaction to posts of the account.

Also, within social platforms, account scores can be used as criteria for selecting popular accounts.

Also, the video consumption time can be scored according to the user's reaction to the video post held by the account.

The apparatus described above may be realized by hardware components, software components, and/or a combination of hardware and software components. For example, the devices and components described in the embodiments include processors, controllers, arithmetic logic units (ALUs), digital signal processors, microcomputers, field programmable gate arrays (FPGAs), programmable logic units (PLUs), microprocessors, Or may be implemented using one or more general purpose or special purpose computers, such as various devices capable of executing and responding to instructions. The processing unit may run an operating system (OS) and one or more software applications that run on the OS. The processor may also access, record, manipulate, process, and generate data in response to executing software. For convenience of understanding, one processing device may be described as being used, but those skilled in the art may recognize that the processing device may include multiple processing elements and/or multiple types of processing elements. You can understand that. For example, a processing unit may include multiple processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include computer programs, code, instructions, or a combination of one or more of these, to configure a processor to operate at its discretion or to independently or collectively instruct a processor. You can Software and/or data may be embodied in any kind of machine, component, physical device, computer storage medium, or device for interpretation by, or for providing instructions or data to, a processing device. good. The software may be stored and executed in a distributed fashion over computer systems linked by a network. Software and data may be recorded on one or more computer-readable recording media.

The method according to the embodiments may be embodied in the form of program instructions executable by various computer means and recorded on a computer-readable medium. Here, the medium may record the computer-executable program continuously or temporarily record it for execution or download. In addition, the medium may be various recording means or storage means in the form of a combination of single or multiple hardware, and is not limited to a medium directly connected to a certain computer system, distributed over a network. It may exist as Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROM, RAM, flash memory, etc., and may be configured to store program instructions. Other examples of media include recording media or storage media managed by application stores that distribute applications, sites that supply or distribute various software, and servers.

As described above, the embodiments have been described based on the limited embodiments and drawings, but those skilled in the art will be able to make various modifications and variations based on the above description. For example, the techniques described may be performed in a different order than in the manner described and/or components such as systems, structures, devices, circuits, etc. described may be performed in a manner different than in the manner described. Appropriate results may be achieved when combined or combined, opposed or substituted by other elements or equivalents.

Accordingly, different embodiments that are equivalent to the claims should still fall within the scope of the appended claims.

220: Processor 310: Account Score Calculation Unit 320: Account Recommendation Unit

Claims (18)

A method implemented on a computing device, comprising:
The computing device includes at least one processor configured to execute computer readable instructions contained in memory;
The method includes:
said at least one processor collecting logs for video posts among posts held by an account on a social platform; and said at least one processor consuming video based on video duration according to said logs. scoring time to determine a video score for said account. Calculating the video score includes:
The method of claim 1, wherein the video score is calculated using the ratio of the video duration of the account to the total video duration of all accounts. Calculating the video score includes:
Video consumption rate, which is the ratio of the time actually consumed by the user to the playback time of videos owned by the account, and video duration, which is the ratio of the video duration of the account to the total video duration of all accounts 2. The method of claim 1, wherein a contribution rate is used to calculate the video score. Collecting logs for the video post includes:
2. The method according to claim 1, characterized by collecting a log that the user's video consumption time for the video post is greater than or equal to a certain period of time. Collecting logs for the video post includes:
2. The method of claim 1, further comprising: collecting a log of a video consumption time of a second hour or longer by a user for a video post having a video playing time of a first hour or longer. The method includes:
The at least one processor further comprising utilizing the video score for at least one of recommendation logic and ranking logic within a timeline service, which is one of the functions included in the social platform. 1. The method according to 1. The method includes:
the at least one processor recommending at least one account through a timeline service that is one of the functions included in the social platform;
The recommending step includes:
2. The method of claim 1, comprising using the video score to select a popularity-based recommended account. The recommending step includes:
selecting a follow-based recommended account based on a follow relevance indicating a registration relationship in the timeline service; and forming a recommend pool by mixing the popularity-based recommended account and the follow-based recommended account. 8. The method of claim 7, further comprising: A method implemented on a computing device, comprising:
The computing device includes at least one processor configured to execute computer readable instructions contained in memory;
The method includes:
the at least one processor calculating a video score for each account using the ratio of each account's video duration to the total video duration of all accounts on the video platform; and utilizing said video score in at least one of recommendation logic and ranking logic within a video platform. A computer program for causing a computer device to perform the method according to any one of claims 1 to 9. A computer device,
at least one processor configured to execute computer readable instructions contained in memory;
The at least one processor
Collect logs for video posts among posts held by accounts on social platforms;
The computer device calculates a video score of the account by scoring the video consumption time based on the video duration of the log. The at least one processor
12. The computer device of claim 11, wherein the ratio of the video duration of the account to the total video duration of all accounts is used to calculate the video score. The at least one processor
Video consumption rate, which is the ratio of the time actually consumed by the user to the playback time of videos owned by the account, and video duration, which is the ratio of the video duration of the account to the total video duration of all accounts 12. The computer device of claim 11, wherein a contribution rate is used to calculate the video score. The at least one processor
12. The computer apparatus according to claim 11, wherein a log is collected in which the user's video consumption time for the video post is longer than or equal to a certain period of time. The at least one processor
12. The computer apparatus according to claim 11, wherein a log of a video consumption time of a second hour or more by a user is collected for a video post having a video playback time of the first hour or more. The at least one processor
12. The computing device of claim 11, wherein the video score is used for at least one of recommendation logic and ranking logic within a timeline service, which is one of the functions included in the social platform. . The at least one processor
11. Selecting a popularity-based recommended account using the video score to recommend at least one account through a timeline service, which is one of functions included in the social platform. The computer device as described in . The at least one processor
selecting a follow-based recommended account based on the degree of follow-up relevance that indicates the registration relationship in the timeline service;
18. The computer device of claim 17, wherein the popularity-based recommended accounts and the follow-based recommended accounts are mixed to form a recommendation pool.

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