Recommendation device and recommendation method

Abstract

This recommendation device comprises: a reception unit that receives, from a plurality of users, information relating to excitement grown in a plurality of portions by the use of content including the plurality of portions on the time series; an estimation unit that, between a first user who wants to estimate a target to follow and one or more second users using the same content as the first user, compares information relating to excitement for each portion of the same content, and estimates a target to follow from among the one or more second users on the basis of the comparison result; and a recommendation unit that recommends the target to follow, which is estimated by the estimation unit, to the first user.

Description

Recommendation device and recommendation method

[0001] One aspect of the present disclosure relates to a recommendation device and a recommendation method.

[0002] Patent Document 1 discloses a technique for estimating the excitement (audience excitement) of each scene in a video.

[0003] JP 2010-161489 A

[0004] In recent years, it has become common for users to follow users with similar tastes when using content services such as video viewing, and to decide which content to use based on the content (videos, etc.) that the users have used. However, even if users are watching the same video, their favorite scenes in the video are individual, making it difficult to follow users simply because they have watched the same video.

[0005] The present disclosure has been made in consideration of the above-described circumstances, and provides a recommendation device and a recommendation method that can appropriately recommend users with similar tastes in content usage as targets to follow.

[0006] A recommendation device according to one aspect of the present disclosure includes a reception unit that receives, from a plurality of users, information regarding the popularity of each part resulting from the use of content including a plurality of parts arranged in chronological order; an estimation unit that compares information regarding the popularity of each part of the same content between a first user who wishes to estimate a follow target and one or more second users who have used the same content as the first user, and estimates a follow target from among the one or more second users based on the comparison result; and a recommendation unit that recommends the follow target estimated by the estimation unit to the first user.

[0007] A recommendation device according to one aspect of the present disclosure estimates and recommends users to follow based on the results of a comparison of information regarding the popularity of each part of the same content along a timeline, rather than simply based on whether the same content was used. This configuration makes it possible to appropriately recommend users who are excited about the same points (same parts), i.e., who have similar tastes in content, as users to follow. As described above, a recommendation device according to one aspect of the present disclosure can appropriately recommend users with similar tastes in content usage as users to follow, and can appropriately recommend content that the user is likely to like. This allows users to follow users who have similar content preferences, thereby appropriately expanding the scope of their viewing.

[0008] According to the present disclosure, it is possible to appropriately recommend users with similar tastes in content usage as targets to follow.

[0009] FIG. 1 is a diagram illustrating an overview of a recommendation device according to this embodiment. FIG. 2 is a diagram illustrating the functional configuration of a recommendation device according to this embodiment. FIG. 3 is a diagram illustrating a follow target estimation process. FIG. 4 is a diagram illustrating a follow target estimation process. FIG. 5 is a diagram illustrating a recommendation image. FIG. 6 is a flowchart illustrating processing executed by the recommendation device. FIG. 7 is a diagram illustrating an example of the hardware configuration of a recommendation device.

[0010] Hereinafter, the embodiments will be described in detail with reference to the drawings. In the description, the same elements or elements having the same functions are denoted by the same reference numerals, and redundant description will be omitted.

[0011] FIG. 1 is a diagram illustrating an overview of a recommendation device according to this embodiment. The recommendation device is a device that estimates other users with similar content usage tendencies as follow targets and recommends the follow targets to a user. Such recommendations meet the needs of users who want to follow users with similar preferences and decide on content to use based on the user's content usage history. The content here may be anything provided by a provider that includes multiple parts in chronological order. For example, the content may be a video that includes multiple scenes in chronological order. The following describes an example in which the content is a video that includes multiple scenes in chronological order, but is not limited to this. The content may also be, for example, music that includes multiple parts in chronological order (details will be described later).

[0012] The recommendation device compares the degree of excitement for each scene of content (video) among multiple users, and estimates and recommends other users with similar levels of excitement for each scene as a target to follow. In the two graphs shown in FIG. 1, the horizontal axis represents time and the vertical axis represents excitement. The excitement level is assumed to be derived, for example, from the number of repetitions. In the example shown in FIG. 1, the excitement levels for each scene of user A and user B who watched video X are shown in chronological order. As shown in FIG. 1, it is assumed that the excitement levels for each scene of user A and user B who watched the same video X are very similar. In this case, the recommendation device determines that it would be beneficial for user A to follow user B, and recommends user B to user A as a target to follow. The recommendation device may similarly recommend user A to user B as a target to follow.

[0013] Fig. 2 is a diagram showing the functional configuration of a recommendation device 20 according to this embodiment. Fig. 2 shows a recommendation system including the recommendation device 20 and a plurality of terminals 10. In the recommendation system, the plurality of terminals 10 and the recommendation device 20 are configured to be able to communicate with each other via a network including a wireless communication network and a fixed communication network.

[0014] The terminal 10 is a terminal used by a user to watch a video. The terminal 10 may be, for example, a personal computer, a smartphone, a tablet terminal, a feature phone, a server device, a game console, or the like. Although FIG. 2 illustrates only one terminal 10, the recommendation system actually includes multiple terminals 10. When a user watches a video, the terminal 10 transmits video information (details will be described later) including various information related to the video viewing to the recommendation device 20. The timing for transmitting the video information from the terminal 10 to the recommendation device 20 may be when the user finishes watching the video, while the user is watching the video (in real time), or at predetermined time intervals (periodic timing). When video information is transmitted at predetermined time intervals, video information related to all videos viewed between the previous transmission and the current transmission may be transmitted together.

[0015] The recommendation device 20 includes, as functional components, a reception unit 21, an estimation unit 22, a recommendation unit 23, and a storage unit 24.

[0016] The reception unit 21 receives, from each of the terminals 10 (i.e., multiple users), video information related to a video viewed by the user. The video information may include information indicating the user who viewed the video, information indicating the video, and information related to the excitement of multiple scenes included in the video. In this way, the reception unit 21 receives information related to the excitement of each scene (portion) resulting from viewing (using the content) a video including multiple scenes (portions) in chronological order.

[0017] The information indicating the user who viewed the video is information that can uniquely identify the user who viewed the video via the terminal 10. The information indicating the user who viewed the video may be, for example, a user ID that uniquely identifies the user.

[0018] The information indicating a video is information that can uniquely identify a video that the user has viewed. The information indicating a video may be, for example, a video ID that can uniquely identify the video, or the video itself.

[0019] The information regarding the excitement of multiple scenes included in a video is information that enables an estimation of the level of excitement for each scene. The receiving unit 21 may receive, as the information regarding the excitement of a scene, at least one of the number of times the corresponding scene is repeated, information regarding the volume of the playback of the corresponding scene, and information regarding the user's reaction to the corresponding scene. The receiving unit 21 may also receive, as the information regarding the excitement, information other than the above, as long as it enables an estimation of the level of excitement for the scene.

[0020] The number of times a corresponding scene has been repeated is information indicating how many times the user has repeated that scene. The number of times a corresponding scene has been repeated may be the number of times it has been repeated within a predetermined period (for example, within a predetermined period of several minutes to several days). It is estimated that the more times a scene has been repeated, the more interested the user is in that scene and the higher the level of excitement.

[0021] The information about the volume during playback of the corresponding scene may be information indicating the volume itself or information indicating a change in volume over time. The louder the volume, the more interested the user is in the scene and the higher the excitement level is estimated to be. Also, if the volume is gradually increased during the scene, it is estimated that the user is interested in the scene and the excitement level is high.

[0022] The information about the user's reaction to the corresponding scene is information indicating how the user evaluated the scene. The information about the user's reaction may be, for example, information indicating a qualitative evaluation such as "high rating" or "low rating," or information indicating a quantitative evaluation such as a score. It is estimated that the higher the rating, the more interested the user is in the scene and the higher the level of excitement.

[0023] The receiving unit 21 may receive information regarding the excitement of each scene when the same video is viewed multiple times by the same user. That is, even when video information regarding the same video is received multiple times by the same user (when the same user watches the same video multiple times), the receiving unit 21 may receive each piece of video information.

[0024] The reception unit 21 stores the plurality of pieces of video information received from each terminal 10 in the storage unit 24. The storage unit 24 stores the plurality of pieces of video information input from the reception unit 21. The storage unit 24 also stores the estimation results (information indicating the follow target, etc.) by the estimation unit 22, which will be described later.

[0025] The estimation unit 22 compares information regarding excitement for each scene (each part of the content) of the same video between a first user who wishes to estimate a follow target and one or more second users who have watched the same video (used the content) as the first user, and estimates a follow target from among one or more second users based on the comparison results.

[0026] The estimation unit 22 first identifies one or more videos that the first user has previously viewed by referring to the video information stored in the storage unit 24. Then, the estimation unit 22 identifies one or more second users who have viewed the identified videos by referring to the video information.

[0027] The estimation unit 22 then compares information about excitement for each scene of the same video between the first user and each second user. The estimation unit 22 may derive a numerical value of excitement level from the information about excitement level received by the reception unit 21, and compare the excitement levels. The excitement level may be a normalized value between 0 and 100, for example.

[0028] The estimation unit 22 may derive the absolute value of the difference in the degree of excitement between the first user and the second user for each scene in the same video, and prioritize the second user with a smaller absolute value of the difference in the degree of excitement as the follow target. FIG. 3 is a diagram illustrating the follow target estimation process. In the example shown in FIG. 3, the absolute value of the difference in the degree of excitement between the first user "User A" and the second user "User B" is derived for each scene ("scene from 0 to 10 seconds," "scene from 11 to 20 seconds," "scene from 21 to 30 seconds," "scene from 31 to 40 seconds," "scene from 41 to 50 seconds," and "scene from 51 to 60 seconds" in the video).

[0029] The estimation unit 22 may derive the sum of the absolute values ​​of the differences in the excitement levels of each scene, and estimate that a second user whose sum is smaller than a predetermined value is a target to be followed. For example, in the example shown in FIG. 3, assume that the predetermined value is set to "50." Since the sum of the absolute values ​​of the differences in the excitement levels of each scene is now "45," in this case, the estimation unit 22 estimates that the second user, "User B," is a target to be followed (a target to be recommended to "User A").

[0030] For a second user who has multiple "identical videos" (videos watched by both the first user and the second user), the estimation unit 22 may derive the sum of the absolute values ​​of the differences in the excitement levels of each scene for each of the multiple "identical videos," and estimate that the second user whose average value of the multiple sums is smaller than a predetermined value is the one to be followed.

[0031] The estimation unit 22 may derive the amount of change in the excitement level (the amount of change between the multiple patterns) when a first user and a second user watch the same video multiple times and there are multiple patterns of excitement level for each scene in the same video. FIG. 4 is a diagram illustrating the follow target estimation process. In the example shown in FIG. 4, for example, "-20" is displayed for the "0-10 second scene" of "User A," who is a first user. This indicates that the excitement level of "User A" changed by "-20" from the first to the second viewing for the "0-10 second scene" of a certain video. Similarly, "-10" is displayed for the "0-10 second scene" of "User B," who is a second user. This indicates that the excitement level of "User B" changed by "-10" from the first to the second viewing for the "0-10 second scene" of a certain video.

[0032] The estimation unit 22 may then derive the absolute value of the difference in the amount of change between the first user and the second user for each scene, and estimate as the second user to be followed, the second user for whom the sum of the absolute values ​​of the difference in the amount of change for each scene is smaller than a predetermined value. For example, in the example shown in FIG. 4 , the absolute value of the difference in the amount of change for the "scene from 0 to 10 seconds" is "10," the absolute value of the difference in the amount of change for the "scene from 11 to 20 seconds" is "0," the absolute value of the difference in the amount of change for the "scene from 21 to 30 seconds" is "10," the absolute value of the difference in the amount of change for the "scene from 31 to 40 seconds" is "20," the absolute value of the difference in the amount of change for the "scene from 41 to 50 seconds" is "0," and the absolute value of the difference in the amount of change for the "scene from 51 to 60 seconds" is "10." In this case, the sum of the absolute values ​​of the difference in the amount of change for each scene is "50." Therefore, when the predetermined value is set to a value greater than "50," the estimation unit 22 estimates the second user, "User B," as the follow target (the follow target to be recommended to "User A"). According to this estimation method, it is possible to estimate, as the follow target, users whose changes in excitement level when watching the same video multiple times are similar (users whose sensibilities are more similar).

[0033] The estimation unit 22 stores the estimation result in the storage unit 24. The estimation result includes information indicating a second user who is a follow target to be recommended to the first user. The estimation result may also include at least one of information indicating the degree of similarity in viewing tendencies between the follow target and the first user and information indicating scenes with similar information regarding excitement. The information indicating the degree of similarity in viewing tendencies may be, for example, a score indicating how similar the viewing tendencies of the second user who is a follow target are to the viewing tendencies of the first user. The estimation unit 22 may derive such a score (information indicating the degree of similarity in viewing tendencies) based on the above-mentioned sum of absolute values ​​of differences in excitement levels or the sum of absolute values ​​of differences in the amount of change in excitement levels. For example, instead of using the sum of absolute values ​​of differences in excitement levels as the score, the estimation unit 22 may convert the magnitude of the sum to a standard deviation value across all users and subtract the result from 100 to obtain the score. Information indicating scenes with similar information regarding excitement may be, for example, information indicating a scene in which the excitement level for the first user and the second user is above a predetermined threshold (i.e., a scene that both the first user and the second user like), and in which the absolute value of the difference in excitement level between the first user and the second user is below a predetermined value.

[0034] The recommendation unit 23 identifies the follow target estimated by the estimation unit 22 by referring to the estimation results stored in the storage unit 24, and recommends the follow target to the first user. The recommendation unit 23 may present to the first user at least one of information indicating the degree of coincidence of viewing tendencies with the follow target (similar user score) and information indicating scenes with similar information regarding excitement, by referring to the estimation results stored in the storage unit 24.

[0035] FIG. 5 is a diagram showing a recommendation image (a screen image displayed on the terminal 10). In the example shown in FIG. 5, "Mr. A" and "Mr. B" are recommended as follow targets for a certain first user. Furthermore, "90" is displayed as the similar user score for "A," and an image of a scene with similar information about excitement (and where both the first user and "Mr. A" have a high level of excitement) is displayed. Similarly, "85" is displayed as the similar user score for "B," and an image of a scene with similar information about excitement (and where both the first user and "Mr. B" have a high level of excitement) is displayed. Along with this information, buttons for following "Mr. A" and "Mr. B" are displayed on the screen, so that the first user who has checked the information can immediately follow "Mr. A" and "Mr. B."

[0036] Next, the process executed by the recommendation device 20 will be described with reference to Fig. 6. Fig. 6 is a flowchart showing the process executed by the recommendation device 20.

[0037] 6 , first, the recommendation device 20 receives video information (multiple pieces of video information) from each terminal 10 (step S1). The video information includes information indicating the user who viewed the video, information indicating the video, and information regarding the excitement of multiple scenes included in the video.

[0038] Next, the recommendation device 20 compares information about the excitement of each scene of the same video between multiple users, and estimates the users to follow (users with similar viewing tendencies) based on the comparison results (step S2).

[0039] Next, the recommendation device 20 recommends information indicating the estimated follow targets (users with similar viewing tendencies) to the first user (step S3).

[0040] Next, the effects of the recommendation device 20 according to this embodiment will be described.

[0041] The recommendation device 20 includes a reception unit 21 that receives, from a plurality of users, information regarding the excitement in each part resulting from the use of content including a plurality of parts arranged in a time series; an estimation unit 22 that compares information regarding the excitement for each part of the same content between a first user who wishes to estimate a follow target and one or more second users who have used the same content as the first user, and estimates a follow target from one or more second users based on the comparison result; and a recommendation unit 23 that recommends the follow target estimated by the estimation unit 22 to the first user.

[0042] The recommendation device 20 according to this embodiment estimates and recommends users to follow based on the results of a comparison of information regarding the popularity of each part of the same content along a timeline, rather than simply based on the use of the same content. This configuration makes it possible to appropriately recommend users who are excited about the same points (same parts), i.e., who have similar tastes in content, as users to follow. As described above, the recommendation device 20 according to this embodiment can appropriately recommend users who have similar tastes in content usage as users to follow, and can appropriately recommend content that the user is likely to like. This allows users to follow users who have similar content preferences, thereby appropriately widening the scope of their viewing.

[0043] The content may be a video, and the multiple time-series portions may be multiple time-series scenes. With this configuration, it is possible to appropriately recommend a subject to follow in the field of "videos," where it has become common in recent years to determine viewing content based on the viewing history of the subject to follow.

[0044] The estimation unit 22 may derive a numerical level of excitement from the information related to excitement received by the reception unit 21, derive the absolute value of the difference in excitement level between the first user and the second user for each scene of the same video, and prioritize second users with smaller absolute values ​​of the difference in excitement level as targets to be followed. With this configuration, users with a small difference in the quantified level of excitement and similar viewing tendencies can be appropriately selected as targets to be followed.

[0045] The estimation unit 22 may derive the sum of the absolute values ​​of the differences in the excitement levels of each scene, and estimate that a second user whose sum is smaller than a predetermined value is a target to be followed. With this configuration, it is possible to appropriately select users who have similar viewing tendencies not only for one scene but for the entire video as a target to be followed.

[0046] For a second user who has multiple identical videos, the estimation unit 22 may derive a total value for each of the multiple identical videos and estimate a second user whose average value of the multiple total values ​​is smaller than a predetermined value as a follow target. With this configuration, even when multiple videos are taken into consideration, users with similar viewing tendencies can be selected as follow targets, and users with even more similar tastes can be selected as follow targets.

[0047] The receiving unit 21 receives information regarding the excitement of each scene when the first user and the second user watch the same video multiple times. The estimation unit 22 derives a numerical excitement level from the excitement level information received by the receiving unit 21, derives the amount of change in excitement level for each scene of the same video viewed multiple times for each of the first user and the second user, derives the absolute value of the difference in the amount of change for each scene between the first user and the second user, and estimates the second user whose total absolute value of the difference in the amount of change for each scene is smaller than a predetermined value as a follow target. When watching a video, the excitement level changes when viewed multiple times, and there are ways to enjoy it only the second time (foreshadowing in a mysterious scene, the meaning of the lyrics of an insert song, simply liking a song, etc.). Therefore, users with similar changes in excitement level when viewed multiple times can be said to have similar sensibilities. By selecting such users as follow targets, users with similar viewing habits can be appropriately recommended as follow targets.

[0048] The recommendation unit 23 may present the first user with at least one of information indicating the degree of similarity in viewing habits with the following target and information indicating scenes with similar information regarding excitement. In this way, by presenting the first user with information for determining whether or not to follow, it is possible to improve the appropriateness and ease of following by the first user.

[0049] The recommendation device according to the present disclosure is not limited to the above embodiment. For example, the above-described video (content) may be a video in the metaverse space. Such a video may be, for example, a video of a live event in the metaverse space. In this case, the reception unit may receive the number of keystrokes by the user as information regarding excitement. At a live event in the metaverse space, it is considered that the tendency of users' keystrokes indicates excitement. By following users who have a similar number of keystrokes, i.e., who are excited in the same way, it becomes possible to follow users who can enjoy a live event with the same enthusiasm, for example, and enjoy the live event in a location close to the user. Such recommendations for followees may be implemented for live broadcast events. The recommendation unit may also recommend a location for the followee.

[0050] Furthermore, content can be something other than video, such as music. For example, if the content is music that can only be viewed by NFT holders, there is less information available for recommendations compared to subscription-based video streaming. In this regard, by making the recommendations of followable targets for such content, it becomes possible to obtain user information that is similar to one's own interests and trace those users to purchase NFTs that suit one's tastes. Furthermore, when searching for private NFT works, it becomes possible to search based on followable targets.

[0051] The recommendation device and recommendation method of the present disclosure have the following configuration.

[0052] [1] A recommendation device comprising: a reception unit that receives, from a plurality of users, information regarding the popularity of each part resulting from the use of content including a plurality of parts arranged in a time series; an estimation unit that compares information regarding the popularity of each part of the same content between a first user who is to be inferred as a follow target and one or more second users who have used the same content as the first user, and infers the follow target from among the one or more second users based on the comparison result; and a recommendation unit that recommends the follow target inferred by the estimation unit to the first user.

[0053] [2] The recommendation device according to [1], wherein the content is a video, and the plurality of time-series parts are a plurality of time-series scenes.

[0054] [3] The recommendation device according to [2], wherein the estimation unit derives a numerical level of excitement from the information relating to the excitement received by the reception unit, derives an absolute value of the difference in excitement level between the first user and the second user for each scene of the same video, and preferentially estimates that the second user having a smaller absolute value of the difference in excitement level is the target to be followed.

[0055] [4] The recommendation device according to [3], wherein the estimation unit derives a sum of absolute values ​​of the differences in the excitement levels of each scene, and estimates that the second user whose sum is smaller than a predetermined value is the second user to be followed.

[0056] [5] The recommendation device described in [4], wherein the estimation unit derives the total value for each of the multiple identical videos for the second user who has multiple identical videos, and estimates that the second user whose average value of the multiple total values ​​is smaller than a predetermined value is the second user to be followed.

[0057] [6] The recommendation device described in [2], wherein the reception unit receives information regarding the excitement level for each scene when the first user and the second user watch the same video multiple times, and the estimation unit derives a numerical value of the excitement level from the information regarding the excitement level received by the reception unit, derives an amount of change in the excitement level for each scene of the same video watched multiple times for each of the first user and the second user, derives an absolute value of a difference in the amount of change for each scene between the first user and the second user, and estimates the second user for whom the total absolute value of the difference in the amount of change for each scene is smaller than a predetermined value to be the follow target.

[0058] [7] The recommendation device according to any one of [2] to [6], wherein the recommendation unit presents to the first user at least one of information indicating a degree of similarity in viewing tendencies with the following target and information indicating scenes with similar information regarding the excitement.

[0059] [8] The recommendation device according to [2], wherein the content is a video in a metaverse space, and the reception unit receives the number of keystrokes by the user as the information related to the excitement.

[0060] [9] The recommendation device according to [1], wherein the content is music.

[0061]

[10] A recommendation method executed by a recommendation device, the recommendation method including: receiving, from a plurality of users, information regarding the popularity of each part resulting from the use of content including a plurality of parts arranged in a time series; comparing the information regarding the popularity for each part of the same content between a first user for whom it is desired to infer a target to follow and one or more second users who have used the same content as the first user, and inferring the target to follow from among the one or more second users based on the comparison result; and recommending the inferred target to follow to the first user.

[0062] The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of hardware and / or software. Furthermore, the method for realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are connected directly or indirectly (e.g., via wire, wirelessly, etc.) and these multiple devices. The functional block may also be realized by combining the single device or multiple devices with software.

[0063] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0064] For example, a recommendation device 20 constituting a recommendation system according to an embodiment of the present disclosure may function as a computer that performs processing of the control method of the present disclosure. FIG. 7 is a diagram illustrating an example of the hardware configuration of the recommendation device 20 according to this embodiment. The recommendation device 20 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, and the like. Note that the recommendation device 20 may be configured as a computer device including at least one processor such as a CPU or GPU, or may be configured as a computer device including multiple processors, or may be configured to include multiple computer devices. The terminal 10 may also have a similar hardware configuration.

[0065] In the following description, the term "device" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the recommendation device 20 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

[0066] Each function of the recommendation device 20 is realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.

[0067] The processor 1001 controls the entire computer by running, for example, an operating system. The processor 1001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, the above-mentioned reception unit 21, estimation unit 22, recommendation unit 23, etc. may be realized by the processor 1001.

[0068] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The program used may be a program that causes a computer to execute at least some of the operations described in the above-described embodiments. For example, the reception unit 21, the estimation unit 22, and the recommendation unit 23 may be implemented by a control program stored in the memory 1002 and running on the processor 1001, and similar implementations may be used for other functional blocks. While the above-described various processes have been described as being executed by one processor 1001, they may also be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may also be transmitted from a network via a telecommunications line.

[0069] The memory 1002 is a computer-readable recording medium and may be configured, for example, by at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a control method according to an embodiment of the present disclosure.

[0070] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0071] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). For example, the above-mentioned reception unit 21, recommendation unit 23, etc. may be realized by the communication device 1004.

[0072] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0073] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0074] The recommendation device 20 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0075] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0076] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0077] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be sent to another device.

[0078] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0079] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0080] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0081] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0082] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0083] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0084] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0085] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0086] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0087] In this disclosure, the terms "Mobile Station (MS)," "user terminal," "User Equipment (UE)," "terminal," and the like may be used interchangeably.

[0088] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other suitable terminology.

[0089] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0090] The terms "connected," "coupled," or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access." As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using one or more wires, cables, and / or printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

[0091] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0092] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0093] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0094] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0095] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0096] 20...recommendation device, 21...reception unit, 22...estimation unit, 23...recommendation unit.

Claims

1. A recommendation device comprising: a reception unit that receives, from a plurality of users, information regarding the popularity of each part resulting from the use of content that includes a plurality of parts arranged in chronological order; an estimation unit that compares information regarding the popularity of each part of the same content between a first user who is to be inferred as a follow target and one or more second users who have used the same content as the first user, and infers the follow target from among the one or more second users based on the comparison results; and a recommendation unit that recommends the follow target inferred by the estimation unit to the first user.

2. The recommendation device according to claim 1, wherein the content is a video, and the plurality of time-series parts are a plurality of time-series scenes.

3. The recommendation device described in claim 2, wherein the estimation unit derives a numerical level of excitement from the information regarding the excitement received by the reception unit, derives the absolute value of the difference in excitement level between the first user and the second user for each scene of the same video, and preferentially estimates that the second user with the smaller absolute value of the difference in excitement level is the target to be followed.

4. The recommendation device according to claim 3, wherein the estimation unit derives the sum of the absolute values ​​of the differences in the excitement levels of each scene, and estimates that the second user whose sum is smaller than a predetermined value is the second user to be followed.

5. A recommendation device as described in claim 4, wherein the estimation unit derives the total value for each of the multiple identical videos for the second user who has multiple identical videos, and estimates that the second user whose average value of the multiple total values ​​is smaller than a predetermined value is the second user to be followed.

6. The recommendation device described in claim 2, wherein the reception unit receives information regarding the excitement level for each scene when the first user and the second user watch the same video multiple times, and the estimation unit derives a numerical level of excitement from the information regarding the excitement level received by the reception unit, derives an amount of change in the excitement level for each scene of the same video watched multiple times for each of the first user and the second user, derives an absolute value of the difference in the amount of change for each scene between the first user and the second user, and estimates that the second user for whom the total absolute value of the difference in the amount of change for each scene is smaller than a predetermined value is the target to be followed.

7. A recommendation device according to any one of claims 2 to 6, wherein the recommendation unit presents to the first user at least one of information indicating the degree of similarity in viewing tendencies with the follow target and information indicating scenes with similar information regarding the excitement.

8. The recommendation device according to claim 2, wherein the content is a video in a metaverse space, and the reception unit receives the number of keystrokes by the user as information related to the excitement.

9. The recommendation device according to claim 1, wherein the content is music.

10. A recommendation method executed by a recommendation device, comprising: receiving, from a plurality of users, information regarding the popularity of each part resulting from the use of content including a plurality of parts arranged in chronological order; comparing the information regarding the popularity of each part of the same content between a first user for whom it is desired to infer a target to follow and one or more second users who have used the same content as the first user, and inferring the target to follow from among the one or more second users based on the comparison result; and recommending the inferred target to follow to the first user.

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