A video recommendation system and a video recommendation method

A recommendation system and recommendation method technology, applied in the field of film systems, can solve problems such as personal experience inconsistent with descriptions, unpleasant user experience, etc., to achieve the effect of enhancing trust and satisfaction, and reducing discomfort

Inactive Publication Date: 2019-06-04
SUZHOU RAKEN TECH
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AI-Extracted Technical Summary

Problems solved by technology

However, each user's habits, preferences, and life experience are different. Even if the supplier's suggestions or instructions are consi...
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Method used

[0060] In summary, the movie recommendation system and movie recommendation method proposed by the present invention analyze the user's emotional state when watching a movie by detecting the user's brain wave data. Since the pre-classification of videos does not necessarily conform to the user's viewing mood, the present invention can adaptively record, update and recommend videos suitable for the user's current mood, which can overcome the problem that the current video classification does not conform to the user's actual mood. Let the user's e...
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Abstract

The invention discloses a film recommendation system and a film recommendation method. The film recommendation system comprises a virtual reality device and a server. The virtual reality device comprises a brain wave sensor and a processor. The brain wave sensor is used for capturing first brain wave data. The processor is coupled to the brain wave sensor and receives the first brain wave data. The server is coupled to the virtual reality device. The server generates a recommendation list according to the first emotion data corresponding to the first brain wave data and transmits the recommendation list to the virtual reality device. Wherein the recommendation list has a plurality of film lists for the virtual reality device to play at least one film. The method can adaptively record, update and recommend the movie suitable for the current emotion of the user, can overcome the problem that the current movie classification does not conform to the actual emotion of the user, enables theemotion state of the user to conform to the played movie, and improves the confidence and satisfaction of the user for movie watching. And meanwhile, any singularity sense of the user is not caused inthe film recommendation process.

Application Domain

Input/output for user-computer interactionBuying/selling/leasing transactions +2

Technology Topic

Recommender systemVirtual reality +3

Image

  • A video recommendation system and a video recommendation method
  • A video recommendation system and a video recommendation method
  • A video recommendation system and a video recommendation method

Examples

  • Experimental program(1)

Example Embodiment

[0041] Examples: The detailed features and advantages of the present invention will be described in detail in the following embodiments. The content is sufficient to enable any person skilled in the art to understand the technical content of the present invention and implement it accordingly, and according to the content disclosed in this specification and the scope of patent application With the illustrations, any person skilled in the art can easily understand the related objectives and advantages of the present invention. The following examples illustrate the viewpoints of the present invention in further detail, but do not limit the scope of the present invention by any viewpoint.
[0042] Please refer to figure 1 , Is a functional block diagram of a movie recommendation system 100 according to some embodiments of the present invention. Such as figure 1 As shown, the movie recommendation system 100 includes a virtual reality device 110 and a server 510. In some embodiments, the virtual reality device 110 can play a movie for the user wearing it to watch, and at the same time can record the brain wave data of the user while watching the movie. Further, the virtual reality device 110 analyzes the brain wave data to obtain emotion data, and pushes the emotion data (or its classification) to the server 510. Alternatively, the virtual reality device 110 can also directly transmit the brain wave data of the user when watching a movie to the server 510, and the server 510 analyzes the brain wave data to obtain the user's mood data or classification. Therefore, the server 510 can associate the movie type with the classification of the emotional data according to the emotional data of the user watching the movie, so that when the user wants to watch the movie later, it can recommend a more suitable user (such as the user's current emotional state or reaction). Video type.
[0043] The virtual reality device 110 includes a brain wave sensor 111, a processor 113, a display 115 and a transmission interface 117. The brain wave sensor 111, the display 115, and the transmission interface 117 are respectively coupled to the processor 113. In one embodiment, when the user wears the virtual reality device 110 on his head, the user is watching a movie and emits subtle changes in electric or magnetic waves, that is, brain wave signals. The brain wave sensor 111 can receive the user's brain wave signal and generate brain wave data. The brain wave data can be the following but not limited to P300 wave, Lambda (λ) wave, Gamma (γ) wave, Beta (β) wave, Alpha (α) wave, Theta (θ) wave, Delta (δ) wave, etc.
[0044] In one embodiment, the processor 113 analyzes the frequency or potential change of the brain wave data, and learns the type of brain wave data and the signal strength of various types of brain wave data. For example, the processor 113 analyzes the frequency from the brain wave data to be 20-100 Hz, and determines that it is a Gamma (γ) wave. In addition, the signal strength is measured in the range of 0 to 100. When the analyzed value is 60, it is judged that the signal strength of the Gamma (γ) wave is moderate. For another example, the processor 113 analyzes the frequency from the brain wave data to be 15-28 Hz, and determines that it is a Beta (β) wave. When the value of the signal strength is 90, it is judged that the signal strength of the Beta (β) wave belongs to the strength. By analogy, the processor 113 analyzes one or more brain wave types in the brain wave data and the signal intensity of each brain wave type.
[0045] In another embodiment, the processor 113 does not analyze or process the brain wave data after obtaining the brain wave data, but transmits the brain wave signal to the server 510 through the transmission interface 117. The server 510 can directly process the brain wave data to obtain the type of brain wave data and the signal strength of various types of brain wave data. The data processing capability of the server 510 is generally much higher than that of general consumer electronic products (such as the virtual reality device 110 in this embodiment). Therefore, in this embodiment, the processor 113 of the virtual reality device 110 only collects and does not process the brain wave data, and the unanalyzed data is provided to the server 510 for processing and analysis. In addition to data processing capabilities, the server 510 can also perform a variety of processing to obtain more accurate emotional data when the user watches a movie, and establish a correlation between the type of the movie and the classification of the emotional data, so that when the user wants to watch the movie later, it can recommend The type of movie that is more suitable for users.
[0046] In one embodiment, the processor 113 parses the brain wave data to obtain one or more brain wave types and the signal strength of each brain wave type, and generates emotion data according to the brain wave types and signal strengths. For example, the Gamma (γ) wave of the brain wave data represents the current happiness of the user. When it is analyzed that the signal intensity of the Gamma (γ) wave is moderate, it means that the user can feel the degree of happiness when watching the movie is moderate. For another example, the Beta (β) wave of the brain wave signal represents that the user is currently excited. When the signal strength of the Beta (β) wave is analyzed as the strength, it means that the user has a greater reaction or a greater degree of excitement when watching the movie. In other words, the current movies are more capable of attracting users' attention or emotional reactions to users. Emotional data can be, but not limited to, happiness, excitement, relaxation, concentration, fatigue, thinking, excitement, etc. It is worth mentioning that the emotional data associated with brain wave data is not limited to one emotional state. The user may feel excited and tired at the same time while watching the movie, and the emotional data at this time is excited and tired.
[0047] The virtual reality device 110 then pushes and broadcasts the generated emotional data to the server 510 through the transmission interface 117. The server 510 correspondingly generates a recommendation list according to the received emotion data. The server 510 includes a movie database with several movies, and each movie has a corresponding at least one movie type. For example, when the film is a sweet romantic comedy, the film type of the film may include happiness and relaxation. When the film is a thriller, the type of the film may include excitement, excitement, and so on. In one embodiment, each movie includes several movie types, and each movie type has a corresponding weight. For example, a movie has a movie type of happiness and a weight of 10. At the same time, the movie can also have a movie type of excitement and a weight of 6, and so on.
[0048] The recommendation list includes several movie lists, and each movie list has movie type data. In each movie list, there are several movie links, and the movies corresponding to each movie link belong to the same movie type. For example, the recommendation list includes a first movie list (for example, the movie type data is happiness) and a second movie list (for example, the movie type data is excitement). The first recommended list contains links to movies that belong to happiness movies, and the second recommendation list contains links to movies that belong to exciting movies. Therefore, the server 510 searches the movie database for the movie type data associated with the mood data according to the mood data, and records the movie links of the corresponding movies in the form of several movie lists in the recommendation list. In one embodiment, it is determined whether to record in the movie list according to the weight of the movie type to which each movie belongs. For example, if the emotional data is a feeling of happiness and intensity of emotional response, the server 510 will record a happiness movie with a weight of 8 or more, and link the movie to the movie list. In this way, the corresponding video recommendation can be made more in line with the emotional intensity of the user.
[0049] The server 510 transmits the generated recommendation list to the virtual reality device 110. The display 115 of the virtual reality device 110 presents a screen containing a recommendation list for the user to click on. In one embodiment, after the movie link is selected by the user, the virtual reality device 110 obtains the movie stream from the server 510 through the transmission interface 117, and plays the movie on the display 115 for the user to watch. The screen that contains the recommendation list can be presented, but is not limited to a drop-down menu, a table list, a slot-shaped list, etc.
[0050] It is worth mentioning that when the user operates the virtual reality device 110, he needs to log in to the server 510 through the Internet. For example, the server 510 is provided with a user account database. The user account database records the following but not limited to each user's account, password, personal information (such as age, gender, favorite movie type, past movie link click data), recommendation list, etc. The aforementioned recommendation list generated based on sentiment data varies with different user accounts. Therefore, it is possible to adaptively recommend suitable movies for users to watch according to different users' preferences and brainwave reactions.
[0051] In one embodiment, if the user uses the movie watching function of the server 510 for the first time, since there is no relevant historical data in the user account database, the server 510 will first predict the user based on the user’s age, gender and other personal data at the time of registration. The type of movies you like to watch, based on which a default movie list is generated. The default movie list contains several movie lists, and each movie list has corresponding movie type data. Each movie list includes movie links of several movies for the virtual reality device 110 to play at least one movie.
[0052] Please refer to figure 2 , Which shows a flowchart of the steps of a movie recommendation method according to some embodiments of the present invention. In one embodiment, figure 2 The method for recommending movies is that the server 510 does not have the user's historical viewing data as an illustration. Please refer to the following figure 1 and figure 2. In step S210, after the user wears the virtual reality device 110, the user views the screen through the display 115 to perform operations. The virtual reality device 110 transmits the operation to the server 510 through the transmission interface 117, and registers and logs in a user account in the server 510. In step S220, the server 510 generates a default movie list according to the user's personal information, and transmits the default movie list to the virtual reality device 110.
[0053] In step S230, a list of default movies is presented through the virtual reality device 110 for the user to click. In one embodiment, the user clicks on the movie list of "happiness" on the default movie list, and several movie links of the movie list of "happiness" are further presented. The user then clicks one of these video links to play the video. In the process of playing the video, the brain wave sensor 111 captures brain wave data, and the brain wave data at this time represents the user's reaction or emotion when watching the current video.
[0054] In step S240, after the movie is finished, it is determined whether the collected brain wave data is sufficient. In one embodiment, after the movie is finished, it is determined whether the playing time of the movie is less than the threshold value to determine whether the brainwave data is sufficient. For example, if the video playing time is only one minute, it means that the amount of brainwave data collected is not enough to recommend the video. Those skilled in the art to which the present invention pertains can design other methods to evaluate whether the brain wave data is sufficient, and the foregoing is only described as an example. In step S250, when it is determined that the brainwave data is insufficient, an error message is generated and sent back to the server 510.
[0055] In step S260, if the aforementioned brain wave data of watching the movie is sufficient, the brain wave data is analyzed to generate first emotion data. It should be noted that the analysis of brainwave data can be executed by the virtual reality device 110 or by the server 510, and the present invention does not limit the analysis of brainwave data to be executed by specific hardware. The explanation of parsing the brainwave data to generate the first emotion data is as described above, and will not be repeated here. In an embodiment, the virtual reality device 110 performs an action of analyzing brain wave data, and then the virtual reality device 110 transmits the first emotion data to the server 510. In step S270, the server 510 generates a recommendation list corresponding to the classification of the first emotion data. In step S280, the server 510 transmits the recommendation list to the virtual reality device 110. Next, in step S290, a recommendation list is presented through the virtual reality device 110 for the user to click on the movie link to watch the movie.
[0056] Please refer to image 3 , Which shows a flowchart of the steps of a movie recommendation method according to other embodiments of the present invention. In one embodiment, image 3 The method for recommending movies in is that the server 510 already has the user's historical viewing data, and the recommendation list is further updated with the user's status as an illustration. Please refer to the following figure 1 and image 3. In step S310, when the virtual reality device 110 plays a video, the brain wave sensor 111 continues to capture brain wave data. In step S320, after the movie is finished, it is determined whether the collected brain wave data is sufficient. If it is determined that the brain wave data is insufficient, in step S330, an error message indicating that the brain wave data is insufficient is generated and sent back to the server 510.
[0057] If the brain wave data is sufficient, in step S340, the brain wave data captured subsequently is analyzed to generate the second emotion data. The method of generating the second emotion data is similar to that of generating the first emotion data, and will not be repeated here. Then, the virtual reality device 110 transmits the second emotion data to the server 510.
[0058] In step S350, the server 510 updates the content of the recommendation list corresponding to the classification of the second emotion data. In one embodiment, the server 510 determines whether the second emotion data corresponds to the movie type of the movie being played. For example, if the type of movie played is happiness, but the second emotion data judged is excitement, it means that although the user watched the movie marked as "happiness", the user's emotional reaction is actually excited . At this time, it is determined that the second emotion data does not match the movie type. Then, the server 510 uses the second emotion data (for example, excitement) to update several movie lists in the recommendation list.
[0059] The server 510 records the updated recommendation list for the user to use next time. In step S360, if the user wants to continue watching the movie, the server 510 transmits the updated recommendation list to the virtual reality device 110. In step S370, the updated recommendation list is presented through the virtual reality device 110. In one embodiment, if the user continues to watch the movie, after performing step S370 of the movie recommendation method, return to step S310 to continue the execution, so as to continuously detect the user’s mood of watching the movie, and to update the push to the user Recommended list.
[0060] In summary, the video recommendation system and video recommendation method proposed by the present invention analyze the user's emotional state of watching the video by detecting the user's brain wave data. Since the pre-made classification of movies does not necessarily conform to the user’s viewing mood, the present invention can adaptively record, update and recommend movies suitable for the user’s current mood, which can overcome the problem that the current movie classification does not conform to the user’s actual mood. Let the user's emotional state match the movie being played, and increase the user's trust and satisfaction in watching the movie. In addition, the present invention uses a virtual reality device 110 (such as a head-mounted virtual reality helmet) to allow the user to watch the movie. Since the user wears the virtual reality helmet, the brain wave signal can be detected at the same time, which can reduce the user's brain wave signal detection. Discomfort, so that the process of recommending movies will not cause any strangeness to the user.

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