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WO2019198950A1 - Appareil permettant de fournir des informations de contenu et procédé associé - Google Patents

Appareil permettant de fournir des informations de contenu et procédé associé Download PDF

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Publication number
WO2019198950A1
WO2019198950A1 PCT/KR2019/003498 KR2019003498W WO2019198950A1 WO 2019198950 A1 WO2019198950 A1 WO 2019198950A1 KR 2019003498 W KR2019003498 W KR 2019003498W WO 2019198950 A1 WO2019198950 A1 WO 2019198950A1
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Prior art keywords
content
information
management information
contents
occurrence matrix
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Ceased
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PCT/KR2019/003498
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English (en)
Korean (ko)
Inventor
권오범
김지훈
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Smilegate Stove Inc
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Smilegate Stove Inc
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4662—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms
    • H04N21/4666—Learning process for intelligent management, e.g. learning user preferences for recommending movies characterized by learning algorithms using neural networks, e.g. processing the feedback provided by the user
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies

Definitions

  • the present invention relates to a content information providing apparatus and a method thereof, and more particularly, to a technology for providing content information of similar content when viewing content provided through the Internet.
  • search technology for selecting and providing content desired by a user is being developed.
  • similar content is provided by providing the user with content information of similar content related to the content desired by the user.
  • the service to induce the viewing of is also provided.
  • similar content information is extracted and provided based on similar property information of contents, or contents viewed simultaneously are determined to have high similarity, and thus content with high simultaneous viewing frequency is provided as similar content information.
  • similar content is provided based on the simultaneous viewing frequency, there is a problem in that it is difficult to provide similar content information on the newly registered content because there is no information on the simultaneous viewing frequency of the newly registered content.
  • Embodiments of the present invention provide a content information providing apparatus and method for providing similar content information as recommended content to a user.
  • An apparatus for providing content information generates management information of the viewed contents by using a co-occurrence matrix based on the simultaneous viewing frequency of the viewed contents, and based on the management information, the similarity of the viewed contents.
  • a first processor providing content information;
  • a second processor for updating the simultaneous generation matrix based on the simultaneous viewing frequency of the viewed content based on the similar characteristic information when there is no management information of the viewed content.
  • the first processor calculates a correlation between the watched contents based on the co-occurrence matrix, classifies the watched contents on a vector space based on the correlation, and manages the management information. May include generating.
  • the management information may include location information of the viewed contents on a vector space.
  • the co-occurrence matrix may include a simultaneous viewing frequency of two contents forming a data pair.
  • the second processor calculates a similarity probability value between the contents that are not included.
  • the second processor may include calculating a hyperparameter based on a frequency of simultaneous occurrence of content having the highest similarity probability value.
  • the second processor may include calculating, as the hyperparameter, the largest number of total simultaneous occurrence frequencies or simultaneous occurrence frequencies of content having the highest similarity probability value.
  • the second processor may add the content in which the management information does not exist in the row and column of the co-occurrence matrix as an item, calculate the similarity probability value and the hyperparameter of the watched content, respectively, and manage the management. And calculating the co-occurrence frequency of content for which there is no information to update the co-occurrence matrix.
  • the second processor may include calculating a correlation between the entire contents including the content in which the management information does not exist based on the updated co-occurrence matrix.
  • the second processor calculates a correlation between the entire contents based on a co-occurrence matrix that does not include the content in which the management information does not exist, and the management information exists based on the similarity probability value. And calculating correlation between content not to be viewed and content viewed in the co-occurrence matrix.
  • the first processor may include generating management information of content in which the management information does not exist based on a correlation calculated by the second processor, and providing the similar content information. have.
  • a method of providing content information generates management information of the viewed contents using a co-occurrence matrix based on the simultaneous viewing frequency of the viewed contents, and generates the viewed information based on the management information.
  • the method may further include providing similar content information of content in which the management information does not exist based on the updated co-occurrence matrix.
  • the providing of the similar content information may include calculating a correlation between the viewed contents based on the co-occurrence matrix, and classifying the viewed contents on a vector space based on the correlation. Generating management information.
  • the updating of the co-occurrence matrix may include: managing the watched content and the management using similar property information of content in which the management information does not exist using an algorithm learned based on similar property information.
  • the method may include calculating a similarity probability value between contents in which information does not exist.
  • updating the co-occurrence matrix may include calculating a hyperparameter based on a co-occurrence frequency of content having the highest similarity probability value.
  • the updating of the co-occurrence matrix may include calculating, as the hyperparameters, the total sum of all co-occurrence frequencies of the content having the highest similarity probability value or the co-occurrence frequency as the hyperparameter, Computing each of the hyperparameters may be calculated as the frequency of the simultaneous occurrence of the content that does not exist the management information to update the co-occurrence matrix.
  • the providing of the similar content information may include calculating a correlation between the entire contents including the content without the management information based on the updated co-occurrence matrix.
  • the providing of the similar content information may include: calculating a correlation between all contents based on a co-occurrence matrix that does not include content in which the management information does not exist, and based on the similarity probability value.
  • the method may include calculating a correlation between content for which no management information exists and watched contents included in the co-occurrence matrix.
  • the providing of the similar content information may include determining the content of the content in which the management information does not exist based on a correlation between the content in which the management information does not exist and the viewed content included in the co-occurrence matrix. Generating management information and providing the similar content information.
  • similar content information may be provided to a user as recommended content.
  • the accuracy of similar content information provided as recommended content to a user may be improved.
  • FIG. 1 is a block diagram illustrating a configuration of an apparatus for providing content information according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating an example of a data pair of simultaneously viewed contents for generating a co-occurrence matrix according to an embodiment of the present invention.
  • FIG. 3 is an exemplary diagram for generating a co-occurrence matrix using the data pair of FIG. 2.
  • 4A and 4B are diagrams for describing an example of learning a relative position of a co-occurrence matrix-based contents in a vector space according to an embodiment of the present invention.
  • FIG. 5 is a diagram illustrating a method of learning similarity of contents using a neural network trained based on a co-occurrence matrix according to an embodiment of the present invention.
  • FIG. 6 is a diagram illustrating a method of calculating similarity probability values of existing contents for new contents using a neural network trained based on a co-occurrence matrix according to an embodiment of the present invention.
  • FIG. 7 is an exemplary diagram of updating a co-occurrence matrix for new content by calculating a maximum frequency-based hyperparameter according to an embodiment of the present invention.
  • FIG. 8 is an exemplary diagram of updating a co-occurrence matrix for new content by calculating a total frequency-based hyperparameter according to an embodiment of the present invention.
  • FIG. 9 is an exemplary view illustrating a method of calculating a correlation PMI according to an embodiment of the present invention.
  • FIG. 10 is an exemplary diagram of updating a co-occurrence matrix for new content by calculating a correlation (PMI) according to an embodiment of the present invention.
  • FIG. 11 is a flowchart illustrating a method of generating and managing a co-occurrence matrix for content according to an embodiment of the present invention.
  • FIG. 12 is a flowchart illustrating a content providing method according to an embodiment of the present invention.
  • FIG. 13 is a flowchart illustrating a method of updating a co-occurrence matrix based on similar property information for new content according to an embodiment of the present invention.
  • FIG. 14 is a flowchart illustrating a method of updating a co-occurrence matrix based on similar property information for new content according to another embodiment of the present invention.
  • FIG. 15 illustrates a computing system in accordance with an embodiment of the present invention.
  • FIG. 1 is a block diagram illustrating a configuration of an apparatus for providing content information according to an embodiment of the present invention.
  • the content information providing apparatus 100 includes a first processor 110 and a second processor 120.
  • the first processor 110 and the second processor 120 may be implemented as a learned network.
  • the first processor 110 and the second processor 120 are shown in separate configurations, but are not limited thereto.
  • the first processor 110 and the second processor 120 may be integrated. It may also operate as a processor.
  • the learned neural network of the first processor 110 generates a co-occurrence matrix based on the learned algorithm (eg, the first algorithm) and based on the co-occurrence matrix.
  • Management information of contents may be generated.
  • the learned algorithm may be defined as an algorithm for generating similar contents to have similar management information values.
  • contents that are similar to each other may have management information located adjacent to a vector space, and may mean the same or similar contents in content, subject, material, appearance, category, summary, and the like of the content. .
  • the learned neural network of the second processor 120 updates the co-occurrence matrix by calculating similarity probability values between the contents based on the similar characteristic information of the contents based on the learned algorithm (eg, the second algorithm).
  • the similarity probability value means the degree of similarity between contents
  • the calculation of the similarity probability value may be implemented by a general similarity related algorithm, and the learning network of the second processor 120 may be learned by such an algorithm.
  • the 'similar property information' may include the same or similar property information in content, theme, location, appearance, category, summary, and the like of the content. For example, if the content is a movie, it may include a main actor, release year, movie title, production cost and the like.
  • the first processor 110 may generate management information of the viewed contents using a simultaneous generation matrix based on the simultaneous viewing frequency of the viewed contents, and provide similar content information of the viewed contents based on the management information. In this case, the first processor 110 may generate management information of the contents at predetermined intervals or may generate the management information every predetermined time after the new content is registered.
  • the first processor 110 generates a co-occurrence matrix using the simultaneous viewing frequency of the contents simultaneously viewed, calculates a pointwise mutual information (PMI) of the contents using the value of the co-occurrence matrix, and correlates the contents. Based on the diagram, contents may be classified to have a relative position in an N-dimensional vector space. A method of generating the co-occurrence matrix will be described in more detail later with reference to FIGS. 2 and 3.
  • the rows and columns of the co-occurrence matrix may include identification information (eg, content name) of the contents and a frequency at which two contents forming a data pair are simultaneously viewed.
  • identification information eg, content name
  • the condition to be viewed at the same time may include the case where the viewing of the content 2 is started while the content 1 is being viewed.
  • the content 2 when the content 2 is viewed within a predetermined time after the content 1 is completed viewing, it may be regarded as simultaneous viewing.
  • the first processor 110 calculates a correlation between the contents using the value of the co-occurrence matrix, and classifies the contents on the N-dimensional vector space based on the correlation.
  • each of the contents may be arranged in positions adjacent to each other as the frequency of occurrence or correlation is high in the N-dimensional vector space, and the first processor 110 may determine that the similarities of the contents in the adjacent positions are high. Can be. That is, the first processor 110 may classify the contents in the adjacent position as the similar contents and generate management information (vector information) which is relative position information on the vector space for each contents.
  • the second processor 120 may update the co-occurrence matrix based on the simultaneous viewing frequency of the watched content in which the management information does not exist based on the similar characteristic information of the content. .
  • the second processor 120 may calculate a similarity probability value between the watched content and the new content by using similar property information of the content (new content) in which the management information does not exist using an algorithm learned based on the similar property information.
  • the new content may include content that is newly registered and is not included in the co-occurrence matrix, so that management information is not generated. That is, when the characteristic information of the new content is input to the second processor 120, the second processor 120 may generate new content and existing content (content already viewed and management information existing or simultaneously generated based on the neural network on which learning has been completed). The similarity probability value between the contents included in the matrix) is calculated.
  • the second processor 120 may update the co-occurrence matrix by calculating a hyper-parameter based on the co-occurrence frequency of the content having the highest similarity probability value.
  • the second processor 120 may calculate the total sum of all simultaneous occurrence frequencies of the content having the highest similarity probability value as a hyper parameter.
  • the second processor 120 may calculate, as a hyper parameter, the largest number of simultaneous occurrence frequencies of content having the highest similarity probability value.
  • the second processor 120 adds new content to the rows and columns of the co-occurrence matrix as items, calculates similarity probability values and hyperparameters of the watched contents, respectively, and calculates the co-occurrence matrix by updating the co-occurrence matrix.
  • 'operation' may include a multiplication operation.
  • the second processor 120 may calculate a correlation between the entire contents based on the updated co-occurrence matrix based on the hyperparameter and the similarity probability value. In this case, a correlation between the new content and the existing content may also be calculated.
  • the second processor 120 does not calculate a hyperparameter, but calculates a correlation between the entire contents using a similarity probability value between the new content and the existing content and a co-occurrence matrix that does not include the new content. Can be.
  • the first processor 110 When the second processor 120 calculates a correlation between the new content and the existing content, the first processor 110 generates the new content based on the correlation between the new content and the existing content calculated by the second processor 120.
  • the management information for the content may be generated, and similar content information may be generated and provided by a learned algorithm.
  • the content information providing apparatus 100 of the present invention having such a configuration provides similar content information based on the simultaneous viewing frequency, but does not have the simultaneous viewing frequency information, that is, the existing content for the new content that does not generate the simultaneous matrix. Based on a neural network trained based on similar property information of contents, a similarity probability value for each of the existing contents is calculated and randomly calculated the simultaneous viewing frequency of the new content based on the similarity probability value. Similar content information may be provided based on the simultaneous viewing frequency.
  • FIG. 2 is a diagram illustrating an example of a data pair of simultaneously viewed contents for generating a co-occurrence matrix according to an embodiment of the present invention.
  • 3 is an exemplary diagram for generating a co-occurrence matrix using the data pair of FIG. 2.
  • FIGS. 2 and 3 contents are described as examples for convenience of description, but the contents may include various multimedia data for the purpose of shopping and advertisement.
  • the process of generating the co-occurrence matrix of FIGS. 2 and 3 may be implemented by the first processor 110.
  • the content information providing apparatus 100 may include movie 1, movie 2, and movie 1. And movie 3 are each generated as data pairs.
  • the content information providing apparatus 100 includes four data pairs (movie 2 and movie 1, movie 2 and movie 3, movie). 2 and movie 4) are generated.
  • the content information providing apparatus 100 generates a co-occurrence matrix based on the data pairs generated in FIG. 2. That is, the movie 1 and the movie 2 are simultaneously watched twice, the frequency is recorded at 2, and the movie 2 and the movie 3 are also watched twice at the same time, and the frequency is recorded at 2. As such, the content information providing apparatus 100 generates a value of the co-occurrence matrix based on the number of simultaneous viewing.
  • 4A and 4B are diagrams for describing an example of learning a relative position (management information) in a vector space of co-occurrence matrix-based contents according to an embodiment of the present invention.
  • the first processor 110 is implemented as a one hot vector, and the one hot vector refers to a vector in which only one value is '1' and the rest is '0' in the entire array.
  • the first processor 110 weights W such that 'movie 1' is input to the input layer and 'movie 2' is output from the output layer. Learn the value of the number of embedding dimensions (N)). When the learning is completed, the relative positions in the vector space of the words (movie 1 and movie 2) are learned to the weight values.
  • the value of movie 2 is set to '1' as a similar content for movie 1, but the value of '1' may be replaced with a correlation (PMI (movie 1 ';' movie 2 ')) as shown in FIG. 4B.
  • PMI movingie 1 ';' movie 2 '
  • FIG. 4B a correlation
  • FIG. 5 is a diagram illustrating a method of learning similarity of contents using a neural network trained based on a co-occurrence matrix according to an embodiment of the present invention
  • FIG. 6 is a co-occurrence according to an embodiment of the present invention.
  • 5 and 6 disclose an example of calculating similarity or similarity probability values through the learned characteristic network based on the similar characteristic information by the second processor 120.
  • the neural network is composed of N layers composed of a plurality of units, and the calculated value Y calculated through the layers calculates the similarity or similarity probability value through a softmax function. can do.
  • the second processor 120 when similar property information of content is input to a neural network, the second processor 120 outputs a calculated value Y and performs a softmax function to output a similarity degree.
  • the similar characteristic information may include a director, a lead actor, a release year, a movie title, a production cost, etc. of the movie 2, and the similar characteristic information of the movie 2 and the movie 3 may coincide.
  • FIG. 7 is an exemplary diagram of updating a co-occurrence matrix for new content for which management information does not exist by calculating a maximum frequency-based hyperparameter according to an embodiment of the present invention.
  • FIG. 7 illustrates an example of generating a value of a co-occurrence matrix for a new content 'movie 9' by calculating a hyper parameter.
  • the second processor 120 may calculate the maximum frequency of the content having the highest similarity probability value as the hyper parameter among the contents constituting the co-occurrence matrix, and multiply the hyper parameter by the similarity probability value of each content to calculate the frequency for the new content. have.
  • the maximum frequency of movie 3 is calculated as a hyper parameter. That is, the simultaneous viewing frequency of movie 3 with movie 1 is 2, the simultaneous viewing frequency of movie 3 and movie 2 is 2, the simultaneous viewing frequency of movie 3 and movie 4 is 2, and the simultaneous viewing frequency of movie 3 and movie 5 is 1 Therefore, the content information providing apparatus 100 calculates the maximum frequency '2' as a hyper parameter. Subsequently, the second processor 120 may calculate a frequency for 'movie 9' by multiplying the hyperparameter '2' by the similarity probability value of each content.
  • the second processor 120 may record only the frequency of the movie 3 having the largest value as '1' and the remaining contents as '0'.
  • the second processor 120 may record values obtained by multiplying the hyperparameter and the similarity probability value for each content corresponding to the new content having no management information. In this case, the number recorded may be discarded or rounded down to a decimal point to record only an integer.
  • 8 is an exemplary diagram of updating a co-occurrence matrix for new content by calculating a total frequency-based hyperparameter according to an embodiment of the present invention. 8 illustrates an example of generating a value of a co-occurrence matrix for the new content "movie 9" by calculating a hyper parameter.
  • the second processor 120 adds the frequency of the content having the highest likelihood probability value among the contents constituting the co-occurrence matrix, calculates the sum as a hyperparameter, multiplies the hyperparameter with the similarity probability value of each content, and generates a frequency for the new content. Can be calculated.
  • the second processor 120 calculates a hyper parameter '7', which is the sum of the frequencies.
  • the second processor 120 may calculate the frequency for the movie 9 by multiplying the hyperparameter '7' by the similarity probability value of each content.
  • the second processor 120 may record only the frequency of the movie 3 having the largest value as '1' and the remaining contents as '0'. In this case, the second processor 120 may record all of the content obtained by multiplying the hyperparameter and the similarity probability value for each content, but may discard only the decimal point and record only the integer.
  • FIG. 9 illustrates an example of calculating Pointwise Mutual Information (PMI) between contents according to an embodiment of the present invention
  • PMI Pointwise Mutual Information
  • FIG. 10 simultaneously includes information on new content according to an embodiment of the present invention. It is a figure which shows an example of an generation matrix.
  • the first processor 110 calculates a correlation between the contents of the co-occurrence matrix to generate management information between the contents
  • the second processor 120 correlates the contents of the co-occurrence matrix updated based on the hyperparameter.
  • the degree of correlation may be calculated between the contents of the existing co-occurrence matrix before the update or the update, and the correlation between the new content and the existing contents may be calculated based on the similarity probability value of the new content. The method is described in detail through the following equations.
  • p (x) is the probability that x content is watched
  • p (y) is the probability that y content is watched
  • p (x, y) is the probability that x content and y content are watched simultaneously.
  • Equation 1 may be expressed as Equation 2.
  • x i is the sum of the values of the rows of the content with the highest similarity probability value
  • x j is the sum of the values of the columns of the content with the highest similarity probability value
  • D is the sum of the frequencies of the co-occurrence matrix (see FIG. 9).
  • the first processor 110 and the second processor 120 may calculate a correlation between existing contents of the co-occurrence matrix through Equation 2. However, in order to calculate the correlation between the new content and the existing content, the second processor 110 assumes a similarity probability value as shown in Equation 3 below.
  • Equation 4 When the equation is represented by substituting the similarity probability value of Equation 3 into Equation 2, Equation 4 below.
  • the second processor 110 may calculate the correlation PMI of the new content by using the similarity probability value as shown in Equation 4.
  • the equation 2 is applied to calculate the equation.
  • the second processor 120 inputs and learns the original hot vector as shown in FIG. 4B based on the correlation between the contents. That is, the second processor 120 optimizes the weight so that the movie 3 is output when the movie 2 is input to the neural network and the movie 3 is set as the output. Then, when the learning is completed, the second processor 120 learns the relative position in the vector space of the movie 2 and the movie 3 by using the weight values. That is, the correlation between the probability of watching each movie may be learned through the weight value. That is, the correlation between the movies 1 to 8 of FIG. 10 may be calculated through Equation 5.
  • Equation 2 described above is applied to the correlation between Movies 1 to 8
  • Equation 4 is applied to the calculation of the correlation between the new content (Movie 9) and the existing content (Movies 1 to 8).
  • Table 1 shows an example of calculating correlation between movie 9 and existing contents (movies 1 to 8) by applying x j and D values of FIG. 9 to Equation 4, and table 2 displays final calculated results.
  • Table 1 shows an example of calculating correlation between movie 9 and existing contents (movies 1 to 8) by applying x j and D values of FIG. 9 to Equation 4, and table 2 displays final calculated results.
  • Table 1 shows an example of calculating correlation between movie 9 and existing contents (movies 1 to 8) by applying x j and D values of FIG. 9 to Equation 4, and table 2 displays final calculated results.
  • Table 1 shows an example of calculating correlation between movie 9 and existing contents (movies 1 to 8) by applying x j and D values of FIG. 9 to Equation 4, and table 2 displays final calculated results.
  • the second processor 120 may calculate all the correlations between the movies 1 to 9 and display them as shown in FIG. 10, and the first processor 110 manages the respective contents based on the calculated correlations between the entire contents.
  • the movie 9 When the movie 9 is watched by generating the information, the movie 3 may be recommended as similar content information.
  • FIG. 11 is a flowchart illustrating a method of generating and managing a co-occurrence matrix for content according to an embodiment of the present invention.
  • the content information providing apparatus 100 of FIG. 1 performs the process of FIG. 11.
  • the operations described as being performed by the apparatus are controlled by the first processor 110.
  • the content information providing apparatus 100 calculates a correlation PMI between contents based on the updated co-occurrence matrix (S140). Subsequently, the content information providing apparatus 100 classifies the contents on the vector space using the correlation to generate management information (vector information) (S150). In this case, the vector information is position information in a vector space, and the positions of contents having high similarity are adjacent to each other. In addition, the content information providing apparatus 100 may calculate the correlation between the contents through Equation 2 described above.
  • the content information providing apparatus 100 provides similar content information of the viewed content based on the generated management information (S160).
  • the content information providing apparatus 100 checks whether there is concurrent matrix information on the viewed content (S220).
  • the content information providing apparatus 100 provides similar content information of the viewed content on the basis of the simultaneous generation matrix (S230).
  • the content information providing apparatus 100 updates the co-occurrence matrix by using the neural network learned based on the similar characteristic information. Thereafter, the apparatus 100 for providing content information provides similar content information based on the updated co-occurrence matrix (S240). That is, the management information of each content is generated using the updated co-occurrence matrix, and similar content information is provided based on the management information.
  • FIG. 12 illustrates an example in which the content information providing apparatus 100 updates a co-occurrence matrix based on similar property information when there is no management information of the viewed content, but the present invention is not limited thereto.
  • the information providing apparatus 100 may be implemented to periodically update the co-occurrence matrix based on the similar characteristic information.
  • FIG. 13 is a flowchart illustrating a method of updating a co-occurrence matrix based on similar property information with respect to new content according to an embodiment of the present invention.
  • S310 to S340 are controlled by the second processor 120 and the process of S350 is controlled by the first processor 110 among the operations described as being performed by the apparatus. have.
  • the content information providing apparatus 100 calculates each similarity probability value for contents that are items of a co-occurrence matrix. That is, the content information providing apparatus 100 calculates a similarity probability value using a neural network that has been previously learned based on similar characteristic information of new content (S310). In this case, referring to FIG. 6, the content information providing apparatus 100 may calculate similarity probability values of respective contents by inputting similar characteristic information of the new movie 9 into the neural network (second processor 120) that has already been learned. Can be.
  • the content information providing apparatus 100 calculates a hyper parameter based on the simultaneous viewing frequency of the contents which are the concurrent occurrence matrix items (S320).
  • the hyperparameter may be calculated from the sum or the maximum frequency of the frequencies of the content having the highest similarity probability value.
  • the hyper parameter may be calculated as the sum value or the maximum value of the row.
  • the content information providing apparatus 100 updates the co-occurrence matrix for the new content with a value obtained by multiplying the similarity probability value and the hyperparameter (S330).
  • the content information providing apparatus 100 may store a value obtained by multiplying a hyperparameter with a similarity probability value of each of the contents (movies 1 to 8) for the new content, movie 9, as the frequency for the movie 9 item in the co-occurrence matrix. have.
  • the content information providing apparatus 100 calculates a correlation PMI of all contents (new content and existing content) based on the updated co-occurrence matrix (S340).
  • the content information providing apparatus 100 may calculate a correlation between the entire contents (movies 1 to 9) through Equation 2.
  • the content information providing apparatus 100 inputs a correlation degree (PMI) between the new content and the existing content to the first processor 110 to generate management information of the new content and generate similar content information based on the management information.
  • PMI correlation degree
  • the first processor 110 may generate a weight value obtained by inputting a correlation to the neural network as shown in FIG. 4B as management information of contents.
  • FIG. 13 discloses an example in which similar content information is provided by calculating a correlation between all contents (including new content) after updating a co-occurrence matrix for new content based on a hyper parameter.
  • FIG. 14 is a flowchart illustrating a method of updating a co-occurrence matrix based on similar property information with respect to new content according to another embodiment of the present invention.
  • FIG. 12 is new content without management information of process S240 of FIG. 12.
  • the content information providing apparatus 100 of FIG. 1 performs the process of FIG. 14.
  • S410 to S420 among the operations described as being performed by the apparatus may be understood to be controlled by the second processor 120 and the process of S430 may be controlled by the first processor 110. have.
  • the content information providing apparatus 100 calculates each similarity probability value for contents that are items of a co-occurrence matrix. That is, the content information providing apparatus 100 calculates a similarity probability value using a neural network that has been previously learned based on similar characteristic information of new content (S410). In this case, referring to FIG. 6, the content information providing apparatus 100 may calculate similarity probability values of respective contents by inputting similar characteristic information of movie 9, which is new content, to a neural network (second processor 120) that has already been learned. Can be.
  • the content information providing apparatus 100 calculates a correlation between the entire contents based on the similarity probability value and the co-occurrence matrix that does not include the new contents (S420). That is, the content information providing apparatus 100 calculates a correlation between movies 1 to 8 by applying Equation 2 based on a co-occurrence matrix that does not include new content, and between the new content (movie 9) and existing contents.
  • the correlation between the new content (movie 9) and the existing content may be calculated by applying Equation 4 based on the similarity probability value and the concurrent matrix including no new content.
  • Correlation PMI (Movie 1, Movie 2) to PMI (Movie 7, Movie 8) is calculated through Equation 2 with respect to Movies 1 through 8 in FIG. 10, and Correlation PMI through Equation 4 with respect to Movie 9. (Movie 9, Movie 1) to PMI (Movie 8, Movie 9) are calculated.
  • the content information providing apparatus 100 inputs a correlation degree (PMI) between the new content and the existing content to the first processor 110 to generate management information of the new content and generate similar content information based on the management information.
  • PMI correlation degree
  • the first processor 110 may generate a weight value obtained by inputting a correlation to the neural network as shown in FIG. 4B as management information of contents.
  • the apparatus 100 for providing content information of the present invention generates a co-occurrence matrix, generates management information of respective contents based on the generated co-occurrence matrix, and provides similar content information based on the management information.
  • the content information providing apparatus 100 updates the co-occurrence matrix for the new content by using the learned neural network based on the similar characteristic information of the new content without the management information.
  • FIG. 15 illustrates a computing system in accordance with an embodiment of the present invention.
  • the computing system 1000 may include at least one processor 1100, a memory 1300, a user interface input device 1400, a user interface output device 1500, and storage connected through a bus 1200. 1600, and network interface 1700.
  • the processor 1100 may be a central processing unit (CPU) or a semiconductor device that executes processing for instructions stored in the memory 1300 and / or the storage 1600.
  • the memory 1300 and the storage 1600 may include various types of volatile or nonvolatile storage media.
  • the memory 1300 may include a read only memory (ROM) and a random access memory (RAM).
  • the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, software module, or a combination of the two executed by the processor 1100.
  • the software module resides in a storage medium (ie, memory 1300 and / or storage 1600), such as RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM. You may.
  • An exemplary storage medium is coupled to the processor 1100, which can read information from and write information to the storage medium.
  • the storage medium may be integral to the processor 1100.
  • the processor and the storage medium may reside in an application specific integrated circuit (ASIC).
  • ASIC application specific integrated circuit
  • the ASIC may reside in a user terminal.
  • the processor and the storage medium may reside as discrete components in a user terminal.
  • similar content information may be provided to the user as recommended content, and the accuracy of the similar content information provided as the recommended content to the user may be improved. Can be improved.

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Abstract

Selon un mode de réalisation, l'invention concerne un appareil permettant de fournir des informations de contenu. L'appareil peut comprendre : un premier processeur permettant de générer des informations de gestion de contenus visualisés à l'aide d'une matrice de génération simultanée sur la base d'une fréquence de visualisation simultanée du contenu visualisé et de fournir des informations de contenu similaires du contenu visualisé sur la base des informations de gestion ; et un second processeur permettant de mettre à jour la matrice de génération simultanée sur la base de la fréquence de visualisation simultanée des contenus visualisés sur la base d'informations caractéristiques similaires lorsqu'il n'y a pas d'informations de gestion du contenu visualisé.
PCT/KR2019/003498 2018-04-13 2019-03-26 Appareil permettant de fournir des informations de contenu et procédé associé Ceased WO2019198950A1 (fr)

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KR102257550B1 (ko) * 2020-04-07 2021-05-27 주식회사 엘지유플러스 Vod 가이드 채널에의 예고편 편성용 컨텐츠 선정 방법 및 장치
US12510523B2 (en) * 2020-06-22 2025-12-30 Sony Group Corporation Fragrance information processing system, fragrance information processing device, and fragrance information processing method
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