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WO2019164040A1 - Procédé et système de recommandation de liste de diffusion sur la base d'un graphe croissant - Google Patents

Procédé et système de recommandation de liste de diffusion sur la base d'un graphe croissant Download PDF

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Publication number
WO2019164040A1
WO2019164040A1 PCT/KR2018/002317 KR2018002317W WO2019164040A1 WO 2019164040 A1 WO2019164040 A1 WO 2019164040A1 KR 2018002317 W KR2018002317 W KR 2018002317W WO 2019164040 A1 WO2019164040 A1 WO 2019164040A1
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WO
WIPO (PCT)
Prior art keywords
playlist
playlists
user
tree graph
sound source
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/KR2018/002317
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English (en)
Korean (ko)
Inventor
최진원
정재헌
조민경
조성용
류대원
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Line Plus Corp
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Line Plus Corp
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Filing date
Publication date
Application filed by Line Plus Corp filed Critical Line Plus Corp
Priority to PCT/KR2018/002317 priority Critical patent/WO2019164040A1/fr
Priority to JP2020544831A priority patent/JP7106663B2/ja
Priority to KR1020207017758A priority patent/KR20200124215A/ko
Publication of WO2019164040A1 publication Critical patent/WO2019164040A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/901—Indexing; Data structures therefor; Storage structures
    • G06F16/9024—Graphs; Linked lists
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
    • G06F16/63—Querying
    • G06F16/638—Presentation of query results
    • G06F16/639—Presentation of query results using playlists
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/60—Information retrieval; Database structures therefor; File system structures therefor of audio data
    • G06F16/64—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the description below relates to a technique for recommending a playlist.
  • Korean Laid-Open Patent Publication No. 10-2013-0008696 (published Jan. 23, 2013) allows a user to create a playlist of available content by interacting with other users. Techniques are disclosed.
  • the playlist can be provided in the form of a graph with a plurality of nodes, which is a new recommendation method.
  • Providing playlists as a growing tree graph can provide additional pleasure through game elements.
  • a method of providing a playlist executed on a computer system comprising at least one processor configured to execute computer readable instructions contained in a memory, the method of providing a playlist by the at least one processor. Constructing a plurality of playlists based on the similarity between the music contents; And recommending, by the at least one processor, the playlist by providing a tree graph with each of the playlists as a node.
  • the recommending may include expanding the node while adding at least one or more depths based on the node selected in the tree graph.
  • the recommending may include disposing an initial playlist reflecting a user's preference for music content at a root node of the tree graph.
  • the recommending may further include gradually expanding nodes of the tree graph to further arrange other playlists as playlists disposed at each node of the tree graph are consumed. have.
  • the recommending step may include visualizing a node of a playlist consumed by the user on the tree graph.
  • the recommending may include visualizing the content consumption path of the user by marking and connecting a node of a playlist consumed by the user on the tree graph.
  • the playlist providing method may further include managing, by the at least one processor, data related to a user's consumption history for the playlist provided through the tree graph.
  • the configuring may include: extracting a sound source feature preferred by the user using data related to the consumption history of the user; And constructing a personalized playlist with songs corresponding to the extracted sound source feature.
  • constructing the personalized playlist includes constructing a plurality of personalized playlists having a common sound source feature that the user prefers and different sound source features between playlists. can do.
  • the configuring may include: generating a unique sound source feature by learning sound source data of the music content through a deep learning model for each of the music content; And calculating the similarity between the music contents using the sound source feature to construct the playlist based on the similarity between the music contents.
  • a non-transitory computer readable recording medium having a program recorded thereon for causing the computer to execute the playlist providing method.
  • a playlist providing system implemented with a computer system, comprising: a memory; And at least one processor coupled to the memory and configured to execute computer readable instructions contained in the memory, the at least one processor configured to play a plurality of playlists based on the similarity between music contents.
  • FIG. 1 is a diagram illustrating an example of a network environment according to an embodiment of the present invention.
  • FIG. 2 is a block diagram illustrating an internal configuration of an electronic device and a server according to an embodiment of the present invention.
  • FIG. 3 is a block diagram illustrating an example of components that may be included in a processor of a server according to an embodiment of the present invention.
  • FIG. 4 is a flowchart illustrating an example of a method that may be performed by a server according to an embodiment of the present invention.
  • FIG. 5 is a flowchart illustrating an example of a process of constructing a playlist in an embodiment of the present invention.
  • 6 to 9 are exemplary diagrams illustrating a process of recommending a playlist in a graph of a gradually growing tree in one embodiment of the present invention.
  • Embodiments of the present invention relate to a technique for recommending a playlist, and more particularly, to a technique for providing a playlist in the form of a graph having a plurality of nodes.
  • Embodiments including those specifically disclosed herein, can provide playlists in the form of graphs with multiple nodes, thereby providing significant advantages in terms of efficiency, variety, fun factor, convenience, cost savings, and the like. Can be achieved.
  • FIG. 1 is a diagram illustrating an example of a network environment according to an embodiment of the present invention.
  • the network environment of FIG. 1 illustrates an example including a plurality of electronic devices 110, 120, 130, and 140, a plurality of servers 150 and 160, and a network 170.
  • 1 is an example for describing the present invention, and the number of electronic devices or the number of servers is not limited as shown in FIG. 1.
  • the plurality of electronic devices 110, 120, 130, and 140 may be fixed terminals or mobile terminals implemented as computer systems.
  • Examples of the plurality of electronic devices 110, 120, 130, and 140 include a smart phone, a mobile phone, a navigation device, a computer, a notebook computer, a digital broadcasting terminal, a personal digital assistant (PDA), and a portable multimedia player (PMP).
  • PDA personal digital assistant
  • PMP portable multimedia player
  • Tablet PCs game consoles, wearable devices, wearable devices, Internet of things (IoT) devices, virtual reality (VR) devices, augmented reality (AR) devices, and the like.
  • IoT Internet of things
  • VR virtual reality
  • AR augmented reality
  • the electronic device 110 may be substantially different from the network 170 using a wireless or wired communication scheme. It may mean one of various physical computer systems capable of communicating with the electronic devices 120, 130, 140 and / or the servers 150, 160.
  • the communication method is not limited and includes not only a communication method using a communication network (for example, a mobile communication network, a wired internet, a wireless internet, a broadcasting network, a satellite network, etc.) that the network 170 may include, but also a short range wireless communication between devices.
  • a communication network for example, a mobile communication network, a wired internet, a wireless internet, a broadcasting network, a satellite network, etc.
  • the network 170 may include, but also a short range wireless communication between devices.
  • the network 170 may include a personal area network (PAN), a local area network (LAN), a campus area network (CAN), a metropolitan area network (MAN), a wide area network (WAN), and a broadband network (BBN). And one or more of networks such as the Internet.
  • the network 170 may also include any one or more of network topologies, including bus networks, star networks, ring networks, mesh networks, star-bus networks, trees, or hierarchical networks, but It is not limited.
  • Each of the servers 150 and 160 communicates with the plurality of electronic devices 110, 120, 130, and 140 through the network 170 to provide a command, code, file, content, service, or the like. It may be implemented in devices.
  • the server 150 may be a system that provides a first service to a plurality of electronic devices 110, 120, 130, and 140 connected through the network 170, and the server 160 may also have a network ( It may be a system that provides a second service to the plurality of electronic devices 110, 120, 130, and 140 connected through the 170.
  • the server 150 uses an application as a computer program installed and driven in the plurality of electronic devices 110, 120, 130, and 140 to provide a service (for example, a music service, etc.) targeted by the corresponding application.
  • the first service may be provided to the plurality of electronic devices 110, 120, 130, and 140.
  • the server 160 may provide a service for distributing the file for installing and driving the above application to the plurality of electronic devices 110, 120, 130, and 140 as a second service.
  • 2 is a block diagram illustrating an internal configuration of an electronic device and a server according to an embodiment of the present invention. 2 illustrates an internal configuration of the electronic device 110 and the server 150 as an example of the electronic device.
  • the other electronic devices 120, 130, 140, or the server 160 may also have the same or similar internal configuration as the above-described electronic device 110 or the server 150.
  • the electronic device 110 and the server 150 may include memories 211 and 221, processors 212 and 222, communication modules 213 and 223, and input / output interfaces 214 and 224.
  • the memories 211 and 221 are non-transitory computer readable recording media.
  • the memories 211 and 221 are non-transitory computer-readable recording media.
  • the memories 211 and 221 are non-transitory computer-readable recording media.
  • the non-volatile mass storage device such as a ROM, an SSD, a flash memory, a disk drive, or the like may be included in the electronic device 110 or the server 150 as a separate persistent storage device that is distinct from the memories 211 and 221.
  • the memory 211, 221 includes an operating system and at least one program code (for example, a code installed in the electronic device 110, a browser running on the electronic device 110, or an application installed in the electronic device 110 to provide a specific service). Can be stored.
  • These software components may be loaded from a computer readable recording medium separate from the memories 211 and 221.
  • Such a separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, disk, tape, DVD / CD-ROM drive, memory card, and the like.
  • software components may be loaded into the memory 211, 221 through a communication module 213, 223 that is not a computer readable recording medium.
  • At least one program is a computer program that is installed by files provided by a file distribution system (for example, the server 160 described above) through the network 170 to distribute installation files of developers or applications. It may be loaded into the memories 211 and 221 based on (for example, the above-described application).
  • a file distribution system for example, the server 160 described above
  • the network 170 to distribute installation files of developers or applications. It may be loaded into the memories 211 and 221 based on (for example, the above-described application).
  • Processors 212 and 222 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input / output operations. Instructions may be provided to the processors 212, 222 by the memory 211, 221 or the communication modules 213, 223. For example, the processors 212 and 222 may be configured to execute a command received according to a program code stored in a recording device such as the memory 211 and 221.
  • the communication modules 213 and 223 may provide a function for the electronic device 110 and the server 150 to communicate with each other through the network 170, and the electronic device 110 and / or the server 150 may be different from each other. It may provide a function for communicating with an electronic device (for example, the electronic device 120) or another server (for example, the server 160). For example, a request generated by the processor 212 of the electronic device 110 according to a program code stored in a recording device such as the memory 211 may be controlled by the server 150 through the network 170 under the control of the communication module 213. Can be delivered.
  • control signals, commands, contents, files, and the like provided according to the control of the processor 222 of the server 150 pass through the communication module 223 and the network 170 to the communication module 213 of the electronic device 110. ) May be received by the electronic device 110.
  • the control signal, command, content, file, etc. of the server 150 received through the communication module 213 may be transmitted to the processor 212 or the memory 211, and the content, file, etc. may be transferred to the electronic device ( 110 may be stored as a storage medium (permanent storage device described above) that may further include.
  • the input / output interface 214 may be a means for interfacing with the input / output device 215.
  • an input device may include a device such as a keyboard, a mouse, a microphone, a camera, and the like
  • an output device may include a device such as a display, a speaker, a haptic feedback device, and the like.
  • the input / output interface 214 may be a means for interfacing with a device in which functions for input and output are integrated into one, such as a touch screen.
  • the input / output device 215 may be configured as the electronic device 110 and one device.
  • the input / output interface 224 of the server 150 may be a means for interfacing with an apparatus (not shown) for input or output that may be connected to or included in the server 150.
  • a service configured using data provided by the server 150 or the electronic device 120 when the processor 212 of the electronic device 110 processes a command of a computer program loaded in the memory 211.
  • the screen or content may be displayed on the display through the input / output interface 214.
  • the electronic device 110 and the server 150 may include more components than those of FIG. 2. However, it is not necessary to clearly show most of the prior art components.
  • the electronic device 110 may be implemented to include at least some of the above-described input and output devices 215 or other components such as a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, a database, and the like. It may also include more.
  • GPS Global Positioning System
  • the electronic device 110 is a smartphone, an acceleration sensor or a gyro sensor, a camera module, various physical buttons, a button using a touch panel, an input / output port, a vibrator for vibration generally included in the smartphone Various components such as may be implemented to be further included in the electronic device 110.
  • a playlist refers to music content that is a recommendation target, and may mean a track collection including at least one music content having a common feature.
  • independent playlists are provided in the form of one-time recommendation, which is often only a one-time consumption.
  • the present invention can recommend a playlist through a tree graph with a plurality of nodes to provide the user with more choices for the playlist and support more active and cascaded consumption.
  • FIG. 3 is a block diagram illustrating an example of a component that a processor of a server according to an embodiment of the present invention may include, and FIG. 4 is an example of a method that the server may perform according to an embodiment of the present invention. It is a flowchart showing.
  • the server 150 serves as a platform for providing a music service to a plurality of electronic devices 110, 120, 130, and 140 that are clients.
  • the server 150 may provide a music service in association with an application installed on the electronic devices 110, 120, 130, and 140.
  • the processor 222 of the server 150 is a component for performing the playlist providing method according to FIG. 4, as shown in FIG. 3, the playlist constructing unit 310, the playlist providing unit 320, and The consumption history manager 330 may be included.
  • the components of the processor 222 may be optionally included in or excluded from the processor 222.
  • the components of the processor 222 may be separated or merged to represent the functions of the processor 222.
  • the processor 222 and the components of the processor 222 may control the server 150 to perform steps S410 to S440 included in the playlist providing method of FIG. 4.
  • the processor 222 and the components of the processor 222 may be implemented to execute instructions according to code of an operating system included in the memory 221 and code of at least one program.
  • the components of the processor 222 may be representations of different functions of the processor 222 performed by the processor 222 according to an instruction provided by the program code stored in the server 150.
  • the playlist constructer 310 may be used as a functional representation of the processor 222 controlling the server 150 in accordance with the above-described instructions such that the server 150 constructs a playlist.
  • the processor 222 may read a command required from the memory 221 loaded with a command related to the control of the server 150.
  • the read command may include a command for controlling the processor 222 to execute steps S420 to S440 to be described later.
  • the playlist organizer 310 may configure a plurality of playlists made of similar music contents based on the similarity between the music contents.
  • a user interface in the form of a tree graph may be used, and a recommended playlist may be provided through each node of the tree graph.
  • the playlist constructing unit 310 may configure a playlist personalized to the user based on data related to the user's consumption history of the music content (hereinafter referred to as 'consumption history data').
  • the playlist constructing unit 310 may extract a sound source feature that the user prefers using the consumption history data of the user, and construct a personalized playlist with songs corresponding to the extracted sound source feature.
  • the sound source feature may mean not only a melody but also a parameter for determining a feature of music content such as an album, an artist, a genre, a beat, and the like.
  • the playlist organizer 310 may compose a plurality of personalized playlists with songs having a sound source characteristic that a user prefers based on the similarity between music contents, and the plurality of personalized playlists may have some similarity. At the same time, they can be appropriately differentiated.
  • the playlist constructing unit 310 may configure a plurality of personalized playlists having a common sound source feature that the user prefers and different sound source features between playlists. For example, the playlist organizer 310 creates a playlist with songs of the same genre as the user's favorite genre, and at this point, the playlist may create different playlists.
  • the playlist constructer 310 may construct a playlist using a deep learning based learning model.
  • 5 is a flowchart illustrating an example of a process of constructing a playlist according to an embodiment of the present invention.
  • the playlist organizer 310 may receive sound source data of music content for each music content and process the received sound source data in the form of learning data.
  • the music content may mean all digital data having an audio file format, and may include, for example, MP3 (MPEG AudioLayer-3), WAVE (Waveform AudioFormat), FLAC (Free Lossless AudioCodec), and the like.
  • the playlist organizer 310 may receive text information related to the music content together, wherein the text information includes lyrics or meta information such as a singer, genre, title, album name, and the like. It may include information such as hashtags and queries input in relation to classification or search. Subsequently, the playlist configuration unit 310 may express the sound source data in time-frequency through preprocessing.
  • the playlist organizer 310 may convert the sound source data into time-frequency-size data such as Mel-spectrogram or Mel Frequency Cepstral Coefficient (MFCC).
  • the playlist configuration unit 310 may preprocess the text information when the text information is received together with the music content.
  • the playlist constructer 310 may filter meaningless texts from the input text information using a language preprocessor such as a morpheme analyzer and an index word extractor.
  • the playlist constructing unit 310 removes unnecessary parts of speech or special symbols (!,?, /, Etc., etc.) included in text information, such as surveys and quiet verbs, and removes words corresponding to the message or root. Can be extracted.
  • the playlist constructing unit 310 may learn the preprocessed learning data on the sound source data through a learning model to generate a unique feature, and then store the generated unique feature in a database (not shown).
  • the playlist constructing unit 310 may generate unique acoustic features of the sound source data itself using deep learning.
  • the playlist constructer 310 may use a learning model based on a convolutional neural network (CNN).
  • CNN convolutional neural network
  • the playlist configuration unit 310 may express the sound source data as a multidimensional real vector using a CNN learning model.
  • the CNN learning model may include a sound source data learning layer, and steps 1 to 3 below may be examples of a process of generating a real vector corresponding to sound source data in the sound source data learning layer.
  • the sound source data (for example, mp3 file) of the music content may be converted into data of a time-frequency-size type such as mel-spectrogram or MFCC through preprocessing.
  • a plurality of frequency frames for one or more short time intervals (1 second to 10 seconds) may be sampled from the sound source data converted in step 2 and used as input data for the learning model of the sound source data.
  • the playlist organizer 310 may sample a plurality of frames and use the input as a CNN model that is presented as an example of a music model in the sound source data learning layer. Therefore, the CNN model for learning sound source data may be a model having the same number of channels as the number of sampled frames.
  • each generated frame can be used as a single channel, and after convolutional / pooling of each frame, the generated feature vectors for each frame can be bonded to each other to be used as an input of a fully connected layer.
  • an abstracted feature may be generated from a music frame by repeatedly configuring a plurality of convolutional and pooling layers that the sound source data learning layer may include.
  • the patch size can be configured in various ways.
  • the pooling techniques such as the pooling technique using the maximum pooling degree (max), the pooling technique using the average value, and the hybrid pooling technique combining the two pooling techniques, At least one may be used.
  • the multiple convolutional and pooling layers is a fully-connected layer for generating acoustic features, with the functions of each layer being the sigmoid function, the hyperbolic tangent function, and the ReLU ( Various functions such as Rectified Linear Unit) function can be used.
  • the text information may also be expressed as a multidimensional real vector.
  • the playlist constructing unit 310 may generate texts filtered through preprocessing of the text information into a word vector using a previously trained learning model.
  • the word vector may be expressed in the form of a numerical multidimensional vector.
  • a word appearance frequency histogram, a term frequency (TF) / inverse document frequency (IDF), a language learning model eg, word2vec, phrase2vec, document2vec, etc.
  • TF term frequency
  • IDF inverse document frequency
  • a language learning model eg, word2vec, phrase2vec, document2vec, etc.
  • the order of the text information fields (singer, genre, title, year, etc.) for the language learning model is not fixed and can be changed to suit the purpose.
  • the playlist organizer 310 may store and maintain a feature vector for sound source data and a word vector for text information in a database for each music content.
  • the feature vector for the sound source data and the word vector for the text information may be constructed in separate databases, or in one database.
  • Such a database may be implemented as a component included in the server 150 or may exist as an external database built on a separate system interoperable with the server 150.
  • the playlist organizer 310 may calculate the similarity between the sound sources using the unique characteristics of the sound source data stored in the database for each music content, and play the music content based on the similarity between the sound sources. You can configure lists automatically. For example, the playlist constructing unit 310 may calculate the similarity between sound sources using the feature vector for the sound source data, and in another example, by using the word vector for the text information in combination with the feature vector for the sound source data. Similarity between sound sources can be calculated. In this case, the playlist constructing unit 310 plays as a seed song at least one of songs recently consumed by the user or songs constituting a playlist currently being consumed by the user as a seed song and plays in a chain form of songs similar to the seed song. You can construct an infinite list. In addition, the playlist organizer 310 may configure a playlist with songs that the user prefers based on the user's consumption history data for the music content.
  • the present invention can automatically construct a playlist with similar songs based on the similarity between the music contents using a model of learning the sound source data and the text information of the music content.
  • the playlist provider 320 may recommend a playlist by providing a tree graph of nodes of the playlists configured in operation S420.
  • the playlist provider 320 may recommend the playlist through a tree graph in which the depth and the number of nodes are fixed in advance.
  • the playlist provider 320 may add at least one depth based on the nodes of the playlist selected in the tree graph. You can recommend playlists as you expand nodes gradually.
  • the playlists of nodes connected to each other have similarities to each other and some degree of difference.
  • the playlist providing unit 320 arranges playlists of songs such as a genre that the user prefers in each node. In this case, the playlist of each node may be composed of different songs.
  • the root node is an initial playlist S
  • the initial playlist S may be a playlist reflecting user preferences.
  • the playlist S of the root node may be composed of songs corresponding to a sound source feature extracted from a user's consumption history data accumulated for a predetermined time, that is, a user's favorite sound source feature.
  • the playlist provider 320 may recommend playlists in a form in which nodes of the tree graph 600 are expanded starting from the root node.
  • the playlist constructing unit 310 may display a new playlist (based on the playlist S of the root node while the user consumes the playlist S of the root node or at the time when the consumption is completed). I to IV) can be configured.
  • the playlist provider 320 expands the child node from the root node of the tree graph 600 when a predetermined time (for example, 1 day) elapses after the user completes the consumption of the playlist S of the root node.
  • the newly configured playlists I to IV can be added to the nodes.
  • the playlist configuration unit 310 may display a new playlist while the user is consuming the playlist of any of the playlists I to IV in the tree graph 600 or at the time when the consumption is completed. (V ⁇ XI) and the playlist providing unit 320 expands the node of the tree graph 600 to the expanded node when a predetermined time (for example, 1 day) has elapsed after completing the consumption of the previous playlist. Newly configured playlists (V-XI) can be added.
  • the nodes of the tree graph 600 may be expanded the next day after the initial playlist S is consumed, and new playlists I to IV may be additionally arranged.
  • playlist II is consumed in the tree graph 600 in which playlists I to IV are additionally arranged
  • the next day when the child node for recommending a new playlist is expanded from the node of playlist II and the playlist III is consumed the next day.
  • a child node for recommending a new playlist from the node of playlist III may be expanded.
  • the child node is expanded or the next node among the nodes of the same depth as the corresponding node the next day. It is also possible to expand child nodes in batches at all nodes in the depth.
  • the node expansion rule and the order of the tree graph 600 can be changed as many as possible.
  • Playlists of nodes connected to each other have similarities to each other and have some degree of difference, and child nodes from one parent node have some degree of similarity and difference.
  • the user may freely move the node in the tree graph 600 to consume the recommended playlist, and the playlist provider 320 may visualize the history of the playlist consumed by the user on the tree graph 600. Can be.
  • the playlist provider 320 may visualize the content consumption path by marking and connecting the nodes of the playlist consumed by the user in the tree graph 600.
  • the playlist providing unit 320 may be connected with other nodes that do not consume nodes of a playlist (eg, S, II, III, VI, and V) consumed by the user on the tree graph 600. Can be distinguished.
  • the consumption history manager 330 may manage data related to the consumption history of the user for the playlist provided through the tree graph.
  • the consumption history manager 330 may store and manage the music reproduction log of each user who uses the music service as consumption history data.
  • the consumption history data includes information about playlists consumed by the user. For each playlist, the identifier of the playlist (eg, ID), the number of times the playlist is played, and the total playing time of the playlist The percentage of play that has been played, user feedback on the playlist (e.g. user response like 'likes', 'favorite' registration, etc.), whether or not the playlist has been played continuously, Time and user location at the time.
  • the location may be automatically collected from the electronic device 110 of the user for the user who previously agreed to the collection of location information.
  • the consumption history manager 330 may store and manage metadata including music information included in a playlist consumed by the user, for example, metadata including song identifier, album information, artist information, and genre information as consumption history data. Can be.
  • the consumption history manager 330 may store and manage external information related to the playlist consumed by the user, for example, weather or social issues at the time when the playlist is consumed as consumption history data.
  • the consumption history data may be used to analyze the user's consumption behavior of the playlist, and in particular, the playlist organizer 310 may be used to personalize the playlist.
  • the consumption history data of playlists consumed by the user through the tree graph can be collected and used for organizing the playlist in the next recommendation process to further refine playlist recommendation.
  • the tree graph can be used to collect more detailed and sophisticated feedback data in an indirect manner, which makes it easier to understand the user's consumption history and behavior and to personalize playlists to the user. Can be used to construct
  • the apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components.
  • the devices and components described in the embodiments may include a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable PLU (programmable). It can be implemented using one or more general purpose or special purpose computers, such as logic units, microprocessors, or any other device capable of executing and responding to instructions.
  • the processing device may execute an operating system (OS) and one or more software applications running on the operating system.
  • the processing device may also access, store, manipulate, process, and generate data in response to the execution of the software.
  • OS operating system
  • the processing device may also access, store, manipulate, process, and generate data in response to the execution of the software.
  • processing device includes a plurality of processing elements and / or a plurality of types of processing elements. It can be seen that it may include.
  • the processing device may include a plurality of processors or one processor and one controller.
  • other processing configurations are possible, such as parallel processors.
  • the software may include a computer program, code, instructions, or a combination of one or more of the above, and configure the processing device to operate as desired, or process it independently or collectively. You can command the device.
  • the software and / or data may be embodied in any type of machine, component, physical device, computer storage medium or device in order to be interpreted by or provided to the processing device or to provide instructions or data. have.
  • the software may be distributed over networked computer systems so that they may be stored or executed in a distributed manner. Software and data may be stored on one or more computer readable recording media.
  • the method according to the embodiment may be embodied in the form of program instructions that can be executed by various computer means and recorded in a computer readable medium.
  • the medium may be to continuously store a program executable by the computer, or to temporarily store for execution or download.
  • the medium may be a variety of recording means or storage means in the form of a single or several hardware combined, not limited to a medium directly connected to any computer system, it may be distributed on the network. Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, And ROM, RAM, flash memory, and the like, configured to store program instructions.
  • examples of another medium may include a recording medium or a storage medium managed by an app store that distributes an application, a site that supplies or distributes various software, a server, or the like.

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Reverberation, Karaoke And Other Acoustics (AREA)

Abstract

L'invention concerne un procédé permettant de fournir une liste de diffusion, ledit procédé consistant à : configurer de multiples listes de diffusion d'après la similarité entre des contenus musicaux ; et recommander la liste de diffusion en fournissant un graphe d'arborescence ayant chaque liste de diffusion comme nœud.
PCT/KR2018/002317 2018-02-26 2018-02-26 Procédé et système de recommandation de liste de diffusion sur la base d'un graphe croissant Ceased WO2019164040A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
PCT/KR2018/002317 WO2019164040A1 (fr) 2018-02-26 2018-02-26 Procédé et système de recommandation de liste de diffusion sur la base d'un graphe croissant
JP2020544831A JP7106663B2 (ja) 2018-02-26 2018-02-26 成長グラフ基盤のプレイリスト推薦方法およびシステム
KR1020207017758A KR20200124215A (ko) 2018-02-26 2018-02-26 성장 그래프 기반의 플레이리스트 추천 방법 및 시스템

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/KR2018/002317 WO2019164040A1 (fr) 2018-02-26 2018-02-26 Procédé et système de recommandation de liste de diffusion sur la base d'un graphe croissant

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WO2019164040A1 true WO2019164040A1 (fr) 2019-08-29

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WO (1) WO2019164040A1 (fr)

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JP2025073906A (ja) * 2023-10-27 2025-05-13 株式会社 ミックウェア プログラム、情報処理装置

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KR20100095166A (ko) * 2009-02-20 2010-08-30 성균관대학교산학협력단 사용자들의 재생 목록 분석을 통한 맞춤형 음악 추천 방법
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KR20170136200A (ko) * 2016-06-01 2017-12-11 네이버 주식회사 음원 컨텐츠 및 메타 정보를 이용한 플레이리스트 자동 생성 방법 및 시스템

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KR100917086B1 (ko) * 2001-09-10 2009-09-15 톰슨 라이센싱 디지털 오디오 데이터 플레이어에서 인덱싱 재생목록을생성하는 방법 및 장치
US20080091721A1 (en) * 2006-10-13 2008-04-17 Motorola, Inc. Method and system for generating a play tree for selecting and playing media content
KR20100095166A (ko) * 2009-02-20 2010-08-30 성균관대학교산학협력단 사용자들의 재생 목록 분석을 통한 맞춤형 음악 추천 방법
US20120136814A1 (en) * 2010-11-30 2012-05-31 Beijing Ruixin Online System Technology Co., Ltd Music recommendation method and apparatus
KR20170136200A (ko) * 2016-06-01 2017-12-11 네이버 주식회사 음원 컨텐츠 및 메타 정보를 이용한 플레이리스트 자동 생성 방법 및 시스템

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