Recommended song menu customization method and device, equipment, storage medium and computer program product

Abstract

The invention relates to the technical field of data processing, and discloses a recommended song menu customization method, device and equipment, a storage medium and a computer program product, and the method comprises the steps: obtaining user identity information of a current user, obtaining a music playing record and a user portrait of the current user based on the user identity information, and obtaining a recommended song menu based on the music playing record; and obtaining a user preference song list of the current user, and screening songs in the user preference song list according to the user portrait to obtain a recommended song list. Therefore, the music playing record and the user portrait of the corresponding current user can be obtained according to the obtained user identity information, the user preference song list of the current user is obtained based on the music playing record of the user, and the songs in the user preference song list are screened according to the user portrait. Due to the fact that the music playing record of the current user and the user portrait are combined, the effect of more accurately matching the song listening requirement of the user is achieved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment, storage medium and computer program product for customizing a recommended playlist. Background Art

[0002] As the level of automobile intelligence continues to improve, smart entertainment car machines have become an important part of automobiles. As one of the important functions of smart entertainment car machines, the user experience of music software directly affects the driving pleasure of car owners. How to recommend appropriate playlists to users and encourage them to use the music software services on smart entertainment car machines has become a question worth discussing.

[0003] The existing ones recommend the most popular song lists to users based on online search popularity or playback volume. The recommended song lists are often unable to be flexibly customized according to the car owner's listening preferences, and thus cannot accurately match the car owner's listening needs, resulting in low usage of music software services on smart entertainment car machines. Summary of the invention

[0004] The main purpose of this application is to provide a method for customizing a recommended playlist, aiming to solve the problem that the existing technology cannot flexibly customize recommendations based on the car owner's listening preferences, and thus cannot accurately match the user's listening needs.

[0005] To achieve the above purpose, the present application proposes a method for customizing a recommended playlist, the method comprising:

[0006] Obtaining user identity information of the current user, and obtaining music playing records and a user portrait of the current user based on the user identity information;

[0007] Obtaining a user preferred playlist of the current user based on the music playing record;

[0008] The songs in the user's preferred playlist are filtered according to the user portrait to obtain a recommended playlist.

[0009] In one embodiment, the step of obtaining a user preferred playlist based on the music playing record includes:

[0010] Acquire a user preference tag of the current user based on the music playing record;

[0011] The preset music library is filtered according to the user preference tag to obtain the user preference song list of the current user.

[0012] In one embodiment, the step of obtaining the user preference tag based on the music playing record includes:

[0013] Determining the music type and the number of times the music is played based on the music playing record, and generating a user preference list based on the music type and the number of times the music is played;

[0014] The user preference list content is spliced ​​into a preset prompt word template to obtain a user preference description text, and a user preference label is obtained according to the user preference description text using a preset large model.

[0015] In one embodiment, after the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist, the step further includes:

[0016] Determine a play time period based on the music play record, and when the play time period is detected, ask the user whether to play the recommended playlist;

[0017] If so, the songs in the recommended playlist are played.

[0018] In one embodiment, the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist includes:

[0019] Determine user attributes and user payment behavior according to the user portrait, the user attributes include: user age and / or user gender, and the user payment behavior includes: at least one of user payment level, user payment frequency, and user payment times;

[0020] The songs in the user's preferred playlist are filtered based on the user's attributes and the user's payment behavior to obtain a recommended playlist.

[0021] In one embodiment, the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist further includes:

[0022] Filter the songs in the user's preferred playlist according to the user portrait to obtain an initial playlist;

[0023] Performing similarity scoring between the music genres corresponding to the songs in the initial playlist and the preference tags;

[0024] Generate a recommended playlist based on the similarity score results.

[0025] In addition, to achieve the above purpose, the present application also proposes a recommended playlist customization device, the device comprising:

[0026] An information acquisition module, used to acquire the user identity information of the current user, and acquire the music playing record and user portrait of the current user based on the user identity information;

[0027] A song list generation module, used to obtain a user preferred song list of the current user based on the music playing record;

[0028] The playlist screening module is used to screen the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes a recommended playlist customization device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the recommended playlist customization method described above.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the recommended playlist customization method described above are implemented.

[0031] In addition, in order to achieve the above-mentioned purpose, the present application also proposes a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the method for customizing a recommended playlist as described above are implemented.

[0032] The present application proposes a method, device, equipment, storage medium and computer program product for customizing a recommended playlist, the method comprising: obtaining the user identity information of the current user, obtaining the music playing record and user portrait of the current user based on the user identity information, obtaining the user preferred playlist of the current user based on the music playing record, filtering the songs in the user preferred playlist according to the user portrait, and obtaining a recommended playlist. This means that the present application obtains the music playing record and user portrait of the corresponding current user based on the obtained user identity information, obtains the user preferred playlist of the current user based on the user music playing record, and then filters the songs in the user preferred playlist according to the above user portrait to obtain a recommended playlist. Since the present application combines the music playing record and user portrait of the current user, it can achieve a more accurate match of the user's listening needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0035] Figure 1 This is a flow chart of a first embodiment of a method for customizing a recommended playlist proposed in an embodiment of the present application;

[0036] Figure 2 This is an example diagram of a user preferred playlist in the recommended playlist customization method proposed in this embodiment;

[0037] Figure 3 This is an example diagram of a user preference list in the method for customizing a recommended playlist proposed in this embodiment;

[0038] Figure 4 This is an example diagram of a user's preferred playlist in the recommended playlist customization method proposed in an embodiment of the present application;

[0039] Figure 5 This is a flow chart of a second embodiment of the method for customizing a recommended playlist proposed in this embodiment;

[0040] Figure 6 A diagram of a device for customizing a recommended playlist provided in an embodiment of the present application;

[0041] Figure 7 A schematic diagram of the structure of a recommended playlist customization device suitable for implementing an embodiment of the present application.

[0042] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0044] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0045] It should be noted that all directional indications in the embodiments of the present application (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0046] It is understandable that with the continuous improvement of the intelligence of automobiles, smart entertainment car machines have become an important part of automobiles. As one of the important functions of smart entertainment car machines, the user experience of music software directly affects the driving pleasure of car owners. How to recommend appropriate playlists to users and encourage them to use the music software services on smart entertainment car machines has become a question worth discussing.

[0047] The car entertainment system of traditional smart connected cars recommends the most popular song lists to users based on the popularity of online searches or the amount of playback. The recommended playlists are often unable to be flexibly customized according to the car owner's listening preferences, and thus cannot accurately match the car owner's listening needs, resulting in low usage of music software services on smart entertainment car machines. In recent years, with the rapid development of the field of smart entertainment cockpits in smart connected cars, large language model technology has gradually been applied to the field of smart cockpit entertainment services. Large language model agents represented by ChatGPT perform well in general fields, but due to the lack of song list recommendation data training during the pre-training process of the large language model, it cannot effectively adapt to downstream tasks such as customized song list recommendations. The car entertainment cockpit intelligent recommendation technology based on large language models still needs to be developed.

[0048] Therefore, in order to solve the problem that the existing technology cannot make flexible and customized recommendations based on the car owner's listening preferences, and thus cannot accurately match the user's listening needs, this embodiment proposes a method for customizing a recommended song list, which includes: obtaining the user identity information of the current user, obtaining the current user's music playback record and user portrait based on the user identity information, obtaining the current user's user preference song list based on the music playback record, and filtering the songs in the user preference song list according to the user portrait to obtain a recommended song list. This shows that this embodiment obtains the corresponding current user's music playback record and user portrait based on the acquired user identity information, and obtains the current user's user preference song list based on the user's music playback record, and then filters the songs in the user preference song list according to the above user portrait to obtain a recommended song list. Since this embodiment combines the current user's music playback record and user portrait, it can achieve the effect of more accurately matching the user's listening needs.

[0049] For ease of understanding, the following combination Figures 1 to 7 The recommended playlist customization method provided in the embodiment of the present application and the recommended playlist customization method, device, equipment, storage medium and computer program product provided in the following embodiments are introduced in detail.

[0050] The present application embodiment provides a method for customizing a recommended playlist, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the method for customizing a recommended playlist proposed in an embodiment of the present application.

[0051] like Figure 1As shown, the method includes:

[0052] Step S10: Obtain the user identity information of the current user, and obtain the music playing record and user portrait of the current user based on the user identity information.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with the functions of customizing recommended playlists, network communication, and program running, such as a vehicle control system, or an electronic device capable of realizing the above functions. This embodiment adopts a recommended playlist customization device (hereinafter referred to as the device), which is equipped on the car as a component of the smart cockpit and can also be applied to other scenarios. This embodiment takes the device equipped on the car as an example for explanation but does not specifically limit this embodiment. The device can be produced by the vehicle itself or installed later, and this embodiment does not limit it.

[0054] It should also be noted that the above-mentioned current user may be the user currently driving the above-mentioned car equipped with the above-mentioned device, or the user corresponding to the user name logged in on the vehicle system, and the above-mentioned user identity information may be identity information specified by numbers or symbols, which is used to enable the above-mentioned device to determine the music playback record and user portrait corresponding to the user who needs to be recommended. The above-mentioned music playback record may refer to the user's behavior record of playing songs on the music software (such as the number of plays, playback time, collection, likes and other data), and the above-mentioned user portrait may be a description of the user based on the user's user attributes (such as age and / or gender, etc.) and user behavior habits (such as consumption habits, etc.).

[0055] In a specific implementation, the above device may include a user portrait library or be connected to the user portrait library, the user portrait library may be a library for storing user portraits, and the above device also includes a memory for storing the playback records of the above music. When the vehicle is started, the above device may query the user identity information of the current user according to the connected vehicle computer or other user-interactive device, and retrieve the music playback records and user portrait of the current user corresponding to the above user identity information from the memory or the user portrait library according to the identity information.

[0056] Step S20: Obtain the user preference playlist of the current user based on the music playing record.

[0057] refer to Figure 2 , Figure 2This is an example diagram of a user preferred playlist in the recommended playlist customization method proposed in this embodiment. It should be noted that the above-mentioned user preferred playlist can be a playlist composed of multiple music lists (for example, {A, B, C}, where A, B, C are music lists composed of several pieces of music), and the above-mentioned music list is a list composed of a preset number of songs (for example, five songs constitute a list A{a, b, c, d, e}).

[0058] Furthermore, in this embodiment, the device further includes a preset music library, and the step of obtaining a user preferred playlist based on the music playing record includes:

[0059] Acquire a user preference tag of the current user based on the music playing record;

[0060] The preset music library is filtered according to the user preference tag to obtain the user preference song list of the current user.

[0061] It should be noted that the above-mentioned user preference tags can be tags used to describe the user's music preferences, such as pop, rock, classical, electronic, jazz, etc., and the above-mentioned preset music library can be a database that stores a number of music data classified according to the above-mentioned tags.

[0062] In a specific implementation, the device will analyze the current user's music playback records, such as the number of plays, favorites, likes, playback time, etc., and use natural language processing and machine learning technologies such as large language models to identify the user's preferred music style, genre, singer, album, etc., so as to generate personalized user preference tags for the user. In this embodiment, the above-mentioned tags can be tags preset by the user and stored in the library, or tags generated according to the music type in the music playback record, but in order to uniformly divide the above-mentioned tags and classify the above-mentioned preset music library, this embodiment uses preset tags for illustration, but does not specifically limit this embodiment.

[0063] Furthermore, the music playing record includes: music type and number of playing times, and the step of obtaining the user preference tag based on the music playing record includes:

[0064] Determining the music type and the number of times the music is played based on the music playing record, and generating a user preference list based on the music type and the number of times the music is played;

[0065] The user preference list content is spliced ​​into a preset prompt word template to obtain a user preference description text, and a user preference label is obtained according to the user preference description text using a preset large model.

[0066] It should be noted that the above-mentioned music type can be the music genre to which the song belongs, such as pop, rock, electronic, classical, jazz, etc. The above-mentioned play count can be the number of times a user plays a certain song or the total number of times a certain music type is played. The above-mentioned user preference list can be a record of the music types preferred by the user and their play counts. For details, please refer to Figure 3 , Figure 3 This is an example diagram of a user preference list in the method for customizing a recommended song list proposed in this embodiment. It includes user identity information (i.e. Figure 3 The above prompt word template can be a template for generating a user preference description text, such as "user likes..., number of plays...; user likes..., number of plays...; user likes..., number of plays...".

[0067] The above-mentioned preset large model can be a large language model trained based on playlist recommendation data, which is used to summarize and query based on the description text. The large language model technology is an important branch in the field of artificial intelligence, and its purpose is to develop a large-scale model that can understand and generate natural language. These models are usually based on deep learning architectures represented by deep neural network models based on self-attention mechanisms. Thanks to the self-attention mechanism in its network, it performs well in processing long texts and capturing fine-grained language patterns. With the increase in computing power and the amount of data, the number of parameters of the large language model has also grown rapidly, thereby improving the performance and accuracy of the model in multiple dimensions. This model learns the paradigm of language by predicting the probability distribution of the next word, and can then complete text generation, translation, summarization, question and answer, and many other complex language understanding tasks. The application scope of large language models continues to expand, covering multiple fields from chatbots, personalized recommendation systems to advanced text analysis, and has had a profound impact on promoting the development and application of artificial intelligence.

[0068] In a specific implementation, the above-mentioned device can summarize the above-mentioned music types and play times through the above-mentioned preset large model to obtain a user preference list, and organize the user preference list according to the preset prompt word model to obtain a user preference description text (for example, the car owner played <song type 1> a total of <play times 1> times, and played <song type 2> a total of <play times 2> times). The above-mentioned device then uses the text understanding and query capabilities of the above-mentioned preset large model to select songs with music types consistent with the above-mentioned user preference tags from the preset music library according to the above-mentioned user preference description text, and divide the songs into several music lists according to the preset number, and integrate the above-mentioned music lists to obtain the user preference playlist. For specific reference Figure 4 , Figure 4 This is an example diagram of a user preferred playlist in the method for customizing a recommended playlist proposed in the embodiment of the present application. It at least includes: user identity information (i.e. Figure 4The car owner ID) and the user's preferred playlist (i.e. Figure 4 The recommended playlist list includes ["Confession Balloon", "Little Luck", "Let's Go", "Confession Night", "Confession Balloon (Piano Version)"]; ["You Once", "Road Song", "Ordinary Road", "Nothing", "Boundless Ocean and Sky"] are two music lists with a preset number of 5).

[0069] Step S30: Filter the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist.

[0070] In a specific implementation, the above-mentioned device can analyze the user through the above-mentioned user portrait to obtain the user's personality constraints, and compare the songs in the above-mentioned user's preferred playlist with the above-mentioned constraints to filter out songs that do not meet the conditions to obtain a recommended playlist.

[0071] In a certain example, the user portrait may be that the user has no consumption habits, and the device will filter out free songs as songs in the recommended playlist based on the portrait content.

[0072] This embodiment proposes a method for customizing a recommended playlist, which includes: obtaining the user identity information of the current user, obtaining the music playing record and user portrait of the current user based on the user identity information, obtaining the user preferred playlist of the current user based on the music playing record, and filtering the songs in the user preferred playlist according to the user portrait to obtain a recommended playlist. This shows that this embodiment obtains the corresponding music playing record and user portrait of the current user based on the obtained user identity information, and obtains the user preferred playlist of the current user based on the user music playing record, and then filters the songs in the user preferred playlist according to the above user portrait to obtain a recommended playlist. Since this embodiment combines the music playing record and user portrait of the current user, it can achieve the effect of more accurately matching the user's listening needs.

[0073] Based on the first embodiment, in the second embodiment, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be described in detail later. Figure 5 , Figure 5 This is a flow chart of a second embodiment of the method for customizing a recommended playlist proposed in this embodiment. Further, the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist includes:

[0074] Step S31: determining user attributes and user payment behavior according to the user portrait, wherein the user attributes include: user age and / or user gender, and the user payment behavior includes: at least one of user payment level, user payment frequency, and user payment times;

[0075] Step S32: Filter the songs in the user's preferred playlist based on the user attributes and the user's payment behavior to obtain a recommended playlist.

[0076] It should be noted that the user attribute can be any one of the user age and user gender, or both. The user payment classification can be a classification based on the user's historical consumption. The user payment frequency can be a frequency determined based on the user's historical consumption frequency (for example, a monthly consumption frequency of more than 30 times is a high frequency). The user payment frequency can be a frequency based on the recorded user's historical consumption frequency (for example, the consumption frequency last month was 20 times; the average annual consumption is 40 times, etc.). The device can summarize the above content and filter the above songs through a large language model or a small model. In this embodiment, a large language model is used for illustration, but no specific limitation is made.

[0077] In a specific implementation, the above-mentioned device generates a user portrait description text by splicing the acquired user portrait content into a prompt word template, and uses a preset large model to filter the user's preferred playlist according to the above-mentioned user portrait description text to obtain a recommended playlist.

[0078] Furthermore, after the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist, the method further includes:

[0079] Determine a play time period based on the music play record, and when the play time period is detected, ask the user whether to play the recommended playlist;

[0080] If so, the songs in the recommended playlist are played.

[0081] It should be noted that the above-mentioned playback time period can be a time interval determined by analyzing the user's music playback record through an algorithm based on the time point or time period when the user habitually listens to music. In a specific implementation, the above-mentioned device determines a set of specific playback time periods (for example, 9:00-10:00) based on the analysis of the user's historical music playback records. When it is detected that the current time is in one of these predetermined playback time periods, the above-mentioned device will actively ask the user whether he is willing to play the song list recommended for him. If the user agrees, the above-mentioned device will immediately start playing the songs in the recommended playlist.

[0082] Furthermore, in order to make the recommendation more accurate, the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist also includes:

[0083] Filter the songs in the user's preferred playlist according to the user portrait to obtain an initial playlist;

[0084] Performing similarity scoring between the music genres corresponding to the songs in the initial playlist and the preference tags;

[0085] Generate a recommended playlist based on the similarity score results.

[0086] It should be noted that the above-mentioned initial playlist can be a list of songs obtained through preliminary screening based on the user's preferred playlist as a candidate set for the recommended playlist. In a specific implementation, the above-mentioned device finely screens the songs in the user's preferred playlist according to the constructed user portrait, thereby generating an initial playlist. Then, the above-mentioned device will compare the music type corresponding to each song in the initial playlist with the user's preferred label and perform a similarity score. Finally, based on the result of the similarity score, the above-mentioned device generates a recommended playlist that is more in line with the user's preferences.

[0087] This embodiment also provides a recommended song list customization device, please refer to Figure 6 , Figure 6 A diagram of a recommended playlist customization device provided in an embodiment of the present application, wherein the recommended playlist customization device includes:

[0088] An information acquisition module, used to acquire the user identity information of the current user, and acquire the music playing record and user portrait of the current user based on the user identity information;

[0089] A song list generation module, used to obtain a user preferred song list of the current user based on the music playing record;

[0090] The playlist screening module is used to screen the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist.

[0091] The recommended song list customization device provided in this embodiment adopts the recommended song list customization method in the above embodiment, which can solve the problem that the prior art cannot flexibly customize recommendations based on the car owner's listening preferences, and thus cannot accurately match the user's listening needs. Compared with the prior art, the beneficial effects of the recommended song list customization device provided in this embodiment are the same as the beneficial effects of the recommended song list customization method provided in the above embodiment, and the other technical features in the recommended song list customization device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0092] The present embodiment provides a recommended playlist customization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the recommended playlist customization method in the above-mentioned embodiment one.

[0093] Reference below Figure 7 , Figure 7 The structure diagram of the recommended song list customization device suitable for implementing the embodiment of the present application is shown in FIG. The recommended song list customization device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The recommended playlist customization device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0094] like Figure 7 As shown, the recommended song list customization device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the recommended song list customization device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the recommended song list customization device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a recommended song list customization device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0095] In particular, according to the present embodiment, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the present embodiment includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present embodiment are executed.

[0096] The recommended song list customization device provided in this embodiment adopts the recommended song list customization method in the above embodiment, which can solve the problem that the prior art cannot make flexible customized recommendations based on the car owner's listening preferences, and thus cannot accurately match the user's listening needs. Compared with the prior art, the beneficial effects of the recommended song list customization device provided in this embodiment are the same as the beneficial effects of the recommended song list customization method provided in the above embodiment, and the other technical features in the recommended song list customization device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0097] It should be understood that the various parts disclosed in this embodiment can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0098] The above is only a specific implementation of this embodiment, but the protection scope of this embodiment is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in this embodiment, which should be included in the protection scope of this embodiment. Therefore, the protection scope of this embodiment should be based on the protection scope of the claims.

[0099] This embodiment provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for customizing the recommended playlist in the above-mentioned embodiment.

[0100] The computer-readable storage medium provided in this embodiment may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0101] The above-mentioned computer-readable storage medium may be included in the recommended playlist customization device; or it may exist independently without being assembled into the recommended playlist customization device.

[0102] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the recommended playlist customization device, the recommended playlist customization device performs recommended playlist customization.

[0103] The computer program code for performing the operation of the present embodiment can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0104] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present embodiment. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The modules involved in the description of this embodiment may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0106] The readable storage medium provided in this embodiment is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for customizing the recommended song list, and can solve the problem that the prior art cannot flexibly customize recommendations based on the car owner's listening preferences, and thus cannot accurately match the user's listening needs. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment are the same as the beneficial effects of the method for customizing the recommended song list provided in the above-mentioned embodiment, and will not be elaborated here.

[0107] The above descriptions are only some embodiments, and are not intended to limit the patent scope of this embodiment. All equivalent structural changes made using the contents of the specification and drawings of this application under the technical concept of this application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of this application.

Claims

1. A method for customizing a recommended playlist, characterized in that: The method comprises: Obtaining user identity information of the current user, and obtaining music playing records and a user portrait of the current user based on the user identity information; Obtaining a user preferred playlist of the current user based on the music playing record; The songs in the user's preferred playlist are filtered according to the user portrait to obtain a recommended playlist.

2. The method according to claim 1, characterized in that The step of obtaining a user preferred playlist based on the music playing record comprises: Acquire a user preference tag of the current user based on the music playing record; The preset music library is filtered according to the user preference tag to obtain the user preference song list of the current user.

3. The method according to claim 2, characterized in that The step of obtaining the user preference tag based on the music playing record includes: Determining the music type and the number of times the music is played based on the music playing record, and generating a user preference list based on the music type and the number of times the music is played; The user preference list content is spliced ​​into a preset prompt word template to obtain a user preference description text, and a user preference label is obtained according to the user preference description text using a preset large model.

4. The method according to claim 1, characterized in that After the step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist, the method further includes: Determine a play time period based on the music play record, and when the play time period is detected, ask the user whether to play the recommended playlist; If so, the songs in the recommended playlist are played.

5. The method according to claim 1, characterized in that The step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist includes: Determine user attributes and user payment behavior according to the user portrait, the user attributes include: user age and / or user gender, and the user payment behavior includes: at least one of user payment level, user payment frequency, and user payment times; The songs in the user's preferred playlist are filtered based on the user's attributes and the user's payment behavior to obtain a recommended playlist.

6. The method according to any one of claims 1 to 5, characterized in that The step of filtering the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist also includes: Filter the songs in the user's preferred playlist according to the user portrait to obtain an initial playlist; Performing similarity scoring between the music genres corresponding to the songs in the initial playlist and the preference tags; Generate a recommended playlist based on the similarity score results.

7. A device for customizing a recommended playlist, characterized in that: The device comprises: An information acquisition module, used to acquire the user identity information of the current user, and acquire the music playing record and user portrait of the current user based on the user identity information; A song list generation module, used to obtain a user preferred song list of the current user based on the music playing record; The playlist screening module is used to screen the songs in the user's preferred playlist according to the user portrait to obtain a recommended playlist.

8. A device for customizing a recommended playlist, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for customizing a recommended playlist as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the recommended playlist customization method as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, which, when executed by a processor, implements the steps of the recommended playlist customization method as described in any one of claims 1 to 6.

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