A book reading background music matching method, an electronic device and a storage medium

Abstract

The application relates to the field of electronic book reading, and provides a book reading background music matching method, an electronic device and a storage medium, which comprises the following steps: setting a background music library, the background music library comprising multiple pieces of music with different styles, each piece of music being provided with a characteristic value; when a reader triggers a book segment point, calculating the characteristic value of the reader; calculating the similarity between the characteristic value of the reader and the characteristic value of each piece of music, and playing the piece of music with the highest characteristic value similarity. The technical scheme provides configuration of user-defined music, optimizes user experience, and improves retention rate; through data modeling analysis and user portrait selection, accurate initial background music is provided for different groups of users, so that the users have better reading experience.

Description

Technical Field

[0001] This application relates to the field of e-book reading software technology, and in particular to a method for matching background music during book reading, an electronic device, and a storage medium. Background Technology

[0002] With the development of technology, e-book reading has gradually become popular. Unlike traditional publications that rely on paper, e-books use computer technology to digitally record text, images, sound, and video information onto devices that use light, electricity, or magnetism as media, and then use specific devices to read, copy, and transmit them. Some e-book reading software automatically plays background music while the reader is reading, thereby improving the reading experience, increasing reading time, and boosting app revenue.

[0003] However, existing background music typically plays fixed music or offers a few tracks for readers to choose from, failing to adequately consider reader preferences and lacking the capability for automated background music playback. Furthermore, patent solutions, such as those in CN106708894B, only pre-install music for specific article segments, ensuring every user receives the same music. Users are forced to passively accept the music, resulting in a lack of personalization and a poor user experience. Summary of the Invention

[0004] To help improve the problems of unreasonable background music playback and lack of personalization in existing reading software, this application provides a method for matching background music for reading, an electronic device, and a storage medium.

[0005] Firstly, this application provides a method for matching background music for book reading, employing the following technical solution:

[0006] A method for matching background music for book reading, the method comprising:

[0007] S10: Set a background music library, which includes multiple pieces of music in different styles, each with a characteristic value;

[0008] S20: When a reader triggers the tracking point of a book segment, calculate the feature value of that reader;

[0009] S30: Calculate the similarity between the reader and the feature values ​​of each piece of music, and play the music with the highest feature value similarity.

[0010] By adopting the above technical solutions, background music can be pushed in a personalized and automated manner based on the reader's data, thereby improving the reader's reading experience.

[0011] Secondly, this application provides an electronic device that adopts the following technical solution:

[0012] An electronic device comprising:

[0013] At least one processor;

[0014] Memory;

[0015] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute a book reading background music matching method.

[0016] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0017] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform any of the book reading background music matching methods provided in the first aspect.

[0018] In summary, the technical solution of this application accurately matches background music for each chapter based on user preferences and provides a customizable music configuration function. By analyzing the behavior of a massive number of users with different reading and consumption habits and combining this with user profiling, it provides precise background music recommendations for each chapter. The ability to configure custom music optimizes the user experience and improves retention rates; through data modeling analysis and user profiling, it provides precisely matched initial background music to different user groups, resulting in a better reading experience. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a method for matching background music for book reading provided in an embodiment of this application;

[0020] Figure 2 This is a flowchart illustrating a method for matching background music for book reading provided in another embodiment of the application;

[0021] Figure 3 This is a schematic diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0022] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figure 1-3 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0023] This application provides a method for matching background music for book reading, which can be used in e-book reading apps.

[0024] This embodiment uses an e-book reading app as an application example to specifically illustrate the above method. In actual implementation, the above method can also be used in other e-book reading systems, and this embodiment does not limit it.

[0025] Specific reference Figure 1 The method shown includes:

[0026] S10: Set up a background music library, which includes a variety of music in different styles, such as classical, rock, country, and opera, to suit readers of different ages, personalities, and interests; each piece of music has a feature value, which can be used to link to the corresponding music.

[0027] S20: When a reader triggers a tracking point for a book segment, such as when the reader turns to the first page of a chapter, the tracking point for that page is triggered; calculate the reader's feature value, which contains the reader's personalized tags, for matching music usage;

[0028] S30: Calculate the similarity between the reader and the feature values ​​of each piece of music, and play the music with the highest feature value similarity. The similarity can be a single value, a set of values, etc., and is not limited to the description in this embodiment.

[0029] In summary, the technical solution presented in this application accurately matches background music to chapters based on user preferences and provides the function of customizing music configurations. By analyzing the behavior of a massive number of users with different reading and consumption habits and combining this with user profiling, it provides precise recommendations for background music to each chapter. This optimizes the user experience and improves retention rates.

[0030] Based on this embodiment, the reader's characteristic values ​​are associated with the reader's reading and consumption habits. Of course, the characteristic values ​​can also be associated with other data about the reader, such as the user's age, gender, and preferred book categories, etc., which are not limited to the description in this embodiment.

[0031] In this embodiment, specifically, the formula for calculating the reader's feature value e is:

[0032] e = dtlc*a + mch*b;

[0033] Where dtlc is the activity index and mch is the consumption index; a and b are weighting coefficients used to adjust the weights of the activity index and the consumption index. Their values ​​can be adjusted appropriately according to business needs. For example, increasing the value of a can improve the impact of user activity on the feature value.

[0034] Furthermore, in eigenvalue calculation,

[0035] The specific formula for calculating the activity index dtlc is as follows:

[0036] dtlc=e lc / (d*t);

[0037] Where lc represents the total number of times the reader has logged into this APP, which can be the total number of logins within a certain period of time; d represents the number of days since the reader's last login time. For example, if the reader's last login time was September 1st and the current date is September 11th, then the value of d is 10.

[0038] t represents the user type. When the reader is a new user, t = g; when the reader is an existing user, t = f. Generally, f > g, meaning that new users have a lower activity index, while existing users have a higher activity index. The values ​​of f and g can be adjusted based on experience and business needs.

[0039] The specific formula for calculating the Consumer Price Index (MCH) is as follows:

[0040] mch = (m*j + c*k)*(p - ln(h));

[0041] Where m is the reader's cumulative consumption of the current book type, c is the number of times the reader has renewed the current book type, h is the reader's cumulative reading time for the current book type; j and k are both weighting coefficients, and p is an adjustment constant.

[0042] It should be noted that the current book type mentioned above refers to books with similar styles, such as both being fantasy novels or both being romance novels. The type can be determined using book classification tags, but other methods can also be used, and are not limited to the description in this embodiment.

[0043] See attached document Figure 2 As shown, in one embodiment of the present invention, when a reader triggers the tracking point of a book segment, before calculating the reader's feature value, a music list from the background music library is pushed for the reader to choose from; at this time, the reader can choose the music in the list or directly close the music list.

[0044] When a reader selects a piece of music from the list, the selected music is played directly as background music; at this time, the reader is added to the music's data, which is used to calculate the music's feature values.

[0045] When the reader does not select specific music, the system matches the reader with music feature values ​​and plays music that is suitable for the reader.

[0046] In this embodiment, the feature value of the music is further associated with the data of readers who selected the music. Specifically, the feature value of the music is the average of the feature values ​​of all readers who selected the music. Of course, the feature value of the music can be calculated in ways other than the average, such as assigning different weights to users at different times, performing a weighted average, etc., and is not limited to the method described in this embodiment.

[0047] In this embodiment, it should be noted that when the similarity between the reader and the feature values ​​of each piece of music is greater than a preset value, i.e., when there is no music that closely matches the reader, the default music is played as background music. This preset value can be set based on the experience and operational strategies of the operators. The default music can be music from the background music library with the most selected music by the reader, or popular music downloaded from the internet.

[0048] In another embodiment of the present invention, a method for calculating the matching of background music to a user profile is defined. In this embodiment, the book excerpt specifically refers to a chapter.

[0049] User type t: Divided into new users and returning users;

[0050] Login time d: The time of the most recent login (in days);

[0051] Login count (lc): Total number of logins;

[0052] Activity Index dtlc: Calculated based on new and returning users, the time of the most recent login, and the cumulative number of logins;

[0053] Cumulative spending m: Cumulative spending on books of the current type;

[0054] Number of renewals (c): Subscription behavior of users for similar books;

[0055] Reading time h: Cumulative reading time for similar books;

[0056] Consumer Index (mch): Calculated from cumulative spending, number of subscription renewals, and reading time.

[0057] Reading Chapter x: The chapter that triggers background music playback;

[0058] Preference music (y): Background music set by the user;

[0059] Recommendation index e: The recommendation index of the music corresponding to the current chapter;

[0060] The specific music matching method is as follows:

[0061] Step 1: Obtain all user profile data that has been entered into the database through the current chapter;

[0062] User profile data includes user type (determining whether a user is a new user based on the number of days since registration), login time, number of logins, book consumption (statistics on consumption information of similar books, determining whether books are similar based on book category and book level), book subscription behavior information (number of subscriptions to similar books), and statistics on the reading time of a user for a single book.

[0063] Step 2: Calculate the activity index;

[0064] In this embodiment, it is assumed that the more times a user logs in, the higher their activity index should be.

[0065] The activity index is also affected by the number of recent login days; the longer a user has not logged in, the lower their activity index needs to be.

[0066] Different weights are assigned to different user types for calculation. In this embodiment, the weight for new users is 0.3, and the weight for old users is 0.7.

[0067] The specific formula is as follows:

[0068] dtlc=e lc / (d*t)

[0069] Step 3: Calculate the consumer price index;

[0070] In this embodiment, users with more cumulative consumption and more renewals have a higher consumption index.

[0071] The index is affected by reading time. If the reading time is higher but the cumulative consumption and renewal are lower, the consumption index needs to be adjusted downward.

[0072] The specific calculation formula is as follows:

[0073] mch=(m*0.8+c*0.2)*(5-ln(h))

[0074] Step 4: Filter out the activity index and consumption index by background music, and then calculate the recommendation index corresponding to the music.

[0075] In this embodiment, the feature values ​​corresponding to music are calculated in summary. The activity index weight is set to 0.3, the consumption index weight is set to 0.7, and the total weight is 1.

[0076] The specific calculation formula is as follows:

[0077] e=Σ(dtlc i *0.3+mch i *0.7) / n

[0078] Step 5: Based on the current user behavior, calculate the feature value of a single user using the above calculation method, then find the most matching background music and automatically play the matching music through the APP.

[0079] This embodiment employs the aforementioned technical solution to accurately match chapter background music based on user preferences and provides a customizable music configuration function. By analyzing the behavior of a massive number of users with different reading and consumption habits, combined with user profiling, it provides precise recommendations for chapter background music. Personalized and automated background music recommendations based on reader data enhance the reader's reading experience.

[0080] This application also provides an electronic device, such as... Figure 3 As shown, Figure 3 The illustrated electronic device 700 includes a processor 701 and a memory 703. The processor 701 and the memory 703 are connected, for example, via a bus 702. Optionally, the electronic device 700 may also include a transceiver 704. It should be noted that in practical applications, the transceiver 704 is not limited to one type, and the structure of this electronic device 700 does not constitute a limitation on the embodiments of this application.

[0081] Processor 701 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 701 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0082] Bus 702 may include a pathway for transmitting information between the aforementioned components. Bus 702 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 702 can be divided into address bus, data bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0083] The memory 703 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto.

[0084] The memory 703 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 701. The processor 701 is used to execute the application code stored in the memory 703 to implement the content shown in the foregoing method embodiments.

[0085] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, PDAs (personal digital assistants), and PADs (tablet computers), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0086] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed in a computer, causes the computer to execute the book reading background music matching method provided in the above embodiments.

[0087] This technical solution accurately matches background music to chapters based on user preferences and provides a customizable music configuration function. By analyzing the behavior of a massive number of users with different reading and consumption habits and combining this with user profiling, it provides precisely targeted background music recommendations for each chapter. The ability to configure user-defined music optimizes the user experience and improves retention rates; through data modeling analysis and user profiling, it provides precisely matched initial background music to different user groups, resulting in a better reading experience.

[0088] It should be understood that although the steps in the flowcharts in the accompanying drawings are shown sequentially as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise expressly stated herein, there is no strict order in which these steps are performed, and they may be performed in other orders.

[0089] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for matching background music for book reading, characterized in that, The method includes: S10: Set a background music library, which includes multiple pieces of music in different styles, each with a characteristic value; S20: When a reader triggers the tracking point of a book segment, calculate the feature value of that reader; S30: Calculate the similarity between the reader and the feature values ​​of each piece of music, and play the music with the highest feature value similarity; The formula for calculating the reader's characteristic value e is as follows: e = dtlc * a + mch * b; Where a and b are weighting coefficients, dtlc is the activity index, and mch is the consumption index; In eigenvalue calculation, The active index dtlc includes: dtlc = e lc / (d * t); Where lc is the cumulative number of logins for the reader, d is the number of days since the reader's last login; t is the user type, where t=g when the reader is a new user, and t=f when the reader is an existing user, and f>g; The consumer index (mch) includes: mch = (m * j + c * k) * (p - ln(h)); Where m is the reader's cumulative consumption of the current book type, c is the number of times the reader has renewed the current book type, h is the reader's cumulative reading time for the current book type; j and k are both weighting coefficients, and p is an adjustment constant.

2. The method for matching background music for book reading according to claim 1, characterized in that, The reader's characteristics are related to their reading and consumption habits.

3. The method for matching background music for book reading according to claim 1, characterized in that, When a reader triggers a tracking point in a book excerpt, a playlist of background music from the library is pushed to the reader for selection. When a reader makes a selection, the selected music is played as background music.

4. The method for matching background music for book reading according to claim 3, characterized in that, The feature values ​​of the music are associated with the data of the readers who selected that music.

5. The method for matching background music for book reading according to claim 4, characterized in that, The characteristic value of music is the average of the characteristic values ​​of all readers who selected that music.

6. The method for matching background music for book reading according to claim 1, characterized in that, When the similarity between the reader and the feature values ​​of each piece of music is greater than the preset value, the default music is played as background music.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the book reading background music matching method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed in the computer, the computer is instructed to perform the book reading background music matching method according to any one of claims 1 to 6.

Images