Book recommendation method and device, electronic equipment and computer storage medium

Abstract

The invention provides a book recommendation method and device, electronic equipment and a computer storage medium, and belongs to the technical field of big data, and the method comprises the steps: determining the borrowing probability of a target reader for each book in each region according to the historical borrowing record of the target reader; the borrowing probability is corrected according to the popularity of each book in each region and the popularity of each book in each region to obtain a book recommendation vector of the target reader in each region, and in addition, the book recommendation vector of the target reader in each region is corrected again through the recommendation degree of the book management subsystem in each region to obtain a book recommendation vector of the target reader in each region. According to the method, the final target book recommendation vector of the target reader is obtained, the obtained target book recommendation vector of the target reader is more accurate, books can be accurately recommended to the target reader, and a good book recommendation effect is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data, and in particular, to a method, device, electronic device, and computer storage medium for recommending books. Background Art

[0002] Currently, when borrowing books, book recommendations often originate from users' historical borrowing records. Based on the books borrowed by users, books similar to those borrowed by users are recommended to them.

[0003] The books recommended by the above method have poor accuracy and it is difficult to achieve a good recommendation effect. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method, device, electronic device, and computer storage medium for recommending books, so as to alleviate the technical problem that the books recommended by the existing book recommendation method have poor accuracy and it is difficult to achieve a good recommendation effect.

[0005] In a first aspect, an embodiment of the present invention provides a method for recommending books, which is applied to a book management system. The book management system is connected to multiple regional book management subsystems. The method includes:

[0006] Obtain the book information uploaded by each regional book management subsystem and the recommendation degree of each regional book management subsystem. Among them, the book information includes: the number of views of each book in each region, the number of discussions of each book in each region, the number of borrowings of each book in each region, and the characteristic information of each book in each region;

[0007] Determine the popularity of each book in each region based on the number of views of each book in each region and the number of discussions of each book in each region;

[0008] Determine the popularity of each book in each region based on the number of borrowings of each book in each region and the number of views of each book in each region;

[0009] Obtain a recommendation request for recommending books to a target reader in a target region initiated by the target region book management subsystem. Among them, the recommendation request carries the reader information of the target reader, and the reader information includes: the personal information of the target reader and the historical borrowing record of the target reader;

[0010] Use a book recommendation model to recommend books based on the reader information of the target reader and the characteristic information of each book in each region, and obtain a borrowing probability matrix of the target reader for each book in each region;

[0011] Determine the book recommendation vector of the target reader in each area according to the borrowing probability matrix of each book in each area for the target reader, the popularity of each book in each area, and the popularity of each book in each area.

[0012] Based on the book recommendation vector of the target reader in each area and the recommendation degree of the book management subsystem in each area, determine the target book recommendation vector of the target reader, and then recommend books to the target reader according to the target book recommendation vector.

[0013] Furthermore, determine the popularity of each book in each area based on the number of views of each book in each area and the number of discussions of each book in each area, including:

[0014] According to the popularity calculation formula Calculate the popularity of each book in each area, where hot ij represents the popularity of book i in area j, scan_num ij represents the number of views of book i in area j, dis_num ij represents the number of discussions of book i in area j, danand_rate ij represents the demand degree of book i in area j.

[0015] Furthermore, determine the popularity of each book in each area based on the number of borrowings of each book in each area and the number of views of each book in each area, including:

[0016] According to the borrowing ratio calculation formula Calculate the borrowing rate of each book in each area, where borrow_scan_rate ij represents the borrowing rate of book i in area j, borrow_num ij represents the number of borrowings of book i in area j, scan_num ij represents the number of views of book i in area j;

[0017] According to the popularity calculation formula Calculate the popularity of each book in each area, where welcome_rate ij represents the popularity of book i in area j, borrow_scan_rate ij represents the borrowing rate of book i in area j, damand_rate ij represents the demand degree of book i in area j.

[0018] Further, the book recommendation vector of the target reader in each region is the rating vector of the target reader for each book in each region. The book recommendation vector of the target reader in each region is determined according to the borrowing probability matrix of each book in each region by the target reader, the popularity of each book in each region, and the popularity of each book in each region, including:

[0019] Calculate the rating of each book in each region by the target reader according to the rating calculation formula score ij = w ij * hot ij * welcome_rate ij to obtain the rating vector of each book in each region by the target reader. Among them, wcore ij represents the rating of book i in region j by the target reader, w ij represents the borrowing probability of book i in region j by the target reader, hot ij represents the popularity of book i in region j, and welcome_rate ij represents the popularity of book i in region j.

[0020] Further, based on the book recommendation vector of the target reader in each region and the recommendation degree of each regional book management subsystem, the target book recommendation vector of the target reader is determined, including:

[0021] Calculate the target book recommendation vector of the target reader according to the target book recommendation vector calculation formula to calculate the target book recommendation vector of the target reader. Among them, U represents the target book recommendation vector of the target reader, z represents the number of regional book management subsystems, and w k represents the recommendation degree of regional book management subsystem k, and S k represents the book recommendation vector of the target reader in the region corresponding to regional book management subsystem k.

[0022] Further, after recommending books to the target reader according to the target book recommendation vector, the method further includes:

[0023] Obtain the actual borrowing information of the target reader;

[0024] If the target reader does not borrow, the recommendation degree of each regional book management subsystem remains unchanged;

[0025] If the target reader borrows a book, calculate the updated recommendation degree of each regional book management subsystem according to the recommendation degree update formula where w' k represents the updated recommendation degree of regional book management subsystem k, and w kIndicates the recommendation degree of the regional library management subsystem k Indicates the recommendation degree of the target regional library management subsystem, where θ is a positive number less than 1 β indicates whether the target reader has borrowed the books recommended by the target regional library management subsystem. β = 0 indicates that the target reader has borrowed the books recommended by the target regional library management subsystem, and β = 1 indicates that the target reader has not borrowed the books recommended by the target regional library management subsystem. U represents the target book recommendation vector of the target reader, and a represents the vector of the books actually borrowed by the target reader

[0026] Furthermore, the book information is statistically obtained based on the user scanning the book code. The book code includes: a publishing institution identification code, a book category code, a publishing batch code, and a serial number. The book code is unique

[0027] In a second aspect, an embodiment of the present invention further provides a book recommendation device, which is applied to a library management system. The library management system is connected to multiple regional library management subsystems. The device includes:

[0028] A first acquisition unit, configured to acquire the book information uploaded by each regional library management subsystem and the recommendation degree of each regional library management subsystem. Wherein, the book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region

[0029] A first determination unit, configured to determine the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region

[0030] A second determination unit, configured to determine the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region

[0031] A second acquisition unit, configured to acquire a recommendation request for recommending books to a target reader in a target region initiated by the target regional library management subsystem. Wherein, the recommendation request carries the reader information of the target reader, and the reader information includes: the personal information of the target reader and the historical borrowing records of the target reader

[0032] A book recommendation unit, configured to use a book recommendation model to perform book recommendations on the reader information of the target reader and the characteristic information of each book in each region, and obtain a borrowing probability matrix of each book in each region for the target reader

[0033] A third determination unit, configured to determine a book recommendation vector of the target reader in each area according to a borrowing probability matrix of each book in each area for the target reader, the popularity of each book in each area, and the popularity of each book in each area.

[0034] A fourth determination unit, configured to determine a target book recommendation vector of the target reader based on the book recommendation vector of the target reader in each area and the recommendation degree of each area book management subsystem, and then recommend books for the target reader according to the target book recommendation vector.

[0035] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the method according to any one of the first aspects are implemented.

[0036] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, on which a computer program is stored. When the computer runs the computer program, the steps of the method according to any one of the first aspects are executed.

[0037] In an embodiment of the present invention, a method for recommending books is provided, which is applied to a book management system. The book management system is connected to multiple regional book management subsystems. The method includes: obtaining book information uploaded by each regional book management subsystem and the recommendation degrees of each regional book management subsystem. Among them, the book information includes: the number of views of each book in each region, the number of discussions of each book in each region, the number of borrowings of each book in each region, and the characteristic information of each book in each region; determining the popularity of each book in each region based on the number of views of each book in each region and the number of discussions of each book in each region; determining the popularity of each book in each region based on the number of borrowings of each book in each region and the number of views of each book in each region; obtaining a recommendation request for recommending books to a target reader in a target region initiated by the target regional book management subsystem. Among them, the recommendation request carries the reader information of the target reader, and the reader information includes: the personal information of the target reader and the historical borrowing records of the target reader; using a book recommendation model to recommend books based on the reader information of the target reader and the characteristic information of each book in each region, and obtaining a borrowing probability matrix of the target reader for each book in each region; determining a book recommendation vector of the target reader in each region according to the borrowing probability matrix of the target reader for each book in each region, the popularity of each book in each region, and the popularity of each book in each region; determining a target book recommendation vector of the target reader based on the book recommendation vector of the target reader in each region and the recommendation degrees of each regional book management subsystem, and then recommending books for the target reader according to the target book recommendation vector. Through the above description, it can be seen that in the method for recommending books of the present invention, a book recommendation model is used to recommend books based on the reader information of the target reader and the characteristic information of each book in each region, and a borrowing probability matrix of the target reader for each book in each region is obtained. In this process, the historical borrowing records of the target reader are considered, that is, first, the borrowing probability of the target reader for each book in each region is determined according to the historical borrowing records of the target reader, and then, the above borrowing probability is corrected according to the popularity of each book in each region and the popularity of each book in each region to obtain a book recommendation vector of the target reader in each region. In addition, the recommendation degrees of each regional book management subsystem are used to correct the book recommendation vector of the target reader in each region again to obtain the final target book recommendation vector of the target reader. Finally, books are recommended for the target reader according to the target book recommendation vector. The above process takes the popularity of each book in each region and the popularity of each book in each region as consideration indicators for book recommendation, which can improve the accuracy of book recommendation. In addition, the target book recommendation vector of the target reader determined by the joint recommendation of multiple regional book management subsystems will be more accurate, and can accurately recommend books for the target reader, achieving a good book recommendation effect, and alleviating the technical problem that the books recommended by the existing book recommendation methods have poor accuracy and it is difficult to achieve a good recommendation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the accompanying drawings required for the description of the specific embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a schematic flowchart of a method for recommending books provided by an embodiment of the present invention;

[0040] Figure 2 It is a schematic diagram of a device for recommending books provided by an embodiment of the present invention;

[0041] Figure 3 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. Specific embodiments

[0042] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0043] The books recommended by traditional book recommendation methods have poor accuracy and it is difficult to achieve good recommendation effects.

[0044] Based on this, in the book recommendation method of the present invention, a book recommendation model is used to recommend books based on the reader information of the target reader and the feature information of each book in each area, and a borrowing probability matrix of each book in each area for the target reader is obtained. In this process, the historical borrowing records of the target reader are considered, that is, first, the borrowing probabilities of each book in each area for the target reader are determined according to the historical borrowing records of the target reader, and then, the above borrowing probabilities are corrected according to the popularity of each book in each area and the popularity of each book in each area to obtain the book recommendation vectors of the target reader in each area. In addition, the recommendation degree of each area book management subsystem is also used to correct the book recommendation vectors of the target reader in each area again to obtain the final target book recommendation vector of the target reader. Finally, books are recommended for the target reader according to the target book recommendation vector. The above process takes the popularity of each book in each area and the popularity of each book in each area as the consideration indicators for book recommendation, which can improve the accuracy of book recommendation. In addition, the target book recommendation vector of the target reader determined by the joint recommendation of multiple area book management subsystems will be more accurate, and can accurately recommend books for the target reader to achieve good book recommendation effects.

[0045] To facilitate the understanding of this embodiment, a book recommendation method disclosed in the embodiments of the present invention will be introduced in detail first.

[0046] Embodiment 1:

[0047] To facilitate the understanding of this embodiment, a book recommendation method disclosed in the embodiments of the present invention will be introduced in detail first. Refer to Figure 1 the flowchart of a book recommendation method shown in the figure, which mainly includes the following steps:

[0048] Step S102, obtain the book information uploaded by each regional book management subsystem and the recommendation degree of each regional book management subsystem. Among them, the book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region;

[0049] In the embodiments of the present invention, the execution subject of this method can be a book management system, and the book management system is connected to multiple regional book management subsystems. The above-mentioned regional book management subsystems will upload the latest book information irregularly. For example, when the regional book management subsystem statistics that the book information has changed, such as the browsing times of each book in the current region have changed, at this time, the regional book management subsystem of the current region can upload the latest book information; for another example, the discussion times of each book in the current region have changed, at this time, the regional book management subsystem of the current region can upload the latest book information; for another example, the borrowing times of each book in the current region have changed, at this time, the regional book management subsystem of the current region can upload the latest book information; of course, it can also be to upload the latest book information at intervals of a preset time period. The embodiments of the present invention do not specifically limit the above-mentioned upload timing.

[0050] The above-mentioned book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region. The browsing times of each book in each region are obtained by the regional book management subsystem in the corresponding region by counting the browsing times of each book in the corresponding region. When implemented, before a reader borrows a book, the reader needs to select a book first. When selecting a book, a simple browse of each book is required. When browsing, the reader needs to scan the book code on the book first. In this way, the regional book management subsystem can count the browsing times of each book. The discussion times of the above-mentioned books are also that the reader scans the book code on the book in the corresponding reading code-scanning software, and then operates the reader interaction platform / community module. After entering the reader interaction page, in this way, the regional book management subsystem can count the discussion information of the books uploaded by the reader interaction to obtain the discussion times of the book. The borrowing times of the above-mentioned books are obtained by counting the borrowing records of each book. The characteristic information of the above-mentioned books includes: information such as the category of the book, the author of the book, the abstract of the book, and the book title.

[0051] The initial value of the recommendation degree of each of the above-mentioned regional book management subsystems is 1, and the recommendation degree of each of the above-mentioned regional book management subsystems will be updated according to the actual borrowing information of the target reader.

[0052] In addition, it should be noted that the view count, discussion count, and borrowing count of each of the above-mentioned books are all time-related and can be flexibly set according to specific needs. For example, it can be set to count the view count, discussion count, and borrowing count of each book within one week before the current time. It can also be set to count the view count, discussion count, and borrowing count of each book within 10 days before the current time. When the time span is long, the basic data statistics can be weighted and summed according to needs. For example, among the view count, discussion count, and borrowing count of each book within 10 days before the current time, the weight of the view count, discussion count, and borrowing count of each book within the first day before the current time is a1, the weight of the view count, discussion count, and borrowing count of each book within the second day before the current time is a2, the weight of the view count, discussion count, and borrowing count of each book within the third day before the current time is a3, and so on. The sum of all weights is 1. Then, the view count of each book is weighted and summed according to the weights, the discussion count of each book is weighted and summed according to the weights, and so on, to obtain the final view count, discussion count, and borrowing count of each book in each region. In this way, relevant data in the near future can be obtained for subsequent calculation of popularity and welcome degree, which is more accurate and scientific.

[0053] Step S104: Determine the popularity of each book in each region based on the view count and discussion count of each book in each region.

[0054] Specifically, the inventor considered that the higher the popularity of a book, the greater the probability that readers will borrow it. Therefore, in order to accurately recommend books to target readers, the popularity of each book in each region is also used as an indicator for book recommendation to assist subsequent book recommendation and improve the accuracy of book recommendation.

[0055] The process of determining the popularity of each book in each region will be introduced in detail later, and will not be elaborated here.

[0056] Step S106: Determine the popularity of each book in each region based on the borrowing count and view count of each book in each region.

[0057] Specifically, the inventor considered that the higher the popularity of a book, the greater the probability that readers will borrow it. Therefore, in order to accurately recommend books to target readers, the popularity of each book in each region is also used as an indicator for book recommendation to assist subsequent book recommendation and improve the accuracy of book recommendation.

[0058] The process of determining the popularity of each book in each area will be introduced in detail below and will not be elaborated here.

[0059] Step S108: Obtain a recommendation request for recommending books to target readers in the target area initiated by the book management subsystem in the target area. Among them, the recommendation request carries the reader information of the target readers, and the reader information includes: the personal information of the target readers, the historical borrowing records of the target readers.

[0060] Specifically, the above recommendation request can be initiated by the target readers through the book recommendation module of the reading code scanning software. The above reading code scanning software is an application program in the book management subsystem in the target area. The personal information of the above target readers can specifically include: the age, gender, education level, book hobbies, etc. of the target readers. The historical borrowing records of the target readers can specifically include: the books historically borrowed by the target readers, the borrowing time and other specific information.

[0061] Step S110: Use the book recommendation model to perform book recommendations on the reader information of the target readers and the feature information of each book in each area, and obtain the borrowing probability matrix of the target readers for each book in each area.

[0062] Specifically, the above book recommendation model is a model pre-trained for predicting the borrowing probability of books.

[0063] The training process can be: obtain the borrowing records of readers borrowing books, the reader information of readers, and the feature information of books, input the reader information of readers and the feature information of books into the original book recommendation model, output the borrowing probability of readers borrowing each book, use the borrowing records of readers borrowing books as the true value, calculate the loss with the predicted value of the model, and adjust and train the parameters of the model through the loss.

[0064] Step S112: Determine the book recommendation vector of the target readers in each area according to the borrowing probability matrix of the target readers for each book in each area, the popularity of each book in each area, and the popularity of each book in each area.

[0065] Specifically, the book recommendation vector of the target readers in each area is the recommended value (i.e., the scoring value) of all the books included in each area. The above process is specifically to correct the borrowing probability matrix of the target readers for each book in each area through the popularity of each book in each area and the popularity of each book in each area, and obtain the book recommendation vector of the target readers in each area.

[0066] Step S114: Determine the target book recommendation vector of the target readers based on the book recommendation vector of the target readers in each area and the recommendation degree of the book management subsystem in each area, and then recommend books to the target readers according to the target book recommendation vector.

[0067] Specifically, the target book recommendation vector contains the recommendation values of all books (each book corresponds to a recommendation value), and books are recommended to the target readers according to the magnitude of the recommendation values (the larger the recommendation value, the higher the priority of recommending the corresponding book).

[0068] In an embodiment of the present invention, a method for recommending books is provided, which is applied to a book management system. The book management system is connected to multiple regional book management subsystems. The method includes: obtaining the book information uploaded by each regional book management subsystem and the recommendation degree of each regional book management subsystem. Among them, the book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region; determining the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region; determining the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region; obtaining a recommendation request for recommending books to the target readers in the target region initiated by the target region book management subsystem. Among them, the recommendation request carries the reader information of the target readers, and the reader information includes: the personal information of the target readers and the historical borrowing records of the target readers; using a book recommendation model to recommend books based on the reader information of the target readers and the characteristic information of each book in each region, and obtaining a borrowing probability matrix of the target readers for each book in each region; determining the book recommendation vector of the target readers in each region according to the borrowing probability matrix of the target readers for each book in each region, the popularity of each book in each region, and the popularity of each book in each region; determining the target book recommendation vector of the target readers based on the book recommendation vector of the target readers in each region and the recommendation degree of each regional book management subsystem, and then recommending books for the target readers according to the target book recommendation vector. Through the above description, it can be seen that in the method for recommending books of the present invention, a book recommendation model is used to recommend books based on the reader information of the target readers and the characteristic information of each book in each region, and a borrowing probability matrix of the target readers for each book in each region is obtained. In this process, the historical borrowing records of the target readers are considered, that is, first, the borrowing probability of the target readers for each book in each region is determined according to the historical borrowing records of the target readers, and then, the above borrowing probability is corrected according to the popularity of each book in each region and the popularity of each book in each region to obtain the book recommendation vector of the target readers in each region. In addition, the recommendation degree of each regional book management subsystem is used to correct the book recommendation vector of the target readers in each region again to obtain the final target book recommendation vector of the target readers. Finally, books are recommended for the target readers according to the target book recommendation vector. The above process uses the popularity of each book in each region and the popularity of each book in each region as the consideration indicators for book recommendation, which can improve the accuracy of book recommendation. In addition, the target book recommendation vector determined by the joint recommendation of multiple regional book management subsystems will be more accurate, and can accurately recommend books for the target readers, achieving a good book recommendation effect, and alleviating the technical problem that the books recommended by the existing book recommendation methods have poor accuracy and it is difficult to achieve a good recommendation effect.

[0069] The above content briefly introduced the book recommendation method of the present invention. Next, the specific content involved will be described in detail.

[0070] In an alternative embodiment of the present invention, the popularity of each book in each region is determined based on the number of views of each book in each region and the number of discussions of each book in each region. The specific steps are as follows:

[0071] According to the popularity calculation formula Calculate the popularity of each book in each region, where hot ij represents the popularity of book i in region j, scan_num ij represents the number of views of book i in region j, dis_num ij represents the number of discussions of book i in region j, damand_rate ij represents the necessity degree of book i in region j.

[0072] Specifically, the necessity degree of a book is preset, and its value range is from 0 to 1. The necessity degree of a book is used to characterize the characteristics of the book. The more the number of views of a book in a certain region, the greater the popularity of the book in that region. However, when calculating the popularity, the noise of the necessity degree of the book needs to be excluded. Therefore, it is necessary to divide by the necessity degree of the book.

[0073] In an alternative embodiment of the present invention, the popularity of each book in each region is determined based on the number of borrows of each book in each region and the number of views of each book in each region. The specific steps are as follows:

[0074] (1) According to the borrowing ratio calculation formula Calculate the borrowing rate of each book in each region, where borrow_scan_rate ij represents the borrowing rate of book i in region j, borrow_num ij represents the number of borrows of book i in region j, scan_num ij represents the number of views of book i in region j;

[0075] (2) According to the popularity calculation formula Calculate the popularity of each book in each region, where welcome_rate ij represents the popularity of book i in region j, borrow_scan_rate ij represents the borrowing rate of book i in region j, damand_rate ij represents the necessity degree of book i in region j.

[0076] Specifically, a high borrowing rate of a certain book does not necessarily mean that the book is popular. Some books are essential books, and their purchase rates should be high, rather than being liked by readers. Therefore, when calculating the popularity of books in each area, it is necessary to divide the borrowing rate of books in each area by the essential degree of books in that area.

[0077] In an alternative embodiment of the present invention, the book recommendation vector of the target reader in each area is the scoring vector of the target reader for each book in each area. The book recommendation vector of the target reader in each area is determined according to the borrowing probability matrix of each book of the target reader in each area, the popularity of each book in each area, and the popularity of each book in each area. The specific steps are as follows:

[0078] According to the scoring calculation formula score ij =w ij *hot ij *welcome_rate ij Calculate the score of the target reader for each book in each area, and then obtain the scoring vector of the target reader for each book in each area. Among them, score ij represents the score of the target reader for book i in area j, w ij represents the borrowing probability of the target reader for book i in area j, hot ij represents the popularity of book i in area j, and welcome_rate ij represents the popularity of book i in area j.

[0079] In an alternative embodiment of the present invention, based on the book recommendation vector of the target reader in each area and the recommendation degree of each area book management subsystem, the target book recommendation vector of the target reader is determined. The specific steps are as follows:

[0080] According to the target book recommendation vector calculation formula Calculate the target book recommendation vector of the target reader. Among them, U represents the target book recommendation vector of the target reader, z represents the number of area book management subsystems, and w k represents the recommendation degree of area book management subsystem k, and S k represents the book recommendation vector of the target reader in the area corresponding to area book management subsystem k.

[0081] In an alternative embodiment of the present invention, after recommending books to the target reader according to the target book recommendation vector, the method further includes the following steps:

[0082] (1) Obtain the actual borrowing information of the target reader;

[0083] (2) If the target reader does not borrow, the recommendation degree of each area book management subsystem remains unchanged;

[0084] (3) If the target reader borrows a book, then update the recommendation degree according to the update formula Calculate the updated recommendation degree of each regional library management subsystem, where w' k represents the updated recommendation degree of regional library management subsystem k, and w k represents the recommendation degree of regional library management subsystem k. represents the recommendation degree of the target regional library management subsystem. θ is a positive number less than 1. β represents whether the target reader has borrowed the book recommended by the target regional library management subsystem. β = 0 means that the target reader has borrowed the book recommended by the target regional library management subsystem, and β = 1 means that the target reader has not borrowed the book recommended by the target regional library management subsystem. U represents the target book recommendation vector of the target reader, and a represents the vector of the book actually borrowed by the target reader.

[0085] In an alternative embodiment of the present invention, the book information is obtained by counting after the user scans the book code. The book code includes: a publishing institution identification code, a book category code, a publishing batch code, and a serial number. The book code is unique.

[0086] Specifically, the above user can specifically be a reader.

[0087] Based on the strategy of one book one code, the book management system of the present invention can also implement various functions. The following will separately introduce the other functions it implements:

[0088] The book code of the present invention uses a globally unique identifier (GUID) or an algorithm with sufficient randomness and uniqueness to generate the book code. The book code should include information segments such as a publishing institution identification code, a book category code, a publishing batch code, and a serial number, ensuring that the book code of each book is unique globally and has identifiability and traceability.

[0089] For example, a typical coding structure can be: [Publishing institution ID (4 digits)] - [Book category ID (3 digits)] - [Publication year (4 digits)] - [Batch number (2 digits)] - [Serial number (6 digits)]. Such a book code can not only distinguish different publishing institutions and book categories but also reflect important information such as publication time and batch order.

[0090] In actual application, a dedicated coding database is constructed to store all generated book codes and their corresponding detailed book information, such as book title, author, ISBN, edition number, price, publication date, printing quantity, etc. The database should have efficient data storage, query, and update capabilities, and support multi-threaded concurrent access to meet the requirements of large-scale book code management. The above-mentioned coding database can be a distributed database, specifically constructed by a ClickHouse cluster. When a user initiates an operation request for relevant data, the operation request is parsed according to the database model to obtain an SQL statement for operating on the ClickHouse cluster, and the SQL statement is sent to the ClickHouse cluster so that the ClickHouse cluster executes the corresponding database operation according to the SQL statement. Furthermore, the user (specifically the system) can obtain the database operation result returned by the ClickHouse cluster.

[0091] At the same time, the database should adopt a secure and reliable encryption technology to encrypt and store the book codes and book information, prevent data leakage and illegal tampering, and ensure the integrity and confidentiality of the data.

[0092] Develop a coding generation software or module and integrate it with the book management system of the publishing enterprise. Before the book layout is completed and ready for printing, a batch of book codes are automatically generated according to the predetermined coding rules, and these book codes and their corresponding book information are entered into the coding database.

[0093] Then, in the printing process, through the docking with the printing equipment, the book codes are printed on specific positions of the book in the form of two-dimensional codes, barcodes, or invisible codes, such as the back cover, copyright page, or specific corners of the inner pages, etc. At the same time, ensure the clarity, accuracy, and readability of the printed book codes, and avoid problems such as blurring, errors, or duplicate printing.

[0094] Develop a reading code-scanning software (APP) applicable to mobile devices such as smartphones and tablets, supporting mainstream operating systems such as iOS and Android. This APP should have a powerful code-scanning and recognition function, capable of quickly and accurately recognizing the two-dimensional code or barcode on the book and parsing the encoded information therein (i.e., the book code).

[0095] The code-scanning application interface should be designed to be simple and user-friendly for easy operation by users. After successful code scanning, it can immediately display the basic information of the book, such as book title, author, publisher, publication date, etc., and provide relevant operation buttons, such as viewing detailed information, participating in interactive activities, purchasing related books or peripheral products, etc.

[0096] Data Collection and Transmission: When readers use the book scanning code software to scan the book code, in addition to parsing the book code, some other relevant data should also be collected, such as the scanning time, scanning location (obtained through the GPS positioning function of the mobile device), user device information (device model, operating system version, etc.). These data will be packaged together with the book code and transmitted to the coding database for storage through a secure network communication protocol, and then analyzed through the system.

[0097] To ensure the stability and reliability of data transmission, the network situation can be detected. When implementing, obtain the current network parameters, use the network condition prediction model to predict the network condition for the current network parameters, and obtain the network condition that will be encountered at a future preset time and the target communication mode corresponding to the network condition. The target communication mode includes any one of the following: the communication mode of the MQTT protocol, the communication mode of the SMS platform; when the future preset time arrives, communicate according to the target communication mode.

[0098] Technologies such as data caching and resume breakpoint transmission can also be adopted to prevent data loss caused by network fluctuations or interruptions. At the same time, encrypt the transmitted data to prevent the data from being stolen or tampered with during transmission.

[0099] Data Storage and Analysis System: Establish a high-performance and scalable data storage platform for storing a large number of book codes and related information transmitted from the code scanning recognition and data collection system. The data storage platform can adopt a ClickHouse cluster for efficient query and analysis; store some unstructured data such as pictures, audio, and video (such as promotional materials related to books, author audio explanations, etc.) in a distributed file system.

[0100] At the same time, the data storage platform should have data backup and recovery functions, regularly back up the data, and store the backup data in an off-site data center to prevent data loss caused by local data center failures.

[0101] Data Analysis and Mining: Use big data analysis techniques and data mining algorithms to deeply analyze the book codes and related information stored in the data storage platform. For example, the books of this application can be recommended, the scanning frequencies and purchase intentions of various books by different regions, different time periods, and different reader groups can be analyzed to understand the market demand and popular trends of books; by analyzing the scanning behaviors and interaction data of readers, the interests and reading habits of readers can be mined to provide accurate reader portraits and personalized marketing suggestions for publishing institutions.

[0102] The results of data analysis can be presented to the management and marketing staff of the publishing house in the form of visual reports, charts, etc., so that they can intuitively understand the market dynamics and reader feedback of the books, timely adjust the publishing strategies and marketing plans, and improve the sales performance and market competitiveness of the books.

[0103] Marketing activity planning and execution: Based on the one-book-one-code system, the publishing house can plan various forms of marketing activities, such as scanning code to participate in lucky draws, points redemption, book recommendation rewards, etc. By setting corresponding activity entrances and rules in the code-scanning application, it can attract readers to participate in the activities and increase the popularity and sales volume of the books.

[0104] For example, when a new book is launched, a code-scanning lucky draw activity can be carried out. Readers can participate in the lucky draw by scanning the book code and have the opportunity to obtain book coupons, physical prizes or the opportunity to interact with the book author, etc. At the same time, the publishing house can set different prize levels and winning probabilities according to the goals and budgets of the activities to ensure the attractiveness and feasibility of the activities.

[0105] Reader interaction and community building - Use the code-scanning application to build a reader interaction platform and community, encourage readers to publish book reviews, experiences, insights, etc. after reading the books, and communicate and interact with other readers. The publishing house can arrange special personnel to manage and reply to the readers' interaction content, enhance the communication and connection with the readers, and create a good reading atmosphere and community culture.

[0106] In addition, the publishing house can also collect readers' feedback opinions and suggestions through the reader interaction platform, understand readers' satisfaction and improvement needs regarding book content, layout, printing, etc., and provide a reference basis for the reprinting and optimization of the books.

[0107] Security and privacy protection system:

[0108] I. Data security protection measures

[0109] Adopt a variety of security protection technologies to ensure the data security in the one-book-one-code related systems. For example, at the network level, deploy devices such as firewalls, intrusion detection systems (IDS) and intrusion prevention systems (IPS) to prevent external network attacks and illegal access; at the server side of the system, adopt technologies such as operating system security hardening, database access control, and encrypted data storage to ensure the security of the server and the confidentiality of the data.

[0110] At the same time, conduct security vulnerability scanning and repair on the code-scanning application to prevent the application program from being hacked or maliciously tampered with, and ensure the information security of users when using the code-scanning application.

[0111] II. Privacy protection policy formulation and implementation

[0112] Formulate a clear privacy protection policy to inform readers about the types of data collected, purposes of use, storage methods, as well as data sharing and protection measures during the use of the one-book-one-code related system. Ensure that readers' personal information and privacy are fully respected and protected, and without the consent of readers, their personal information shall not be used for other commercial purposes or shared with third parties.

[0113] Establish a privacy complaint handling mechanism. When readers have doubts or dissatisfaction regarding the handling of their personal information, be able to promptly accept and properly handle readers' complaints and suggestions to safeguard the legitimate rights and interests of readers.

[0114] Through the above one-book-one-code technical solution, the full life cycle management of books can be realized, including digitization and intelligence in various links such as code generation, printing and distribution, scanning and identification, data collection, analysis and mining, marketing interaction, and security and privacy protection, providing strong technical support and innovation impetus for the development of the book industry.

[0115] Based on one-book-one-code, the following functions can also be realized:

[0116] 1. Precise inventory management

[0117] Real-time inventory tracking: The one-book-one-code technology enables each book to have a unique identity. By scanning the code to record the incoming and outgoing situations of books, libraries or bookstores can keep track of the inventory quantity in real time. Whether it is the incoming of new books, borrowing, returning of books, or transfer and inventory of books, the system can immediately update the inventory information. For example, when allocating books between branches of a large library, as long as the book code is scanned during the handover process, the inventory management system of the main library can immediately know the flow of the books and the actual inventory of each branch, avoiding errors and delays that may occur in manual inventory.

[0118] Precise inventory location: In addition to quantity management, one-book-one-code can also help staff quickly locate the specific location of books. In large libraries or warehouses with complex shelf layouts, by scanning the book code, the system can display the shelf area, floor, and specific location number where the book is located. This greatly improves the efficiency of book searching and sorting, reduces the workload of staff, and also facilitates readers to quickly find the books they need.

[0119] 2. Effectively combat piracy and illegal circulation

[0120] Convenient authenticity identification: Consumers or library managers can quickly verify the authenticity of books by scanning the unique code on the book. Since the codes of genuine books are uniformly managed and distributed by publishers or copyright owners, it is very difficult for pirated books to replicate the same code information. For example, in the book sales channel, when bookstore staff scan the book code during stock intake, the system can automatically determine whether the book is genuine, and promptly detect and prevent pirated books from entering the sales link. When implemented, if the book information obtained by scanning the code does not match the actual book information, it is determined that the current book is fake.

[0121] Traceability of the circulation path: Each book has a unique code that can record the complete circulation path of the book from the publisher to all levels of sales channels and then to the hands of the final consumers. If illegal sales or infringement acts are discovered, by tracing the circulation records of the book, the problem link can be quickly located and the responsible party can be determined. This is of great significance for maintaining the normal order of the book market and protecting the legitimate rights and interests of copyright owners.

[0122] 3. Enhance reader interaction and service experience

[0123] Personalized reading service: By scanning the code, readers can enter the exclusive online platform of the book. The platform provides personalized reading recommendations, author interview videos, book review sharing and other services for readers according to the readers' code scanning history and reading preferences, in accordance with the method of the present invention. For example, if a reader often scans the codes of science fiction novels, the system will recommend more popular science fiction works, related science fiction movies or science fiction-themed event information to him, enhancing the reader's reading experience and participation.

[0124] Construction of an interactive community: Each book has a unique code that builds a bridge for communication among readers. After scanning the code, readers can enter the interactive community of the book to discuss the content of the book with other readers, share reading insights, and carry out book club activities, etc. This kind of interaction not only enhances readers' understanding and love for the book, but also cultivates readers' reading habits and sense of community belonging. At the same time, authors and publishers can also collect readers' feedback through the interactive community to improve the book content and publishing strategies.

[0125] Optimize marketing and sales strategies

[0126] Precise marketing data collection: Publishers and bookstores can use the unique code of each book to collect marketing data of the book, including information such as the geographical location where readers scan the code, the scanning time, and the purchase intention (after readers scan the code, or the purchase intention given by readers on the software). By analyzing these data, understand the attention degree of readers to books in different regions and different time periods, so as to formulate precise marketing activities. For example, if it is found that the scanning rate of a certain new book is particularly high in a certain city, but the purchase conversion rate is low, targeted promotional activities such as discounts and giveaways can be carried out in that city.

[0127] 4. Increase the sales volume of books

[0128] Optimization of sales channel management: For the sales channels of books, one book one code can help publishers better manage and motivate distributors at all levels. By monitoring the sales of books in different channels, publishers can adjust channel strategies. For example, provide more support and rewards to distributors with good sales performance, and optimize or eliminate channels with poor sales. At the same time, it can also prevent the cross-selling behavior among distributors and maintain market price stability. When implemented, associate the book code of the book with the book code, and the whole process of receiving, shipping, and distribution records of the book can be tracked. Market inspection personnel can view the flow of the book by scanning the code. The system can also automatically record the location information of readers scanning the code and compare it with the distribution scope of the distributor (for example, if the location information of the reader scanning the code is Hebei and the recorded distribution scope of the distributor of this book is Beijing, it is determined that the flow of the book is abnormal). Once the abnormal flow of the book is found, a suspected cross-selling report can be generated in a timely manner, so as to effectively control cross-selling behavior and maintain the normal order of the market.

[0129] 5. Quality traceability

[0130] When there are quality problems with books, such as printing errors, binding problems, etc., due to the lack of effective traceability means, it is difficult to quickly determine the source and scope of the problem, resulting in low efficiency in handling problems, increasing costs and risks.

[0131] Solution of one book one code: The code assignment tracking and control function of one book one code can comprehensively master the production process and circulation records of books. When there is a quality problem, scanning the code to inform the book with quality problems can quickly trace the source and scope of the problem, and determine information such as the batch of the problem product, the processing machine and workshop, the warehousing and shipping records, and the circulation scope, so as to achieve rapid response and handling, and improve the efficiency and accuracy of quality control.

[0132] It should be noted that for different personnel, the systems they use have different permissions. Publishers' management personnel have the highest permissions, and readers only have the permission to provide basic data. The functional modules of the systems / software used by different personnel are also different.

[0133] Embodiment 2:

[0134] The embodiment of the present invention also provides a book recommendation device, which is mainly used to execute the book recommendation method provided in the above content of the embodiment of the present invention. The following is a specific introduction to the book recommendation device provided in the embodiment of the present invention.

[0135] Figure 2 It is a schematic diagram of a book recommendation device according to an embodiment of the present invention, as Figure 2As shown in the figure, the book recommendation device mainly includes: a first acquisition unit 10, a first determination unit 20, a second determination unit 30, a second acquisition unit 40, a book recommendation unit 50, a third determination unit 60, and a fourth determination unit 70, where:

[0136] The first acquisition unit is configured to acquire the book information uploaded by each regional book management subsystem and the recommendation degree of each regional book management subsystem. The book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region.

[0137] The first determination unit is configured to determine the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region.

[0138] The second determination unit is configured to determine the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region.

[0139] The second acquisition unit is configured to acquire a recommendation request for recommending books to the target readers in the target region initiated by the target region book management subsystem. The recommendation request carries the reader information of the target readers, and the reader information includes: the personal information of the target readers and the historical borrowing records of the target readers.

[0140] The book recommendation unit is configured to use a book recommendation model to recommend books based on the reader information of the target readers and the characteristic information of each book in each region, and obtain a borrowing probability matrix of the target readers for each book in each region.

[0141] The third determination unit is configured to determine the book recommendation vector of the target readers in each region according to the borrowing probability matrix of the target readers for each book in each region, the popularity of each book in each region, and the popularity of each book in each region.

[0142] The fourth determination unit is configured to determine the target book recommendation vector of the target readers based on the book recommendation vector of the target readers in each region and the recommendation degree of each regional book management subsystem, and then recommend books for the target readers according to the target book recommendation vector.

[0143] In an embodiment of the present invention, a book recommendation device is provided, which is applied to a book management system. The book management system is connected to multiple regional book management subsystems. The device includes: obtaining book information uploaded by each regional book management subsystem and the recommendation degree of each regional book management subsystem. Among them, the book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region; determining the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region; determining the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region; obtaining a recommendation request for recommending books to a target reader in a target region initiated by the target region book management subsystem. Among them, the recommendation request carries the reader information of the target reader, and the reader information includes: the personal information of the target reader and the historical borrowing records of the target reader; using a book recommendation model to perform book recommendation on the reader information of the target reader and the characteristic information of each book in each region to obtain a borrowing probability matrix of the target reader for each book in each region; determining a book recommendation vector of the target reader in each region according to the borrowing probability matrix of the target reader for each book in each region, the popularity of each book in each region, and the popularity of each book in each region; determining a target book recommendation vector of the target reader based on the book recommendation vector of the target reader in each region and the recommendation degree of each regional book management subsystem, and then recommending books to the target reader according to the target book recommendation vector. Through the above description, it can be seen that in the book recommendation device of the present invention, a book recommendation model is used to perform book recommendation on the reader information of the target reader and the characteristic information of each book in each region to obtain a borrowing probability matrix of the target reader for each book in each region. In this process, the historical borrowing records of the target reader are considered, that is, first, the borrowing probability of the target reader for each book in each region is determined according to the historical borrowing records of the target reader, and then, the above borrowing probability is corrected according to the popularity of each book in each region and the popularity of each book in each region to obtain a book recommendation vector of the target reader in each region. In addition, the recommendation degree of each regional book management subsystem is used to correct the book recommendation vector of the target reader in each region again to obtain the final target book recommendation vector of the target reader. Finally, books are recommended to the target reader according to the target book recommendation vector. The above process takes the popularity of each book in each region and the popularity of each book in each region as consideration indicators for book recommendation, which can improve the accuracy of book recommendation. In addition, the target book recommendation vector of the target reader determined by the joint recommendation of multiple regional book management subsystems will be more accurate, and can accurately recommend books to the target reader, achieving a good book recommendation effect, and alleviating the technical problem that the books recommended by the existing book recommendation methods have poor accuracy and it is difficult to achieve a good recommendation effect.

[0144] Optionally, the first determining unit is further configured to: according to the popularity calculation formula calculate the popularity of each book in each area, where hot ij represents the popularity of book i in area j, and scan_num ij represents the number of views of book i in area j, and dis_num ij represents the number of discussions of book i in area j, and damand_rate ij represents the necessity degree of book i in area j.

[0145] Optionally, the second determining unit is further configured to: according to the borrowing ratio calculation formula calculate the borrowing rate of each book in each area, where borrow_scan_rate ij represents the borrowing rate of book i in area j, and borrow_num ij represents the number of borrowings of book i in area j, and scan_num ij represents the number of views of book i in area j; according to the popularity calculation formula calculate the popularity of each book in each area, where welcome_rate ij represents the popularity of book i in area j, and borrow_scan_rate ij represents the borrowing rate of book i in area j, and damand_rate ij represents the necessity degree of book i in area j.

[0146] Optionally, the book recommendation vector of the target reader in each area is the scoring vector of the target reader for each book in each area. The third determining unit is further configured to: according to the scoring calculation formula score ij = w ij * hot ij * welcome_rate ij calculate the score of the target reader for each book in each area, and then obtain the scoring vector of the target reader for each book in each area, where score ij represents the score of the target reader for book i in area j, and w ij represents the borrowing probability of the target reader for book i in area j, and hot ij represents the popularity of book i in area j, and welcome_rate ij represents the popularity of book i in area j.

[0147] Optionally, the fourth determining unit is further configured to: according to the target book recommendation vector calculation formula Calculate the target book recommendation vector of the target reader, where U represents the target book recommendation vector of the target reader, z represents the number of regional book management subsystems, and w k represents the recommendation degree of the regional book management subsystem k, and S k represents the book recommendation vector of the target reader in the area corresponding to the regional book management subsystem k.

[0148] Optionally, the device is further configured to: obtain the actual borrowing information of the target reader; if the target reader has not borrowed, the recommendation degrees of each regional book management subsystem remain unchanged; if the target reader has borrowed a book, then update the recommendation degree according to the recommendation degree update formula Calculate the updated recommendation degrees of each regional book management subsystem, where w' k represents the updated recommendation degree of the regional book management subsystem k, w k represents the recommendation degree of the regional book management subsystem k, represents the recommendation degree of the target regional book management subsystem, θ is a positive number less than 1, β represents whether the target reader has borrowed the book recommended by the target regional book management subsystem. β = 0 means that the target reader has borrowed the book recommended by the target regional book management subsystem, and β = 1 means that the target reader has not borrowed the book recommended by the target regional book management subsystem. U represents the target book recommendation vector of the target reader, and a represents the book vector actually borrowed by the target reader.

[0149] Optionally, the book information is statistically obtained based on the user scanning the book code. The book code includes: a publishing organization identification code, a book category code, a publishing batch code, and a serial number. The book code is unique.

[0150] The device provided by the embodiments of the present invention has the same implementation principle and the same technical effects as the foregoing method embodiments. For a brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0151] As Figure 3 shown, an electronic device 600 provided by an embodiment of the present application includes: a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device runs, the processor 601 communicates with the memory 602 through the bus. The processor 601 executes the machine-readable instructions to perform the steps of the book recommendation method as described above.

[0152] Specifically, the foregoing memory 602 and processor 601 can be general-purpose memory and processor, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the book recommendation method as described above.

[0153] The processor 601 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor 601 or instructions in the form of software. The above-mentioned processor 601 may be a general-purpose processor, including a central processing unit (CPU for short), a network processor (NP for short), etc.; it may also be a digital signal processor (DSP for short), an application specific integrated circuit (ASIC for short), a field-programmable gate array (FPGA for short), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and combines its hardware to complete the steps of the above method.

[0154] Corresponding to the above book recommendation method, an embodiment of the present application also provides a computer-readable storage medium, and the computer-readable storage medium stores machine-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to run the steps of the above book recommendation method.

[0155] The book recommendation device provided by the embodiments of the present application may be specific hardware on a device or software or firmware installed on the device, etc. For the device provided by the embodiments of the present application, its implementation principle and the technical effects produced are the same as those of the foregoing method embodiments. For the sake of brief description, for the parts not mentioned in the device embodiments, reference may be made to the corresponding content in the foregoing method embodiments. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can all refer to the corresponding processes in the above method embodiments, and will not be repeated here.

[0156] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0157] For another example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of this application. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0158] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, the functional units in the embodiments provided in this application can be integrated in one processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0160] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing an electronic device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the book recommendation method described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0161] It should be noted that: similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0162] Finally, it should be noted that: the above-mentioned embodiments are only specific implementation manners of this application, used to illustrate the technical solution of this application, rather than limiting it. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: any person skilled in the art within the technical scope disclosed in this application can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes, or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A method for recommending books, characterized in that, Applied to a library management system, the library management system is connected to multiple regional library management subsystems, and the method includes: Obtain the book information uploaded by each regional library management subsystem and the recommendation degree of each regional library management subsystem. Among them, the book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the characteristic information of each book in each region; Determine the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region; Determine the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region; Obtain a recommendation request for recommending books to a target reader in a target region initiated by the target regional library management subsystem. Among them, the reader information carried in the recommendation request includes: the personal information of the target reader and the historical borrowing records of the target reader; Use a book recommendation model to recommend books based on the reader information of the target reader and the characteristic information of each book in each region, and obtain a borrowing probability matrix of the target reader for each book in each region; Determine the book recommendation vector of the target reader in each region according to the borrowing probability matrix of the target reader for each book in each region, the popularity of each book in each region, and the popularity of each book in each region; Determine the target book recommendation vector of the target reader based on the book recommendation vector of the target reader in each region and the recommendation degree of each regional library management subsystem, and then recommend books to the target reader according to the target book recommendation vector.

2. The method according to claim 1, wherein Determine the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region, including: According to the heat calculation formula Calculate the heat of each book in each area, where hot ij represents the heat of book i in area j, and scan_num ij represents the number of views of book i in area j, and dis_num ij represents the number of discussions of book i in area j, and damand_rate ij represents the degree of just-needed of book i in area j.

3. The method according to claim 1, characterized in that Determine the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region, including: According to the borrowing ratio calculation formula Calculate the borrowing rate of each book in each area. Among them, borrow_scan_rate ij represents the borrowing rate of book i in area j, borrow_num ij represents the borrowing times of book i in area j, scan_num ij represents the browsing times of book i in area j; Popularity calculation formula Calculate the popularity of each book in each area, where welcome_rate ij represents the popularity of book i in area j, and borrow_scan_rate ij represents the borrowing rate of book i in area j, and damand_rate ij represents the rigid demand degree of book i in area j.

4. The method according to claim 1, characterized in that, The book recommendation vector of the target reader in each region is the scoring vector of the target reader for each book in each region. Determining the book recommendation vector of the target reader in each region according to the borrowing probability matrix of the target reader for each book in each region, the popularity of each book in each region, and the popularity of each book in each region includes: Calculate the score according to the scoring formula ij = w ij * hot ij * welcome_rate ij Calculate the scores of the target reader for each book in each area, and then obtain the score vector of the target reader for each book in each area, where score ij represents the score of the target reader for book i in area j, w ij represents the borrowing probability of the target reader for book i in area j, hot ij represents the popularity of book i in area j, welcome_rate ij represents the popularity of book i in area j.

5. The method according to claim 1, characterized in that, Determine the target book recommendation vector of the target reader based on the book recommendation vector of the target reader in each region and the recommendation degree of each regional library management subsystem, including: According to the calculation formula of the target book recommendation vector Calculate the target book recommendation vector of the target reader, where U represents the target book recommendation vector of the target reader, z represents the number of the regional book management subsystems, and w k represents the recommendation degree of the regional book management subsystem k, and S k represents the book recommendation vector of the target reader in the area corresponding to the regional book management subsystem k.

6. The method according to claim 1, wherein After recommending books to the target reader according to the target book recommendation vector, the method further includes: Obtain the actual borrowing information of the target reader; If the target reader does not borrow, the recommendation degree of each regional library management subsystem remains unchanged; If the target reader borrows a book, then update the recommendation degree according to the update formula Calculate the updated recommendation degree of each regional library management subsystem, where w' k represents the updated recommendation degree of regional library management subsystem k, and w k represents the recommendation degree of regional library management subsystem k, represents the recommendation degree of the target regional library management subsystem, θ is a positive number less than 1, β represents whether the target reader has borrowed the book recommended by the target regional library management subsystem. β = 0 means that the target reader has borrowed the book recommended by the target regional library management subsystem, and β = 1 means that the target reader has not borrowed the book recommended by the target regional library management subsystem. U represents the target book recommendation vector of the target reader, and a represents the vector of the book actually borrowed by the target reader.

7. The method according to claim 1, wherein The book information is statistically obtained based on the user scanning the book code. The book code includes: a publishing institution identification code, a book category code, a publishing batch code, and a serial number, and the book code is unique.

8. A recommendation device for books, characterized in that, Applied to a library management system, the library management system is connected to multiple regional library management subsystems, and the device includes: A first acquisition unit, configured to acquire book information uploaded by each regional library management subsystem and the recommendation degrees of each regional library management subsystem, wherein the book information includes: the browsing times of each book in each region, the discussion times of each book in each region, the borrowing times of each book in each region, and the feature information of each book in each region; A first determination unit, configured to determine the popularity of each book in each region based on the browsing times of each book in each region and the discussion times of each book in each region; A second determination unit, configured to determine the popularity of each book in each region based on the borrowing times of each book in each region and the browsing times of each book in each region; A second acquisition unit, configured to acquire a recommendation request for recommending books to a target reader in a target region initiated by the target region library management subsystem, wherein the recommendation request carries reader information of the target reader, and the reader information includes: personal information of the target reader and historical borrowing records of the target reader; A book recommendation unit, configured to use a book recommendation model to recommend books based on the reader information of the target reader and the feature information of each book in each region, and obtain a borrowing probability matrix of the target reader for each book in each region; A third determination unit, configured to determine a book recommendation vector of the target reader in each region according to the borrowing probability matrix of the target reader for each book in each region, the popularity of each book in each region, and the popularity of each book in each region; A fourth determination unit, configured to determine a target book recommendation vector of the target reader based on the book recommendation vector of the target reader in each region and the recommendation degrees of each regional library management subsystem, and then recommend books for the target reader according to the target book recommendation vector.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 above are implemented.

10. A computer storage medium, characterized in that, A computer program is stored thereon, and when the computer runs the computer program, the steps of the method according to any one of claims 1 to 7 above are executed.

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