CN116738044A - Book recommendation method, device and equipment for realizing college library based on individuation - Google Patents
Book recommendation method, device and equipment for realizing college library based on individuation Download PDFInfo
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Abstract
The invention relates to the field of intelligent decision making, and discloses a book recommendation method, device and equipment for realizing a college library based on individuation, which comprises the following steps: identifying the borrowing activity and the interest book category of the borrowing user, and constructing the image of the borrowing user; extracting user borrowing characteristics of the borrowing user, searching neighbor users of the borrowing user in a pre-constructed high-school book management library, and calculating user similarity between the borrowing user and the neighbor users; calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users; the interestingness values are ordered in a descending order to obtain an interestingness sequence, and a first recommended book of the borrowing user is selected from the recommended books; inquiring corresponding class books from a high-school book management library, and taking the class books as second recommended books of borrowing users; and determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books. The method and the device can improve the accuracy of the library of the university drawings in recommending books of borrowing users.
Description
Technical Field
The invention relates to the field of intelligent decision making, in particular to a book recommendation method, device and equipment for realizing a college library based on individualization.
Background
The personalized book recommendation is to take a reader as a center, actively recommend books to a user according to the borrowing requirement of the reader, provide more accurate information and knowledge service for the reader, and gradually develop digital and networked resources along with the development of science and technology, however, how to efficiently query books required by the user in libraries with millions of books is still a big problem.
At present, most of high-school libraries provide books which are required to be borrowed by searching books independently by a traditional user, but the mode is greatly dependent on search fields and search technologies of the user, and the requirements on the books are not the same because of different interest preferences of the user, but the unidirectional search mode of 'people for books' lacks identification of the requirements or intentions of the user, and cannot personally recommend books which are actually required to the user, so that the book recommendation efficiency is low and the accuracy is not enough.
Disclosure of Invention
The invention provides a book recommendation method, device and equipment for realizing a college library based on individuation, and aims to improve book recommendation efficiency and accuracy.
In order to achieve the above purpose, the book recommendation method for realizing a college library based on individuation provided by the invention comprises the following steps:
acquiring a personal attribute tag of a borrowing user, identifying the borrowing activity degree and the interest book category of the borrowing user according to the personal attribute tag, and constructing a borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity degree and the interest book category;
extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-speed book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics
Calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users according to the user similarity and the borrowing activity;
the interestingness values are ordered in a descending order to obtain an interestingness sequence, and a first recommended book of the borrowing user is selected from the recommended books according to the interestingness sequence;
inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
And determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
In one possible implementation manner of the first aspect, the identifying, according to the personal attribute tag, the borrowing activity and the interested book category of the borrowing user includes:
inquiring historical borrowing information of a borrowing user according to the personal attribute tag, and calculating the borrowing frequency, the downloading frequency, the continuous borrowing frequency and the book type of the borrowing user;
respectively determining borrowing weights, downloading weights and renewing weights of the borrowing frequencies, the downloading frequencies and the renewing frequencies;
calculating the borrowing activity of the borrowing user according to the borrowing frequency, the downloading frequency, the continuous borrowing frequency, the borrowing weight, the downloading weight and the continuous borrowing weight;
searching the category borrowing frequency of the book type according to the borrowing activity;
and when the category borrowing frequency is larger than the preset normal borrowing frequency, judging that the book type is the interested book category.
In one possible implementation manner of the first aspect, the calculating the borrowing activity of the borrowing user according to the borrowing frequency, the downloading frequency, the renewing frequency, the borrowing weight, the downloading weight, and the renewing weight includes:
Calculating the borrowing liveness of the borrowing user by using the following formula:
wherein, active 1 (u, i) represents borrowing activity, A 1 Indicating the frequency of borrowing, A 2 Representing the download frequency, A 3 Indicating the duration of borrowing, B 1 Represents the borrowing weight, B 2 Downloading weight, B 3 Representing the renewing weight.
In a possible implementation manner of the first aspect, the extracting, according to the borrowing user portrait, a user borrowing feature of the borrowing user includes:
analyzing the personal attribute feature dimension of the borrowing user according to the borrowing user portrait;
analyzing the tree structure of the interest book class;
selecting a preset level in the tree structure as a book type feature dimension of the borrowing feature of the user;
and identifying the user borrowing characteristics of the borrowing user according to the personal attribute characteristic dimension and the book type characteristic dimension.
In a possible implementation manner of the first aspect, the searching, according to the user borrowing feature, a neighbor user of the borrowing user in a pre-constructed university book management library includes:
extracting the borrowing characteristics of the users in the Gao Jiaotu book management library;
converting the user borrowing feature and the user borrowing feature into a borrowing user feature vector and a library user feature vector respectively;
Calculating cosine similarity between the borrowing user feature vector and the library user feature vector;
and when the cosine similarity is larger than the preset similarity, taking the library user corresponding to the library user feature vector as the neighbor user of the borrowing user.
In a possible implementation manner of the first aspect, the calculating and the computing the user similarity between the borrowing user and the neighboring user include:
identifying a borrowing feature component of said user borrowing feature;
calculating the user similarity between the borrowing user and the neighbor user according to the borrowing feature component by using the following formula:
where sim (u, v) represents the user similarity, cos (u, v) represents the cosine of the angle between borrowing user u and neighbor user v,representing the product of the i-th borrowing feature component of borrowing user u and the i-th adjacent borrowing feature component of neighbor user v.
In one possible implementation manner of the first aspect, the calculating, according to the user similarity and the borrowing activity, an interest level value of the borrowing user on the recommended book corresponding to the neighboring user includes the following formula:
calculating the interest degree value of the borrowing user on the recommended books corresponding to the adjacent users by using the following formula:
Wherein Inter (u, i) represents the interest value of borrowing user u to recommended book i, active (v, i) represents the borrowing activity of neighbor user v to recommended book i, sim (u, v) represents the user similarity between borrowing user u and neighbor user v, u k Representing a k-nearest neighbor set of borrowing users u;
in a second aspect, the present invention also provides a book recommendation device for implementing a college library based on personalization, the device comprising:
the user portrait construction module is used for acquiring personal attribute tags of borrowing users, identifying the borrowing activity degree and the interest book category of the borrowing users according to the personal attribute tags, and constructing the borrowing user portrait of the borrowing users according to the personal attribute tags, the borrowing activity degree and the interest book category;
the neighbor user search module is used for extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-speed book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics
The interest value calculation module is used for calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users according to the user similarity and the borrowing activity;
The first recommended book generation module is used for ordering the interestingness values in a descending order to obtain an interestingness sequence, and selecting the first recommended books of the borrowing users from the recommended books according to the interestingness sequence;
the second recommended book generation module is used for inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
and the final recommended book generation module is used for determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to implement the book recommendation method for implementing a college library based on personalization described above.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned book recommendation method for implementing a college library based on personalization.
According to the method and the device, personal attribute tags of the borrowing user can be obtained to provide guarantee for follow-up personalized recommended books, the borrowing activity and the interested book category of the borrowing user are identified according to the attribute tags to extract personalized borrowing characteristics of the borrowing user, more accurate books can be recommended to the borrowing user, and according to the personal attribute tags, the borrowing activity and the interested book category, close searching can be carried out for follow-up images of the borrowing user to take recommended books of adjacent users as candidate books; secondly, according to the image of the borrowing user, the user borrowing characteristics of the borrowing user are extracted, the user borrowing characteristics can be extracted to be the next adjacent user to be searched for generating a first recommended book, the next adjacent user of the borrowing user is searched in a pre-built high-school book management library for generating the recommended book of the borrowing user by using the recommended book of the next adjacent user, and the user similarity between the borrowing user and the next adjacent user is calculated to provide support for the subsequent calculation of the interest value of the recommended book corresponding to the next adjacent user; further, according to the embodiment of the invention, the interest degree value of the borrowing user on the recommended books corresponding to the adjacent user is calculated according to the user similarity and the borrowing activity degree to screen out the recommended books for the follow-up, the interest degree value is sorted in a descending order, the obtained interest degree sequence can provide guarantee for screening the first recommended books from the first recommended books, the first recommended books of the borrowing user are selected from the recommended books to provide preconditions for producing the final recommended books by combining the second recommended books, the corresponding class books are inquired from the Gao Jiaotu book management library according to the interest book class, the class books are used as the second recommended books of the borrowing user to generate the final recommended book preconditions, and the final recommended books of the borrowing user are determined according to the first recommended books and the second recommended books, so that more accurate, active and personalized book recommendation services can be provided for the borrowing user by using the hybrid recommendation method. Therefore, the book recommendation method, the device and the equipment for realizing the college library based on individualization can improve the accuracy of the college library in recommending books by borrowing users.
Drawings
FIG. 1 is a flowchart of a book recommendation method for implementing a college library based on personalization according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a library recommendation device for implementing a college library based on personalization according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an internal structure of an electronic device for implementing a book recommendation method based on personalized implementation of a college library according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides a book recommendation method for realizing a college library based on individualization. The execution subject of the book recommendation method based on the personalized implementation of the college library comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the invention. In other words, the book recommendation method based on the personalized implementation of the college library may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flowchart of a book recommendation method for implementing a college library based on personalization according to an embodiment of the present invention is shown. In the embodiment of the invention, the book recommendation method for realizing the college library based on individualization comprises the following steps:
s1, acquiring a personal attribute tag of a borrowing user, identifying the borrowing activity degree and the interest book category of the borrowing user according to the personal attribute tag, and constructing a borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity degree and the interest book category;
in the embodiment of the invention, the personal attribute tag of the borrowing user refers to the description of the user attribute in the borrowing of the high-school drawing, and is used for representing the basic information characteristics related to the borrowing of the user, such as gender, age, grade, specialty, college where the user is located, identity type (Gramineae, shuoshi, doctor, teacher) and the like, and the personal attribute tag can be obtained through a data script, and the data script can be compiled through a JS script language. The borrowing user is a user facing a book borrowing service such as a college library for people to read and reference.
Further, according to the personal attribute tag, the borrowing activity and the interested book category of the borrowing user are identified, so that personalized borrowing characteristics of the borrowing user are extracted, and more accurate books can be recommended to the borrowing user. The borrowing activity is related behavior of a user to borrow books in a certain time dimension, and comprises paper book borrowing frequency, electronic book downloading frequency and continuous borrowing frequency. The interest book category is a description of corresponding attributes and type preferences of books of interest to a user, and comprises book attributes and book types, wherein the book attributes refer to basic information attributes of books, and comprise book IDs, book names, ISBN numbers, authors and the like, and the book types represent classification numbers of books corresponding to Chinese library classification methods.
Further, as an optional embodiment of the present invention, the identifying, according to the personal attribute tag, the borrowing activity and the interested book category of the borrowing user includes: inquiring historical borrowing information of a borrowing user according to the personal attribute tag, and calculating the borrowing frequency, the downloading frequency, the continuous borrowing frequency and the book type of the borrowing user; respectively determining borrowing weights, downloading weights and renewing weights of the borrowing frequencies, the downloading frequencies and the renewing frequencies; calculating the borrowing activity of the borrowing user according to the borrowing frequency, the downloading frequency, the continuous borrowing frequency, the borrowing weight, the downloading weight and the continuous borrowing weight; searching the category borrowing frequency of the book type according to the borrowing activity; and when the category borrowing frequency is larger than the preset normal borrowing frequency, judging that the book type is the interested book category.
The borrowing frequency, the downloading frequency and the continuous borrowing frequency refer to the borrowing downloading and book continuous times of the borrowing user, the borrowing weight, the downloading weight and the continuous borrowing weight refer to weight coefficients given to the borrowing frequency, the downloading frequency and the continuous borrowing frequency, and the borrowing activity refers to the borrowing activity degree of the borrowing user.
Further, in an alternative embodiment of the present invention, the borrowing frequency of the borrowing user is calculated by using the following formula:
wherein f represents borrowing frequency, T r (u, i) time for borrowing user u to return book i; t (T) b (u, i) the time at which the borrowing user u borrows the book i; t (T) c Is the overrun threshold of the library.
Further, in an optional embodiment of the present invention, the calculating the borrowing activity of the borrowing user according to the borrowing frequency, the downloading frequency, the renewing frequency, the borrowing weight, the downloading weight, and the renewing weight includes:
calculating the borrowing liveness of the borrowing user by using the following formula:
wherein, active 1 (u, i) represents borrowing activity, A 1 Indicating the frequency of borrowing, A 2 Representing the download frequency, A 3 Indicating the duration of borrowing, B 1 Represents the borrowing weight, B 2 Downloading weight, B 3 Representing the renewing weight.
Further, according to the embodiment of the invention, by constructing the borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity and the interest book category, neighbor searching can be performed for the follow-up, so that the recommended books of the adjacent users can be used as the recommended books of the candidate users. The borrowing user portrait is a labeled user model which is abstracted by outlining user characteristics, describing user interest requirements and comprehensively and carefully describing information overview of a user on the basis of user real data.
As an optional embodiment of the present invention, the construction of the borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity level and the interested book category may be implemented through a user portrait model.
S2, extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-order book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics
According to the method, the device and the system for searching the adjacent user, the user borrowing characteristics of the borrowing user can be extracted according to the borrowing user portrait, and support is provided for the subsequent searching of the adjacent user to generate the first recommended book.
Further, as an optional embodiment of the present invention, the extracting the user borrowing feature of the borrowing user according to the borrowing user portrait includes: analyzing the personal attribute feature dimension of the borrowing user according to the borrowing user portrait; analyzing the tree structure of the interest book class; selecting a preset level in the tree structure as a book type feature dimension of the borrowing feature of the user; and identifying the user borrowing characteristics of the borrowing user according to the personal attribute characteristic dimension and the book type characteristic dimension.
The personal attribute feature dimension refers to the personal feature dimension of the borrowing user, the tree structure refers to the book management structure of the Gao Jiaotu book management library, and the book type feature dimension refers to the book feature dimension of the interested book class.
Further, according to the borrowing characteristics of the user, the neighbor users of the borrowing user are searched in a pre-built high-school book management library so that recommended book preconditions of the borrowing user can be generated by using recommended books of the neighbor users. The pre-constructed high-school book management library is a database of high-school management books created based on BIM technology, and comprises book information, reader information, borrowing information, recently popular borrowing books and the like. The neighbor users refer to a plurality of users with the characteristics most similar to the characteristics of the current user.
Further, as an optional embodiment of the present invention, the searching, according to the user borrowing feature, the neighbor users of the borrowing user in the pre-constructed university book management library includes: extracting the borrowing characteristics of the users in the Gao Jiaotu book management library; converting the user borrowing feature and the user borrowing feature into a borrowing user feature vector and a library user feature vector respectively; calculating cosine similarity between the borrowing user feature vector and the library user feature vector; and when the cosine similarity is larger than the preset similarity, taking the library user corresponding to the library user feature vector as the neighbor user of the borrowing user.
The user borrowing feature refers to borrowing features of users in the Gao Jiaotu book management library, and the borrowing user feature vector and the library user feature vector refer to vectors obtained by performing spatial mapping on the borrowing user feature and the library user feature.
Further, the embodiment of the invention can provide support for the subsequent calculation of the interest level value of the recommended book corresponding to the neighbor user by calculating the user similarity between the borrowing user and the neighbor user.
Identifying a borrowing feature component of said user borrowing feature;
calculating the user similarity between the borrowing user and the neighbor user according to the borrowing feature component by using the following formula:
where sim (u, v) represents the user similarity, cos (u, v) represents the cosine of the angle between borrowing user u and neighbor user v,representing the product of the i-th borrowing feature component of borrowing user u and the i-th adjacent borrowing feature component of neighbor user v.
S3, calculating an interest level value of the borrowing user for the recommended books corresponding to the adjacent users according to the user similarity and the borrowing activity;
according to the method and the device for selecting the recommended books, the interest level value of the borrowing user for the recommended books corresponding to the neighbor user is calculated according to the similarity of the user and the borrowing activity, so that support is provided for subsequent screening of the recommended books.
Further, as an optional embodiment of the present invention, according to the user similarity and the borrowing activity, the interest level value of the borrowing user for the recommended book corresponding to the neighboring user is calculated according to the following formula:
wherein Inter (u, i) represents the interest value of borrowing user u to borrowing book i, active (v, i) represents the borrowing activity of neighbor user v to borrowing book i, sim (u, v) represents the user similarity between borrowing user u and neighbor user v, u k Represents the k-nearest neighbor set of borrowing user u, e represents belonging to the symbol.
S4, ordering the interestingness values in a descending order to obtain an interestingness sequence, and selecting a first recommended book of the borrowing user from the recommended books according to the interestingness sequence;
according to the embodiment of the invention, the interestingness values are sorted in a descending order, so that the interestingness sequence is obtained, and a guarantee can be provided for further screening the first recommended books from the interestingness sequence.
Further, as an optional embodiment of the present invention, the step of ordering the interestingness values in a descending order may be implemented by using the interestingness value comparison method.
Further, according to the embodiment of the invention, the first recommended book can be obtained by selecting the first recommended book of the borrowing user from the recommended books, so that a precondition is provided for the subsequent production of the final recommended book by combining the second recommended book.
Further, as an optional embodiment of the present invention, the selecting, according to the interestingness sequence, the first recommended book of the borrowing user from the recommended books includes: selecting the recommended books corresponding to the first N interestingness values in the interestingness sequence with the interestingness value larger than a preset threshold according to the interestingness sequence to obtain candidate recommended books; and filtering historical borrowed books read by the borrowing user in the candidate recommended books to obtain target recommended books, and taking the target recommended books as first recommended books of the borrowing user.
S5, inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
according to the embodiment of the invention, the corresponding class books are inquired from the Gao Jiaotu book management library according to the interest book class, and the class books are used as the second recommended books of the borrowing user, so that the second recommended books of the relevant lessons of the university can be obtained according to the personal attribute labels of the borrowing user, and a guarantee is provided for the subsequent generation of final personalized recommended books in combination with the first recommended books.
Further, as an optional embodiment of the present invention, the querying, according to the interest book category, a corresponding category book from the Gao Jiaotu book management library, and taking the category book as the second recommended book of the borrowing user includes: and mapping the interested book category with books in the Gao Jiaotu book management library to generate a second recommended book of the borrowing user.
S6, determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
According to the embodiment of the invention, the final recommended books of the borrowing user can be determined according to the first recommended books and the second recommended books by using the mixed book recommendation method, so that more accurate, active and personalized book recommendation services can be provided for the borrowing user.
Further, as an optional embodiment of the present invention, the determining the final recommended book of the borrowing user according to the first recommended book and the second recommended book may be determined by merging the first recommended book and the second recommended book, and removing the repeated recommended books.
According to the method and the device, personal attribute tags of the borrowing user can be obtained to provide guarantee for follow-up personalized recommended books, the borrowing activity and the interested book category of the borrowing user are identified according to the attribute tags to extract personalized borrowing characteristics of the borrowing user, more accurate books can be recommended to the borrowing user, and according to the personal attribute tags, the borrowing activity and the interested book category, close searching can be carried out for follow-up images of the borrowing user to take recommended books of adjacent users as candidate books; secondly, according to the image of the borrowing user, the user borrowing characteristics of the borrowing user are extracted, the user borrowing characteristics can be extracted to be the next adjacent user to be searched for generating a first recommended book, the next adjacent user of the borrowing user is searched in a pre-built high-school book management library for generating the recommended book of the borrowing user by using the recommended book of the next adjacent user, and the user similarity between the borrowing user and the next adjacent user is calculated to provide support for the subsequent calculation of the interest value of the recommended book corresponding to the next adjacent user; further, according to the embodiment of the invention, the interest degree value of the borrowing user on the recommended books corresponding to the adjacent user is calculated according to the user similarity and the borrowing activity degree to screen out the recommended books for the follow-up, the interest degree value is sorted in a descending order, the obtained interest degree sequence can provide guarantee for screening the first recommended books from the first recommended books, the first recommended books of the borrowing user are selected from the recommended books to provide preconditions for producing the final recommended books by combining the second recommended books, the corresponding class books are inquired from the Gao Jiaotu book management library according to the interest book class, the class books are used as the second recommended books of the borrowing user to generate the final recommended book preconditions, and the final recommended books of the borrowing user are determined according to the first recommended books and the second recommended books, so that more accurate, active and personalized book recommendation services can be provided for the borrowing user by using the hybrid recommendation method. Therefore, the book recommendation method for realizing the college library based on individualization can improve the accuracy of the college library in recommending books by borrowing users.
As shown in FIG. 2, the function module diagram of the book recommendation device for realizing college libraries based on individualization of the invention is shown.
The book recommendation device 100 for realizing the college library based on individualization can be installed in electronic equipment. Depending on the implemented functions, the book recommendation device based on the personalized implementation of the college library may include a user portrait construction module 101, a neighboring user search module 102, an interestingness value calculation module 103, a first recommended book generation module 104, a second recommended book generation module 105, and a final recommended book generation module 106. The module according to the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the user portrait construction module 101 is configured to obtain a personal attribute tag of a borrowing user, identify a borrowing activity level and an interest book category of the borrowing user according to the personal attribute tag, and construct a borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity level and the interest book category;
The neighbor user search module 102 is configured to extract user borrowing features of the borrowing user according to the borrowing user portrait, search a pre-constructed high-school book management library for neighbor users of the borrowing user according to the user borrowing features, and calculate user similarity between the borrowing user and the neighbor users according to the user borrowing features
The interestingness value calculation module 103 is configured to calculate, according to the user similarity and the borrowing activity, an interestingness value of the borrowing user for the recommended book corresponding to the neighboring user;
the first recommended book generating module 104 is configured to sort the interestingness values in a descending order to obtain an interestingness sequence, and select a first recommended book of the borrowing user from the recommended books according to the interestingness sequence;
the second recommended book generating module 105 is configured to query, according to the interest book category, a corresponding category book from the Gao Jiaotu book management library, and use the category book as a second recommended book of the borrowing user;
the final recommended book generating module 106 is configured to determine a final recommended book of the borrowing user according to the first recommended book and the second recommended book.
In detail, the modules in the book recommendation device 100 for implementing a college library based on personalization in the embodiment of the present invention use the same technical means as the book recommendation method for implementing a college library based on personalization described in fig. 1, and can generate the same technical effects, which are not described herein.
As shown in fig. 3, a schematic structural diagram of an electronic device 1 for implementing a book recommendation method based on individualization in a college library according to the present invention is shown.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a book recommendation program for implementing a college library based on personalization.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects the respective components of the entire electronic device 1 using various interfaces and lines, executes programs or modules stored in the memory 11 (for example, executes a book recommendation program or the like for realizing a college library based on personalization), and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes based on a book recommendation program for personalizing a library of a college, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device 1 and other devices, including a network interface and an employee interface. Optionally, the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices 1. The employee interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual staff interface.
Fig. 3 shows only an electronic device 1 with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited in scope by this configuration.
The book recommendation program stored in the memory 11 of the electronic device 1 for realizing a college library based on personalization is a combination of a plurality of computer programs, which when run in the processor 10 can realize:
acquiring a personal attribute tag of a borrowing user, identifying the borrowing activity degree and the interest book category of the borrowing user according to the personal attribute tag, and constructing a borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity degree and the interest book category;
Extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-speed book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics
Calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users according to the user similarity and the borrowing activity;
the interestingness values are ordered in a descending order to obtain an interestingness sequence, and a first recommended book of the borrowing user is selected from the recommended books according to the interestingness sequence;
inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
and determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1 may be stored in a non-volatile computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device 1, may implement:
acquiring a personal attribute tag of a borrowing user, identifying the borrowing activity degree and the interest book category of the borrowing user according to the personal attribute tag, and constructing a borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity degree and the interest book category;
extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-speed book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics
Calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users according to the user similarity and the borrowing activity;
the interestingness values are ordered in a descending order to obtain an interestingness sequence, and a first recommended book of the borrowing user is selected from the recommended books according to the interestingness sequence;
inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
and determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the invention can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A book recommendation method for realizing a college library based on individualization, the method comprising:
acquiring a personal attribute tag of a borrowing user, identifying the borrowing activity degree and the interest book category of the borrowing user according to the personal attribute tag, and constructing a borrowing user portrait of the borrowing user according to the personal attribute tag, the borrowing activity degree and the interest book category;
extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-school book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics;
Calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users according to the user similarity and the borrowing activity;
the interestingness values are ordered in a descending order to obtain an interestingness sequence, and a first recommended book of the borrowing user is selected from the recommended books according to the interestingness sequence;
inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
and determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
2. The book recommendation method for realizing a college library based on personalization of claim 1, wherein the identifying the borrowing activity and the interested book category of the borrowing user according to the personal attribute tag comprises:
inquiring historical borrowing information of a borrowing user according to the personal attribute tag, and calculating the borrowing frequency, the downloading frequency, the continuous borrowing frequency and the book type of the borrowing user;
respectively determining borrowing weights, downloading weights and renewing weights of the borrowing frequencies, the downloading frequencies and the renewing frequencies;
Calculating the borrowing activity of the borrowing user according to the borrowing frequency, the downloading frequency, the continuous borrowing frequency, the borrowing weight, the downloading weight and the continuous borrowing weight;
searching the category borrowing frequency of the book type according to the borrowing activity;
and when the category borrowing frequency is larger than the preset normal borrowing frequency, judging that the book type is the interested book category.
3. The book recommendation method for realizing a college library based on personalization of claim 2, wherein the calculating the borrowing activity of the borrowing user according to the borrowing frequency, the downloading frequency, the renewing frequency, the borrowing weight, the downloading weight and the renewing weight comprises:
calculating the borrowing liveness of the borrowing user by using the following formula:
wherein, active 1 (u, i) represents borrowing activity, A 1 Indicating the frequency of borrowing, A 2 Representing the download frequency, A 3 Indicating the duration of borrowing, B 1 Represents the borrowing weight, B 2 Downloading weight, B 3 Representing the renewing weight.
4. The book recommendation method for realizing a college library based on personalization of claim 1, wherein the extracting the user borrowing feature of the borrowing user according to the borrowing user portrait comprises:
Analyzing the personal attribute feature dimension of the borrowing user according to the borrowing user portrait;
analyzing the tree structure of the interest book class;
selecting a preset level in the tree structure as a book type feature dimension of the borrowing feature of the user;
and identifying the user borrowing characteristics of the borrowing user according to the personal attribute characteristic dimension and the book type characteristic dimension.
5. The book recommendation method for realizing a college library based on personalization of claim 1, wherein searching a pre-built college book management library for a neighboring user of the borrowed user according to the user borrowing feature comprises:
extracting the borrowing characteristics of the users in the Gao Jiaotu book management library;
converting the user borrowing feature and the user borrowing feature into a borrowing user feature vector and a library user feature vector respectively;
calculating cosine similarity between the borrowing user feature vector and the library user feature vector;
and when the cosine similarity is larger than the preset similarity, taking the library user corresponding to the library user feature vector as the neighbor user of the borrowing user.
6. The book recommendation method for realizing a college library based on personalization of claim 1, wherein the calculating the user similarity between the borrowed user and the neighbor user according to the user borrowing feature comprises:
Identifying a borrowing feature component of said user borrowing feature;
calculating the user similarity between the borrowing user and the neighbor user according to the borrowing feature component by using the following formula:
where sim (u, v) represents the user similarity, cos (u, v) represents the cosine of the angle between borrowing user u and neighbor user v,representing the product of the i-th borrowing feature component of borrowing user u and the i-th adjacent borrowing feature component of neighbor user v.
7. The book recommendation method for realizing a college library based on individualization as claimed in claim 1, wherein the calculating the interest level value of the borrowing user for the recommended book corresponding to the neighbor user according to the user similarity and the borrowing activity comprises:
calculating the interest degree value of the borrowing user on the recommended books corresponding to the adjacent users by using the following formula:
wherein Inter (u, i) represents the interest value of borrowing user u to recommended book i, active (v, i) represents the borrowing activity of neighbor user v to recommended book i, sim (u, v) represents the user similarity between borrowing user u and neighbor user v, u k Representing the k-nearest neighbor set of borrowing users u.
8. A book recommendation device for realizing a college library based on personalization, the device comprising:
The user portrait construction module is used for acquiring personal attribute tags of borrowing users, identifying the borrowing activity degree and the interest book category of the borrowing users according to the personal attribute tags, and constructing the borrowing user portrait of the borrowing users according to the personal attribute tags, the borrowing activity degree and the interest book category;
the neighbor user search module is used for extracting user borrowing characteristics of the borrowing user according to the borrowing user portrait, searching neighbor users of the borrowing user in a pre-constructed high-speed book management library according to the user borrowing characteristics, and calculating user similarity between the borrowing user and the neighbor users according to the user borrowing characteristics
The interest value calculation module is used for calculating the interest value of the borrowing user for the recommended books corresponding to the neighbor users according to the user similarity and the borrowing activity;
the first recommended book generation module is used for ordering the interestingness values in a descending order to obtain an interestingness sequence, and selecting the first recommended books of the borrowing users from the recommended books according to the interestingness sequence;
the second recommended book generation module is used for inquiring corresponding class books from the Gao Jiaotu book management library according to the interest book class, and taking the class books as second recommended books of the borrowing user;
And the final recommended book generation module is used for determining the final recommended books of the borrowing user according to the first recommended books and the second recommended books.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the book recommendation method of implementing a college library based on personalization as claimed in any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the book recommendation method for implementing a college library based on personalization according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310670028.5A CN116738044A (en) | 2023-06-07 | 2023-06-07 | Book recommendation method, device and equipment for realizing college library based on individuation |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310670028.5A CN116738044A (en) | 2023-06-07 | 2023-06-07 | Book recommendation method, device and equipment for realizing college library based on individuation |
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| CN202310670028.5A Withdrawn CN116738044A (en) | 2023-06-07 | 2023-06-07 | Book recommendation method, device and equipment for realizing college library based on individuation |
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| CN (1) | CN116738044A (en) |
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