A graded reading method and system for improving students' reading ability

Abstract

The present invention relates to the technical field of data processing, and in particular, to a hierarchical reading method and system for improving students' reading ability. The method includes the steps of: collecting the reading data of each user, obtaining the reading enthusiasm of each user for each book every day, and based on the reading enthusiasm, obtaining the love degree of each user for each book; classifying all books to obtain each category of books, obtaining the preference degree of each user for each category of books, obtaining the recommendation degree of each user for each category of books with reference to each other user based on the preference degree, obtaining the recommendation degree of each user for each category of books according to the recommendation degree of each user for each category of books with reference to each other user, and making book recommendations for each user based on the recommendation degree of each user for each category of books. The present invention can accurately make book recommendations for users according to their preferences and improve the reading ability of users.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a graded reading method and system for improving students' reading ability. Background Art

[0002] The graded reading method plays an important role in improving students' reading ability. The graded reading method can provide personalized learning experiences according to students' reading levels, enabling each student to study on reading materials suitable for their own level. By gradually providing reading materials of different difficulties, the graded reading method can gradually challenge students and encourage them to strive to improve their reading ability at an appropriate level. When students succeed in reading materials suitable for their own level, they will build confidence in reading and thus be more willing to try reading other books. Therefore, the present invention can recommend books based on users' preferences for books, improve users' reading interest, and thereby improve users' reading ability.

[0003] Currently, the patent application document with the publication number CN116244525A discloses a campus service system based on a collaborative filtering algorithm. Users, individually or as a unit, can create social units through the social platform sub-module and invite designated groups to join or have designated groups apply. The social platform sub-module is used for daily social communication, and the control main module sorts out the communication information content to form a database and shares it with the community service sub-module and the information service sub-module for data retrieval. The information service sub-module captures the daily communication information content of different users and, according to the user type through the collaborative filtering algorithm, the present invention can provide a comprehensive campus mobile application service software system for college students, which can meet the multi-faceted needs of students' study and life, information release, leisure and entertainment, shopping, second-hand market, etc.

[0004] Since users' preferences for books may change over time, when using the collaborative filtering algorithm in the above method to recommend books to users based on their preferences for books, only static historical interaction data is usually considered. If users' preferences change, the collaborative filtering algorithm may not accurately reflect users' current preferences, resulting in inaccurate book recommendation results, reducing users' reading interest, and thus unable to effectively improve reading ability. Summary of the Invention

[0005] To solve the problem that the collaborative filtering algorithm only considers static historical interaction data and thus cannot accurately reflect users' current preferences, resulting in inaccurate book recommendation results, the present invention provides a graded reading method and system for improving students' reading ability.

[0006] In a first aspect, the present invention provides a graded reading method for improving students' reading ability, adopting the following technical solution:

[0007] A graded reading method for improving students' reading ability, including the steps of:

[0008] Collecting the reading data of each user;

[0009] Obtaining the love degree of each user for each book, where the love degree represents the degree to which the user likes to read the book; obtaining the preference degree of each user for each category of books, where the preference degree is obtained by weighted summing the love degree of the user for each book in each category of books with the reciprocal of the number of days between the time when the user last read each book in each category of books and the current time as the weight;

[0010] Obtaining the recommendation degree of each user for each category of books with reference to each other user ; where represents the recommendation degree of the th user for the th category of books with reference to the th user; represents the preference degree of the th user for the th category of books; represents the preference degree of the th user other than the th user for the th category of books; represents the similarity degree of preference between the th user and the th user other than the th user; represents the maximum value among the similarity degrees of preference between the th user and all other users other than the th user;

[0011] Based on the recommendation degree of each user for each category of books with reference to each other user, obtaining the recommendation degree of each user for each category of books; and recommending books to each user based on the recommendation degree of each user for each category of books.

[0012] The beneficial effects are as follows: The innovation of the present invention lies in obtaining the recommendation degree of each user for each type of book based on the preference degree of each user for each type of book, and obtaining the recommendation degree of each user for each type of book by referring to the recommendation degree of each other user for each type of book. Based on the recommendation degree of each user for each type of book, book recommendations are made for each user, which can accurately reflect the current preferences of each user, and also consider the book recommendation degrees of other users with similar preferences to each user, making the book recommendations for each user more accurate, capable of enhancing the reading interest of users, and thus effectively improving the reading ability. Further, the preference degree of each user for each type of book is obtained according to the love degree of each user for each type of book and the time interval between reading each type of book and the current time, which can accurately reflect the current preferences of users and facilitate subsequent recommendations; further, the acquisition of the recommendation degree of each user referring to the recommendation degree of each other user for each type of book facilitates subsequent recommendations for another user by referring to the books in each type of book read by one user.

[0013] Preferably, the obtaining of the love degree of each user for each book includes the steps of:

[0014] Obtaining the reading enthusiasm of each user for each book every day;

[0015] Taking the maximum value of the reading enthusiasm of each user for each book over all days as the love degree of each user for each book.

[0016] Preferably, the formula for obtaining the reading enthusiasm of each user for each book every day is:

[0017] ;

[0018] In the formula, represents the reading enthusiasm of the rd user for the th book on the th day; represents the single-day browsing times of the rd user for the th book on the th day; represents the th browsing duration of the rd user for the th book on the th day; represents the normalization function.

[0019] Preferably, the formula for obtaining the preference degree of each user for each type of book is:

[0020] ;

[0021] In the formula, Represents the preference level of the th user for the th type of book; Represents the number of books in the th type of book; Represents the love level of the th user for the th book in the th type of book; Represents the number of days between the time when the th user last read the th book in the th type of book and the current time.

[0022] The preference level of each user for each type of book represents which type of book each user prefers to read, facilitating the subsequent acquisition of the recommendation degree of each user for each type of book based on the preference level of each user for each type of book.

[0023] Preferably, the acquisition of the similarity degree of preference includes:

[0024] Sort the preference levels of each user for each type of book in the order of the same book category to obtain the book preference level sequence of each user; use the DTW algorithm to obtain the similarity between the book preference level sequence of the th user and the book preference level sequence of the th user except the th user as the similarity degree of preference between the th user and the th user except the th user.

[0025] The similarity degree of preference represents the similarity degree of the books read by two users before, facilitating the subsequent book recommendation from one user with similar preferences to another user.

[0026] Preferably, the formula for calculating the recommendation degree of each user for each type of book is:

[0027] ;

[0028] In the formula, represents the recommendation degree of the th user for the th type of book; represents the maximum value of the recommendation degrees of the th user referring to all other users for the th type of book; represents the th user referring to each other user for the The average recommendation degree of each type of book; Represents a normalization function.

[0029] The recommendation degree of each user for each type of book represents which type of book each user prefers to read, so that book recommendations can be made for each user, making the book recommendations for each user more accurate.

[0030] Preferably, the book recommendation for each user based on the recommendation degree of each user for each type of book includes:

[0031] Obtain the maximum value among the recommendation degrees of the -th user for each type of book, and record the type of book corresponding to the maximum value as the -th type of book; Obtain the maximum value among the recommendation degrees of the -th user referring to the recommendation degrees of each other user for the -th type of book, and record the other user corresponding to the maximum value as the reference user, and screen out the books of the -th type of book that the reference user has read and the -th user has not read, and recommend them to the -th user.

[0032] When making a recommendation for any user, it is necessary to refer to the recommendation degrees of this user referring to each other user for each type of book, and be able to recommend books to this user based on the books of each type read by other users. Considering the books read by other users with similar reading habits to this user for recommendation, making the book recommendation for this user more accurate.

[0033] In a second aspect, the present invention provides a hierarchical reading system for improving students' reading ability, adopting the following technical solution:

[0034] A hierarchical reading system for improving students' reading ability includes: a processor and a memory, and the memory stores computer program instructions, which implement the above-mentioned hierarchical reading method for improving students' reading ability when executed by the processor.

[0035] By adopting the above technical solution, the above-mentioned hierarchical reading method for improving students' reading ability is generated into a computer program and stored in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0036] The present invention has the following technical effects: The innovation of the present invention lies in obtaining the recommendation degree of each user for each type of book according to the preference degree of each user for each type of book, and obtaining the recommendation degree of each user for each type of book by referring to the recommendation degree of each other user for each type of book. Based on the recommendation degree of each user for each type of book, book recommendations are made for each user, which can accurately reflect the current preferences of each user. It also considers the book recommendation degrees of other users with similar preferences to each user, making the book recommendations for each user more accurate, capable of enhancing the reading interest of users, and thus effectively improving the reading ability. Further, the preference degree of each user for each type of book is obtained according to the love degree of each user for each type of book and the time interval between reading each type of book and the current time, which can accurately reflect the current preferences of users and facilitate subsequent recommendations. Further, obtaining the recommendation degree of each user by referring to the recommendation degree of each other user for each type of book facilitates subsequent recommendations for another user by referring to the books read by one user in each type of book. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0038] Figure 1 is a flowchart of a method for a hierarchical reading method for improving students' reading ability according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] It should be understood that when the claims, the specification, and the drawings of the present invention use terms such as "first" and "second", they are only used to distinguish different objects and not to describe a specific order. The terms "including" and "comprising" used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0041] An embodiment of the present invention discloses a hierarchical reading method for improving students' reading ability, referring to Figure 1 , including steps S1 - S3:

[0042] S1: Collect the reading data of each user.

[0043] Step S1 includes step S10, which is specifically as follows:

[0044] S10: Collect the reading data of each user.

[0045] Specifically, obtain the number of single-day views of each user for each book and the duration of each view in the database of the reading system for consecutive days, and record it as the reading data of each user. In the embodiments of the present invention, the number of preset days , in other embodiments, the implementer can preset according to the specific implementation situation value.

[0046] S2: Obtain the reading enthusiasm of each user for each book every day, and based on the reading enthusiasm, obtain the love degree of each user for each book; classify all books to obtain each category of books, obtain the preference degree of each user for each category of books, and based on the preference degree, obtain the recommendation degree of each user for each category of books with reference to other users.

[0047] It should be noted that since the user's preference for books may change over time, when using the collaborative filtering algorithm to recommend books to users in combination with the user's preference for books, usually only static historical interaction data is considered. If the user's preference changes, the collaborative filtering algorithm may not accurately reflect the user's current preference, resulting in inaccurate recommendation results, reducing the user's reading interest, and thus unable to effectively improve the reading ability.

[0048] Step S2 includes steps S20 - S21, which are specifically as follows:

[0049] S20: Obtain the reading enthusiasm of each user for each book every day, and based on the reading enthusiasm, obtain the love degree of each user for each book; classify all books to obtain each category of books, and obtain the preference degree of each user for each category of books.

[0050] It should be noted that the longer the single browsing time of a user for any book, the more attractive the content of the book to the user. The more single-day views of a user for any book, the more the user likes the content of the book. Therefore, according to the number of single-day views and the duration of each view of a user for any book on a certain day, obtain the reading enthusiasm of the user for the book on that day.

[0051] In the embodiments of the present invention, obtain the reading enthusiasm of the i-th user for the m-th book on the n-th day:

[0052] ;

[0053] In the formula, represents the reading enthusiasm of the th user for the th book on the th day; represents the number of single-day views of the th user for the th book on the th day; represents the th user's th viewing duration of the th book on the jth time; represents the normalization function; when the th user's number of views of the th book on the th day is larger, and the viewing duration of each view is getting longer and longer, it indicates that the th user is more interested in the book on the th day. At this time, the th user's reading enthusiasm for the th book is higher, that is, the

[0054] It should be noted that since a book may be read for several days, there may be a situation where one book is finished and then the next one is read. Therefore, when a user has reading enthusiasm for any book for several days, the maximum reading enthusiasm is selected to obtain the user's love degree for the book.

[0055] In the embodiment of the present invention, to obtain the love degree of the th user for the th book: The maximum value of the reading enthusiasm of the th user for the th book in all days is used as the th user's love degree for the th book.

[0056] It should be noted that the higher the user's love degree for any book, the more the user likes to read the book. Also, since the user's preferences may change over time, when the time interval between the time when the user last read the book and the current time is shorter, it indicates that the user likes to read the book more, and at the same time, the user is more likely to like the data of the category to which the book belongs. For example, if a user likes any historical book, then the user is more likely to like historical books. Therefore, first, when the user's love degree for each book in each category of books is higher and the number of days between the time when the user last read each book and the current time is smaller, it indicates that the user's preference degree for each category of books is higher.

[0057] In the embodiments of the present invention, all books are classified to obtain each category of books.

[0058] Obtain the degree of preference of the

[0059] ;

[0060] In the formula, represents the degree of preference of the th user for the th category of books; represents the number of books in the th category of books; represents the degree of love of the th user for the th book in the th category of books; represents the number of days between the time when the th user last read the th book in the th category of books and the current time. The smaller the number of days between, the closer the book is to the user's current reading preference at this time and the more reference value it has; represents the degree of preference of the th user for the th book in the th category of books. When the degree of love for the th book is higher and the number of days between the time when the th book was last read and the current time is shorter, it means that the th user has a higher degree of preference for the th book in the th category of books. When the degree of preference of the th user for each book in the th category of books is higher, it means that the th user has a higher degree of preference for the th category of books.

[0061] S21: Obtain the recommendation degree of each user for each category of books with reference to each other user.

[0062] It should be noted that when the preference degrees of two users for the same category of books are similar, it indicates that both users like this category of books. Subsequently, book recommendations can be made for one user by referring to the books in this category that the other user has read. Therefore, in the embodiments of the present invention, for any user, the greater the preference degree of the user for any category of books, the higher the recommendation degree of the user for this category of books. And when the preference degree of this user for a certain category of books is similar to that of any other user except this user, this user can subsequently make book recommendations for this user by referring to the books in this category that the other user has read. Therefore, by combining the recommendation degree of each user for each category of books and the similarity degree of the preference degrees of each user and every other user except this user for each category of books, the recommendation degree of each user referring to every other user for each category of books is obtained.

[0063] In the embodiments of the present invention, the preference degrees of each user for each category of books are sorted in the order of the same book category to obtain the book preference degree sequence of each user; the DTW algorithm is used to obtain the book preference degree sequence of the th user and the similarity with the book preference degree sequence of the th user except the th user as the preference similarity degree between the th user and the th user except the

[0064] In the embodiments of the present invention, the recommendation degree of the th user referring to every other user for the th category of books is obtained:

[0065]

[0066] In the formula, represents the recommendation degree of the i-th user referring to the th other user for the th category of books; represents the preference degree of the th user for the th category of books; represents the preference degree of the th other user except the th user for the th category of books; represents the preference similarity degree between the th user and the th other user except the th user; represents the th user and the The maximum value among the similarity degrees of preferences of all users other than a user; The larger the value of , the more the -th user prefers to read -th type of books. Therefore, the -th type of books should be recommended to the -th user more; represents the absolute value of the difference between the preference degree of the -th user for the -th type of books and the preference degree of the -th user other than the -th user for the -th type of books. The smaller the value, the more similar the preference degrees of the -th user and the -th user other than the -th user; The smaller the value of , the more similar the preference degrees of the -th user and the -th user other than the -th user; When the value of is smaller, it means that the recommendation degree of the

[0067] S3: Obtain the recommendation degree of each user for each type of books according to the recommendation degree of each user referring to other users for each type of books, and perform book recommendations for each user based on the recommendation degree of each user for each type of books.

[0068] Step S3 includes steps S30 - S31, specifically as follows:

[0069] S30: Obtain the recommendation degree of each user for each type of books.

[0070] It should be noted that after obtaining the recommendation degree of each user referring to other users for each type of books, since there may be cases where the recommendation degrees are the same, in order to make the recommendation more accurate, we need to further consider the overall recommendation degree of each user for each type of books. When the maximum value among the recommendation degrees of a user referring to all other users for any type of books and the average value of the recommendation degrees of a user referring to each other user for this type of books are larger, it means that the recommendation degree of the user for this type of books is higher.

[0071] In the embodiment of the present invention, the recommendation degree of each user for each type of books is obtained:

[0072] ;

[0073] In the formula, represents the The recommendation degree of the category of books by a user; represents the maximum value among the recommendation degrees of the category of books by the user with reference to all other users; represents the average value of the recommendation degrees of the category of books by the user with reference to each other user; norm() represents the normalization function, adopting the linear normalization method, and the normalization object is the value of the category of books by the user; the larger the value, the greater the recommendation degree of the

[0074] S31: Perform book recommendations for each user based on the recommendation degree of each user for each category of books.

[0075] It should be noted that taking the user as an example, the user has a recommendation degree for each category of books. Since when performing book recommendations for users, a category of books that the user is interested in needs to be selected for recommendation, it is necessary to select the category of books with the highest recommendation degree for the user. And when recommending the category of books with the highest recommendation degree to the user, it is necessary to refer to the recommendation degree of the user with reference to each other user for this category of books, so as to recommend to the user based on the books of this category read by other users.

[0076] In the embodiment of the present invention, the process of performing book recommendations for the user is as follows: Obtain the maximum value among the recommendation degrees of the user for each category of books, and record the category of books corresponding to the maximum value as the category of books; Obtain the maximum value among the recommendation degrees of the user with reference to each other user for the category of books, and record the other users corresponding to the maximum value as the reference users. Screen out the books of the category of books read by the reference users and not read by the user, and recommend them to the user.

[0077] The beneficial effects of the present invention are as follows: according to the preference degrees of each user for each type of book, the recommendation degrees of each user for each type of book are obtained by referring to the recommendation degrees of each user for each type of book to every other user. Based on the recommendation degrees of each user for each type of book, book recommendations are made for each user, which can accurately reflect the current preferences of each user. Also, the recommendation degrees of other users with similar preferences to each user are considered, making the book recommendations for each user more accurate, capable of enhancing the reading interest of users, and thus effectively improving the reading ability. Further, the preference degrees of each user for each type of book are obtained according to the love degrees of each user for each type of book and the time interval between reading each type of book and the current time, which can accurately reflect the current preferences of users and facilitate subsequent recommendations. Further, the obtaining of the recommendation degrees of each user referring to the recommendation degrees of every other user for each type of book facilitates subsequent recommendations for one user by referring to the books read by another user in each type of book.

[0078] An embodiment of the present invention also discloses a hierarchical reading system for improving students' reading ability, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, a hierarchical reading method for improving students' reading ability according to the present invention is implemented.

[0079] The above system also includes other components well-known to those skilled in the art such as a communication bus and a communication interface. Their settings and functions are known in the art, so they will not be elaborated here.

[0080] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high-bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application program, a module, or both. Any such computer storage medium can be a part of the device or accessible or connectable to the device.

[0081] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

[0082] The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention shall be covered within the protection scope of the present invention.

Claims

1. A graded reading method for improving students' reading ability, characterized in that, Including the steps of: collecting the reading data of each user; Obtaining the love degree of each user for each book, where the love degree represents the degree to which the user likes to read the book; Obtaining the preference degree of each user for each category of books, where the preference degree is obtained by taking the reciprocal of the number of days between the time when the user last read each book in each category of books and the current time as the weight, and performing a weighted sum on the love degree of each user for each book in each category of books; Obtain the recommendation degree of each user for each type of book with reference to every other user ; In the formula, represents the recommendation degree of the -th user for the -th type of book with reference to every other -th user; represents the preference degree of the -th user for the -th type of book; represents the preference degree of the -th user (excluding the -th user) for the -th type of book; represents the similarity degree of preference between the -th user and the -th user (excluding the -th user); represents the maximum value among the similarity degrees of preference between the -th user and all other users (excluding the -th user); Based on the recommendation degrees of each user for each type of book with reference to the recommendation degrees of each other user, obtain the recommendation degrees of each user for each type of book. The calculation formula is: , represents the recommendation degree of the th user for the th type of book; represents the maximum value among the recommendation degrees of the th user with reference to all other users for the th type of book; represents the average value of the recommendation degrees of the th user with reference to each other user for the th type of book; represents the normalization function; Recommend books for each user based on the recommendation degree of each user for each type of book, including: obtaining the maximum value among the recommendation degrees of the th user for each type of book, and recording the type of book corresponding to the maximum value as the th type of book; obtaining the maximum value among the recommendation degrees of the th user referring to each other user's recommendation degree for the th type of book, and recording the other user corresponding to the maximum value as the reference user, screening out the books that the reference user has read and the th type of book that the th user has not read, and recommending them to the th user.

2. The hierarchical reading method for improving students' reading ability according to claim 1, wherein The obtaining of the love degree of each user for each book includes the steps of: Obtaining the reading enthusiasm of each user for each book every day; Taking the maximum value of the reading enthusiasm of each user for each book in all days as the love degree of each user for each book.

3. A graded reading method for improving students' reading ability according to claim 2, characterized in that, The formula for obtaining the reading enthusiasm of each user for each book every day is: ； Wherein, represents the reading enthusiasm of the th user for the th book on the th day; represents the single-day browsing times of the th user for the th book on the th day; represents the th browsing duration of the th user for the th book on the th time; represents a normalization function.

4. A graded reading method for improving students' reading ability according to claim 1, characterized in that, The formula for obtaining the preference degree of each user for each category of books is: ； In the formula, represents the th user's preference for the th type of book; represents the number of books in the th type of book; represents the th user's love for the th book in the th type of book; represents the number of days between the time when the th user last read the th book in the th type of book and the current time.

5. A graded reading method for improving students' reading ability according to claim 1, characterized in that, The obtaining of the preference similarity includes: Sort the preference levels of each user for each category of books in the order of the same book category to obtain the book preference level sequence of each user; Use the DTW algorithm to obtain the book preference level sequence of the th user and the similarity between the book preference level sequences of the th user except the th user as the preference similarity degree between the th user and the th user except the th user.

6. A graded reading system for improving students' reading ability, characterized in that, Including: A processor and a memory, where the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a hierarchical reading method for improving students' reading ability according to any one of claims 1-5 is implemented.

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