Go course recommendation method and device, electronic equipment and computer storage medium

Abstract

The invention discloses a Go course recommendation method and device, electronic equipment and a computer storage medium. The method comprises the steps of obtaining a plurality of Go level test questions corresponding to different question types and different difficulty levels; obtaining test result information of answering the plurality of go grade test questions by the user; determining a plurality of characteristic values according to the test result information; inputting the plurality of feature values into a course matching model, and determining the matching degree between the plurality of alternative go courses and the user through the course matching model; and according to the matching degree, determining a target Go course recommended to the user from the plurality of alternative Go courses. According to the method and the device, a plurality of go grade test questions corresponding to different question types and different difficulty grades are pushed to the user, so that the problem of simplification of the test questions can be avoided, and the accuracy of the target go course recommended to the user is improved. The matching degree between the alternative go courses and the user is determined through the course matching model, and the accuracy of the target go course recommended to the user is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of computer-assisted teaching, and in particular to a method, device, electronic device and computer storage medium for recommending a Go course. Background Art

[0002] In the era of digitalization, chess education and training have gradually moved from traditional offline teaching to online teaching, which has not only rapidly popularized chess education, but also reduced the cost of learning. The online course system is different from offline teaching, and it is highly standardized and modular. However, different users have different levels of chess skills, and their needs for courses are not the same. Therefore, how to better adapt to the personalized needs of learning so that users can quickly find courses that suit their level and characteristics has become a difficult problem.

[0003] At present, in order to match users with courses that suit their needs, the main method is to identify the user's chess level and defects through fixed-difficulty assessments, and then make judgments based on the human experience of educators. Finally, courses are matched to users based on the judgment results.

[0004] However, the evaluation method with fixed difficulty is not flexible enough and has low accuracy. At the same time, due to the interference of human experience factors, the courses matched to users have low accuracy in matching their actual conditions. Summary of the invention

[0005] In view of this, the present application provides a Go course recommendation method, device, electronic device and computer storage medium to solve the problem that the courses matched by traditional solutions for users have low accuracy in matching the users' actual conditions.

[0006] In a first aspect, the present application provides a method for recommending a Go course, comprising: obtaining a plurality of Go level test questions corresponding to different question types and different difficulty levels, wherein the question types include at least two of life and death, layout, middle game, and fixed pattern; obtaining test result information of a user answering the plurality of Go level test questions; determining a plurality of feature values ​​according to the test result information; inputting the plurality of feature values ​​into a course matching model, and determining the matching degree between a plurality of alternative Go courses and the user through the course matching model; and determining a target Go course recommended to the user from the plurality of alternative Go courses according to the matching degree.

[0007] Optionally, the process of obtaining a plurality of Go level test questions corresponding to different question types and different difficulty levels may further include: for Go level test questions of different question types, executing: obtaining a first Go level test question according to a preset first difficulty level, determining a second difficulty level according to a first answer result of the user answering the first Go level test question, determining a second Go level test question according to the second difficulty level, determining an Nth difficulty level according to an N-1th answer result of the user answering the N-1th Go level test question, and determining an Nth Go level test question according to the Nth difficulty level, wherein N is a positive integer greater than 1; correspondingly,

[0008] [IQIDAO-TECH-2024-0007][HS2410863CCN]The process of obtaining the test result information of the user's answers to the multiple Go level test questions may also include: determining the test result information based on the 1st to Nth answer results of the user's answers to Go level test questions of different question types, and related information generated by the user in the process of answering the multiple Go level test questions.

[0009] Optionally, the relevant information generated by the user in the process of answering the multiple Go level test questions includes at least: the first difficulty level, answering time, number of questions not answered within the time limit, number of answer modifications, number of consecutive correct answers and number of consecutive incorrect answers.

[0010] Optionally, the process of recommending Go courses may also include: determining the accuracy of Go level test questions corresponding to different question types based on the test result information; if the accuracy of at least one question type of Go level test questions is less than the accuracy threshold, then determining a special Go course to be recommended to the user from the multiple alternative Go courses based on the question types of Go level test questions with an accuracy less than the accuracy threshold, and the difficulty level corresponding to the target Go course.

[0011] Optionally, different types of Go level test questions are answered the same number of times by the user.

[0012] Optionally, the multiple Go level test questions include multiple answer formats, and each type of Go level test question includes the same number of questions of each answer format.

[0013] A second aspect of the present application provides a Go course recommendation device, comprising: a first acquisition module, used to acquire multiple Go level test questions corresponding to different question types and different difficulty levels, wherein the question types include at least two of life and death, layout, middle game and fixed pattern; a second acquisition module, used to acquire test result information of a user's answers to the multiple Go level test questions; a determination module, used to determine multiple feature values ​​based on the test result information; a matching module, used to input the multiple feature values ​​into a course matching model, and determine the matching degree between multiple alternative Go courses and the user through the course matching model; a recommendation module, used to determine a target Go course recommended to the user from the multiple alternative Go courses based on the matching degree.

[0014] The third aspect of the present application provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform an operation corresponding to the method described in the first aspect of the embodiment.

[0015] A fourth aspect of the present application provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect of the embodiment.

[0016] In a fifth aspect, the present application provides a computer program product, including computer instructions, which instruct a computing device to perform operations corresponding to the method described in the first aspect of the embodiment.

[0017] This application can avoid the problem of monotony of test questions by pushing multiple Go level test questions corresponding to different question types and different difficulty levels to users, thereby improving the accuracy of the target Go courses recommended to users. By inputting multiple feature values ​​into the course matching model, the course matching model determines the matching degree of multiple alternative Go courses with users, which can further improve the accuracy of the target Go courses recommended to users. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only for reference.

[0019] [IQIDAO-TECH-2024-0007][HS2410863CCN] Some embodiments described in the application embodiments can also be used by ordinary technicians in this field to obtain other drawings based on these drawings.

[0020] Figure 1is a flowchart of the steps of a method for recommending a Go course in one embodiment of the present application;

[0021] Figure 2 is a flowchart of the steps of a method for determining a topic according to an embodiment of the present application;

[0022] Figure 3 is a schematic diagram of a Go course recommendation device according to an embodiment of the present application;

[0023] Figure 4 It is a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0025] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] The technical solutions in the embodiments of the present application are clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application. In the absence of conflict, the following embodiments and their technical features can be combined with each other.

[0028] After chess education and training went online, the identification of the user's chess level and defects was currently mainly completed by a fixed difficulty assessment combined with human judgment, and finally the user was matched with a suitable course. Such a method is not flexible enough and has low accuracy. It may also cause the course matched to the user to be inconsistent with the user's actual situation due to the interference of human experience factors, resulting in unnecessary waste. Therefore, the present application proposes a Go course recommendation method, device, electronic device and computer storage medium to at least partially solve the above problems.

[0029] Figure 1 1 is a flowchart of the steps of the Go course recommendation method according to an embodiment of the present application. Figure 1 As shown, the Go course recommendation method includes the following steps:

[0030] Step 101: Obtain a plurality of Go level test questions corresponding to different question types and different difficulty levels.

[0031] Step 102: Obtain test result information of the user's answers to multiple Go level test questions.

[0032] Step 103: Determine multiple characteristic values ​​according to the test result information.

[0033] [IQIDAO-TECH-2024-0007][HS2410863CCN]

[0034] Step 104: Input the multiple feature values ​​into a course matching model, and determine the matching degree between the multiple candidate Go courses and the user through the course matching model.

[0035] Step 105: Determine a target Go course recommended to the user from a plurality of candidate Go courses based on the matching degree.

[0036] At present, in order to push courses to users online, several test questions for testing the user's chess level are pushed to the user when the user uses it for the first time, and the user's chess level is determined according to the answer input by the user, and then courses are recommended to the user according to the determined chess level. However, the types of test questions recommended to users in this way are very single, and the process of determining the user's chess level according to the answer input by the user is not intelligent and has low accuracy.

[0037] In order to better recommend courses to users, we first need to obtain multiple Go level test questions corresponding to different question types and different difficulty levels. The question types include at least two of life and death, layout, middle game, set pattern and endgame. Life and death means making your own chess pieces alive or killing the opponent's chess pieces when playing chess. For example, the user needs to make two eyes to survive, or the user needs to prevent the AI ​​from making two eyes and "kill" the AI. Layout is the way to play Go at the beginning, which is generally controlled within dozens of moves. The user and AI compete for the large field in the corners and divide the approximate sphere of influence. The middle game is the contact battle between the user and the AI ​​chess pieces after the layout. The user can choose the specific application of basic techniques, such as the method of capturing pieces: double capture, capture, and shackle capture, etc. Set pattern is a conventional way of playing chess. The pieces are placed in a certain order, and the final gains and losses are equal. Generally, they occur in the corners. There are also a few large set patterns that will play half of the chessboard. Endgame is the stage after the layout and middle game, when the approximate territory of the user and AI and the life and death of the chess pieces have been confirmed, but there are still some boundaries to be competed. The way to obtain Go level test questions is to query from a database storing a large number of Go level test questions based on certain query conditions. The query conditions include but are not limited to the user's current difficulty level, the user's selected difficulty level, question type and other conditions.

[0038] Go level test questions can include not only different question types, but also questions with different answer formats, such as multiple-choice questions, response questions, and picture arrangement questions. The Go level test questions obtained include at least two answer formats of multiple-choice questions, response questions, and picture arrangement questions. The Go level test questions of each question type pushed to users contain the same number of questions of each answer format. By enriching the answer formats, the singleness of the answer mode can be changed and the user's interest in answering questions can be increased. At the same time, different answer formats can reflect the user's Go level from different angles. For example, if a user answers more multiple-choice questions correctly under the same question type and the same level, but has a lower accuracy rate for response questions, picture arrangement questions and other types of questions, it means that the user's proficiency at that level is not high enough. Therefore, by including multiple answer formats in multiple Go level test questions, the user's Go level can be further reflected.

[0039] After obtaining multiple Go level test questions, they will be pushed to the user, and the test result information of the user's answers to the multiple Go level test questions will be obtained. The test result information may include the user's answers to the multiple Go level test questions and the behavior information generated during the answering process. Then, multiple feature values ​​are determined based on the test result information.

[0040] Specifically, it is possible to control the number of times that Go level test questions of different types are answered by users to be the same, that is, the multiple Go level test questions obtained include the same number of each type of question. Under this setting, it is possible to ensure the balance of users' answers to each type of question, and avoid the problem of unsatisfactory answer results for users who are good at a single type of question.

[0041] After obtaining multiple eigenvalues, multiple eigenvalues ​​can also be standardized. For example, multiple eigenvalues ​​can be standardized by z-score standardization (zero-mean normalization) to convert the eigenvalues ​​into normal distribution.

[0042] [IQIDAO-TECH-2024-0007][HS2410863CCN] distribution form so that multiple eigenvalues ​​can be comparable, and then weights can be assigned to multiple eigenvalues.

[0043] Multiple feature values ​​are input into the course matching model, and the matching degree between multiple candidate Go courses and the user is determined through the course matching model. When multiple feature values ​​are input, weights assigned to multiple feature values ​​can also be input at the same time, thereby improving the accuracy of the course matching model. After obtaining the matching degree between multiple candidate Go courses and the user, the candidate Go course with the highest matching degree with the user can be selected as the target Go course recommended to the user.

[0044] In the embodiment of the present application, by pushing multiple Go level test questions corresponding to different question types and different difficulty levels to the user, the problem of single test questions can be avoided, thereby improving the accuracy of the target Go course recommended to the user. By inputting multiple feature values ​​into the course matching model, the course matching model is used to determine the matching degree between multiple candidate Go courses and the user, which can further improve the accuracy of the target Go course recommended to the user.

[0045] In a possible implementation, the process of obtaining a plurality of Go level test questions corresponding to different question types and different difficulty levels may further include: obtaining a first Go level test question according to a preset first difficulty level. Determining a second difficulty level according to a first answer result of a user answering the first Go level test question. Determining a second Go level test question according to the second difficulty level. Determining an Nth difficulty level according to an N-1th answer result of a user answering an N-1th Go level test question. Determining an Nth Go level test question according to the Nth difficulty level.

[0046] The first difficulty level set in advance can be a default difficulty level, such as level 1, or a self-assessed difficulty level input by the user. For example, if the user inputs a difficulty level of 5, the first difficulty level is 5. For the field of Go, the levels are 25-1, and then 1-5. In order to facilitate the determination of the level, Go level 25 can be set as level 1, and so on, Go level 5 is set as level 30. Each time a Go level test question is pushed to the user, the difficulty level of the next Go level test question will be updated according to the answer result entered by the user. The update logic can be that if the user answers correctly, the difficulty level is increased by 1, and if the answer is wrong, the difficulty level is reduced by 1. This is repeated N times to complete the test of the Go level test question of this question type. N can also be preset. For example, it can be set to complete the test after answering 10 questions of each question type, and N is 10 at this time.

[0047] Correspondingly, the process of obtaining test result information of a user's answers to multiple Go level test questions may also include: determining the test result information based on the 1st to Nth answer results of the user's answers to Go level test questions of different question types, and related information generated by the user in the process of answering multiple Go level test questions.

[0048] After the user completes the answering, the answer results of the user in the Go level test questions of different question types and corresponding to the questions of each difficulty level can be determined based on the 1st to Nth answer results. At this time, the answer results and the relevant information generated by the user in the process of answering multiple Go level test questions can be combined to determine the test result information.

[0049] The relevant information generated by the user in the process of answering multiple Go level test questions includes at least the first difficulty level, answering time, number of unanswered questions due to timeout, number of answer modifications, number of consecutive correct answers, and number of consecutive incorrect answers. For example, in order to more accurately obtain the user's actual answering situation, you can refer to the user's answering time. If the general answering time is 5 minutes, and the user's answering time exceeds 5 minutes, then even if the user answers the question correctly, the user still has difficulty answering the question. You can also refer to the number of unanswered questions due to timeout. This situation proves that the user may have some understanding of the knowledge points of the question, but failed to answer in time due to lack of proficiency or incomplete understanding. You can also refer to the number of answer modifications. The more modifications, the lower the user's grasp of the question. The number of consecutive correct answers and the number of consecutive incorrect answers prove that the user has a full grasp of the difficulty level of this part.

[0050] [IQIDAO-TECH-2024-0007][HS2410863CCN] or do not know at all. By referring to the relevant information generated by the user in the process of answering multiple Go level test questions, it is possible to more specifically understand whether the user is adapted to the corresponding difficulty level.

[0051] In an embodiment of the present application, by updating the answer questions in real time according to the user's answers during the answering process, the user can fully demonstrate his or her own Go level. In addition, by determining the test result information based on the 1st to Nth answer results of the user's Go level test questions of different question types, and the relevant information generated by the user in the process of answering multiple Go level test questions, the user's answer situation can be further understood to better determine the user's Go level.

[0052] Figure 2 is a flowchart of the steps of a Go course recommendation method according to another embodiment of the present application. Figure 2 As shown, the Go course recommendation method includes the following steps:

[0053] Step 201: Obtain a plurality of Go level test questions corresponding to different question types and different difficulty levels.

[0054] Step 202: Obtain test result information of the user's answers to multiple Go level test questions.

[0055] Step 203: Determine the accuracy of Go level test questions corresponding to different question types based on the test result information.

[0056] Step 204: If the accuracy rate of at least one type of Go level test question is less than the accuracy threshold, then determine a specialized Go course to be recommended to the user from a plurality of alternative Go courses based on the type of Go level test question with an accuracy rate less than the accuracy threshold and the difficulty level corresponding to the target Go course.

[0057] In addition to pushing target Go courses to users, supplementary courses can also be pushed to users. First, multiple Go level test questions corresponding to different question types and different difficulty levels need to be obtained, and the test result information of the user's answers to the multiple Go level test questions needs to be obtained. After obtaining the test result information, the accuracy of the Go level test questions corresponding to different question types can be determined. If the accuracy of the Go level test questions of at least one question type is less than the accuracy threshold, it means that the user has major deficiencies in the knowledge of the question type. It is necessary to determine the special Go course recommended to the user from multiple alternative Go courses based on the difficulty level corresponding to the question type and the target Go course.

[0058] Specifically, in the process of determining a specialized Go course recommended to the user from multiple alternative Go courses, the accuracy rates of Go level test questions of multiple question types can also be sorted according to the weights of different question types, and the specialized Go course recommended to the user can be determined from multiple alternative Go courses based on the accuracy rates of Go level test questions of question types with higher weights in the sequence being less than the accuracy threshold and the difficulty level corresponding to the target Go course.

[0059] It should be noted that the weights of different question types can be determined according to the course matching model. Specifically, the course matching model can be a decision tree model (decision tree mode). In the process of training the decision tree model, the feature values ​​extracted according to the sample set will be input into the pre-trained gradient boosting tree model XGBoost, and the confidence of multiple alternative Go courses will be used as output for training. After the training is completed, the characteristics of the decision tree model are used to obtain the influence weight of each question type on the prediction result (confidence) from the pre-trained gradient boosting tree model.

[0060] In the embodiment of the present application, specialized Go courses are recommended to users based on the accuracy rates of Go level test questions corresponding to different question types, and targeted course recommendations can be made when users have significant deficiencies in their knowledge of a certain question type.

[0061] Figure 3 is a schematic diagram of a Go course recommendation device according to an embodiment of the present application. Figure 3 The Go course recommendation device 300 includes: a first acquisition module 301 , a second acquisition module 302 , a determination module 303 , a matching module 304 and a recommendation module 305 .

[0062] The first acquisition module 301 is used to acquire a plurality of Go level test questions corresponding to different question types and different difficulty levels.

[0063] [IQIDAO-TECH-2024-0007][HS2410863CCN] item. The second acquisition module 302 is used to obtain the test result information of the user answering multiple Go level test questions. The determination module 303 is used to determine multiple feature values ​​according to the test result information. The matching module 304 is used to input the multiple feature values ​​into the course matching model, and determine the matching degree between multiple candidate Go courses and the user through the course matching model. The recommendation module 305 is used to determine the target Go course recommended to the user from the multiple candidate Go courses according to the matching degree.

[0064] In order to better recommend courses for users, it is first necessary to obtain multiple Go level test questions corresponding to different question types and different difficulty levels through the first acquisition module 301. The question types include at least two of life and death, layout, middle game, fixed pattern and official game. Life and death is to make your own chess alive or kill the opponent's chess when playing chess. For example, the user needs to make two eyes to survive, or the user needs to prevent the AI ​​from making two eyes and "kill" the AI. The layout is the way to go at the beginning of the Go game, which is generally controlled within dozens of moves. The user and the AI ​​compete for the big field at the corner and divide the approximate sphere of influence. The middle game is the contact battle between the user and the AI ​​chess pieces after the layout. The user can choose the specific application of basic techniques, such as the method of capturing pieces: double capture, capture, and shackle capture, etc. The fixed pattern is a conventional way of playing chess. The pieces are placed in a certain order, and the final gains and losses are equal. It usually occurs in the corner. There are also a few large fixed patterns that will go through half of the chessboard. The endgame is the stage after the layout and the middle game, when the approximate territory of the user and the AI ​​and the life and death of the chess pieces have been confirmed, but there are still some boundaries to be contested. The way to obtain the Go level test questions can be to query from a database storing a large number of Go level test questions based on certain query conditions, and the query conditions include but are not limited to the user's current difficulty level, the user's selected difficulty level, question type and other conditions.

[0065] After the first acquisition module 301 acquires multiple Go level test questions, it will be pushed to the user terminal 10, and the second acquisition module 302 acquires the test result information of the user answering the multiple Go level test questions input by the user terminal 10. The test result information may include the user's answers to the multiple Go level test questions and the behavior information generated during the answering process. Then the determination module 303 determines multiple feature values ​​according to the test result information.

[0066] After obtaining multiple eigenvalues, the multiple eigenvalues ​​may be standardized. For example, the multiple eigenvalues ​​may be standardized by z-score normalization (zero-mean normalization) to convert the eigenvalues ​​into a normal distribution so that the multiple eigenvalues ​​are comparable, and weights may be assigned to the multiple eigenvalues.

[0067] The matching module 304 inputs the multiple feature values ​​into the course matching model, and determines the matching degree between the multiple candidate Go courses and the user through the course matching model. When the multiple feature values ​​are input, the weights assigned to the multiple feature values ​​can also be input at the same time, thereby improving the accuracy of the course matching model. After the matching module 304 obtains the matching degree between the multiple candidate Go courses and the user, the recommendation module 305 can select the candidate Go course with the highest matching degree with the user as the target Go course recommended to the user.

[0068] In the embodiment of the present application, by pushing multiple Go level test questions corresponding to different question types and different difficulty levels to the user, the problem of single test questions can be avoided, thereby improving the accuracy of the target Go course recommended to the user. By inputting multiple feature values ​​into the course matching model, the course matching model is used to determine the matching degree between multiple candidate Go courses and the user, which can further improve the accuracy of the target Go course recommended to the user.

[0069] In this embodiment, an electronic device 400 is provided, such as Figure 4 As shown, the electronic device 400 may include: a processor 401, a communications interface 402, a memory 403, and a communication bus 404. Among them:

[0070] [IQIDAO-TECH-2024-0007][HS2410863CCN]

[0071] The processor 401 , the communication interface 402 , and the memory 403 communicate with each other via the communication bus 404 .

[0072] The communication interface 402 is used to communicate with other electronic devices or servers.

[0073] Processor 401 is used to execute program 405, and specifically can execute the relevant steps in the aforementioned Go course recommendation method embodiment.

[0074] Specifically, the program 405 may include program codes, which include computer operation instructions.

[0075] The processor 401 may be a CPU, or an application specific integrated circuit ASIC (Application Specific Integrated Circuit), or may be configured as one or more integrated circuits. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more ASICs.

[0076] The memory 403 is used to store the program 405. The memory 403 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0077] Program 405 can be specifically used to enable processor 401 to execute the Go course recommendation method in the aforementioned embodiment.

[0078] The specific implementation of each step in program 405 can refer to the corresponding description of the corresponding steps and units in the above-mentioned Go course recommendation method embodiment, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiment, which will not be repeated here.

[0079] The electronic device 400 of the embodiment of the present application can avoid the problem of uniform test questions by pushing multiple Go level test questions corresponding to different question types and different difficulty levels to the user, thereby improving the accuracy of the target Go course recommended to the user. By inputting multiple feature values ​​into the course matching model, the course matching model is used to determine the matching degree between multiple candidate Go courses and the user, which can further improve the accuracy of the target Go course recommended to the user.

[0080] In this embodiment, a computer-readable storage medium is provided, which stores instructions for causing a machine to execute the Go course recommendation method as described herein. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0081] In this case, the program code read from the storage medium itself can implement the functions in the above method embodiments, so the program code and the storage medium storing the program code constitute part of the present application.

[0082] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0083] In this embodiment, a computer program product is provided, including computer instructions, which instruct a computing device to perform operations corresponding to the above method embodiments.

[0084] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0085] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or can be implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or can be implemented in a computer program product.

[0086] [IQIDAO-TECH-2024-0007][HS2410863CCN] Computer code originally stored in a remote recording medium or a non-temporary machine-readable medium and to be stored in a local recording medium is now downloaded through the network, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware (such as an ASIC or FPGA). It is understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown here, the execution of the code converts the general-purpose computer into a special-purpose computer for executing the method shown here. Although the present application has been shown and described with respect to one or more implementations, those skilled in the art will think of equivalent deformations and modifications based on the reading and understanding of this specification and the accompanying drawings. The present application includes all such modifications and variations, and is limited only by the scope of the appended claims. In particular, with respect to the various functions performed by the above-described components, the terms used to describe such components are intended to correspond to any component that performs the specified function of the component (e.g., which is functionally equivalent) (unless otherwise indicated), even if not structurally equivalent to the disclosed structure that performs the function in the exemplary implementation of the present specification shown herein.

[0087] That is, the above description is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of the present application, such as the mutual combination of technical features between the embodiments, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

[0088] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0089] In order to enable any technician in the field to implement and use the application, the application provides the above description. In the above description, various details are listed for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the application can also be implemented without using these specific details. In other embodiments, the well-known process will not be elaborated in detail to avoid unnecessary details that make the description of the application obscure. Therefore, the application is not intended to be limited to the embodiments shown, but is consistent with the widest range of principles and features disclosed in the application.

[0090] It should be noted that, under the premise of no conflict, the various embodiments and / or the technical features in the various embodiments described in this application can be arbitrarily combined with each other, and the technical solution obtained after the combination should also fall within the protection scope of this application.

[0091] It should be understood that the specific examples in the embodiments of the present application are only to help those skilled in the art better understand the embodiments of the present application, rather than to limit the scope of the embodiments of the present application. Those skilled in the art can make various improvements and modifications based on the above embodiments, and these improvements or modifications all fall within the scope of protection of the present application. The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be based on the scope of protection of the claims.

Claims

1. A method for recommending a Go course, characterized in that: The method comprises: Acquire a plurality of Go level test questions corresponding to different question types and different difficulty levels, wherein the question types include at least two of life and death, layout, middle game, and fixed pattern; Obtaining test result information of the user's answers to the multiple Go level test questions; Determine a plurality of characteristic values ​​according to the test result information; Inputting the multiple feature values ​​into a course matching model, and determining the matching degree between multiple candidate Go courses and the user through the course matching model; According to the matching degree, a target Go course recommended to the user is determined from the multiple candidate Go courses.

2. The method according to claim 1, characterized in that The step of obtaining a plurality of Go level test questions corresponding to different question types and different difficulty levels includes: For different types of Go level test questions, the following are implemented: According to a preset first difficulty level, a first Go level test question is obtained, according to a first answer result of the user answering the first Go level test question, a second difficulty level is determined, according to the second difficulty level, a second Go level test question is determined, according to the N-1th answer result of the user answering the N-1th Go level test question, an Nth difficulty level is determined, and according to the Nth difficulty level, an Nth Go level test question is determined, wherein N is a positive integer greater than 1; Correspondingly, the step of obtaining the test result information of the user's answers to the multiple Go level test questions includes: The test result information is determined based on the 1st to Nth answer results of the user's Go level test questions of different question types and the relevant information generated by the user in the process of answering the multiple Go level test questions.

3. The method according to claim 2, characterized in that The relevant information generated by the user in the process of answering the multiple Go level test questions includes at least: the first difficulty level, answering time, number of questions not answered within the time limit, number of answer modifications, number of consecutive correct answers and number of consecutive incorrect answers.

4. The method according to claim 1, characterized in that: The method further comprises: Determine the correctness of Go level test questions corresponding to different question types according to the test result information; If the accuracy rate of at least one type of Go level test questions is less than the accuracy threshold, then based on the type of Go level test questions with the accuracy rate less than the accuracy threshold and the difficulty level corresponding to the target Go course, a special Go course recommended to the user is determined from the multiple alternative Go courses.

5. The method according to claim 1, characterized in that Different types of Go level test questions are answered the same number of times by the user.

6. The method according to claim 1, characterized in that The multiple Go level test questions include multiple answer formats, and each type of Go level test question includes the same number of questions in each answer format.

7. A Go course recommendation device, characterized in that: include: A first acquisition module is used to acquire a plurality of Go level test questions corresponding to different question types and different difficulty levels, wherein the question types include at least two of life and death, layout, middle game and fixed pattern; A second acquisition module is used to obtain test result information of the user's answers to the multiple Go level test questions; A determination module, used to determine a plurality of characteristic values ​​according to the test result information; A matching module, used for inputting the plurality of feature values ​​into a course matching model, and determining the matching degree between a plurality of candidate Go courses and the user through the course matching model; A recommendation module is used to determine a target Go course recommended to the user from the multiple candidate Go courses according to the matching degree.

8. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute an operation corresponding to the method according to any one of claims 1 to 6.

9. A computer storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the method according to any one of claims 1 to 6.

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