CN115146162A - Online course recommendation method and system - Google Patents
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Abstract
The invention discloses an online course recommendation method and system, wherein the method comprises the following steps: acquiring historical learning data of a user and preprocessing the historical learning data; respectively extracting knowledge from the preprocessed historical learning data to form a historical course sequence; encoding the historical course sequence through an encoder to generate a historical course vector and a score; constructing a mixed neural network model based on an attention mechanism, and training the mixed neural network model based on the attention mechanism through historical course vectors and scores to obtain a course prediction model; and carrying out course grading prediction and recommendation through a course prediction model. The method and the system convert the course recommendation problem into the score prediction problem, and can learn the historical learning behavior characteristics of the user through the deep learning model, so that the recommendation accuracy is improved.
Description
Technical Field
The invention belongs to the technical field of information recommendation, and particularly relates to an online course recommendation method and system.
Background
With the rapid development of online education, course learning through an online education platform becomes the first choice for various course training. When course recommendation is performed by the existing online education platform, course recommendation is performed randomly or only according to browsing records of a user, so that the superior courses of the online education platform cannot be recommended to the user for learning.
Online course recommendation methods generally include historical data-based recommendation methods and content-based recommendation methods. The recommendation method based on the historical data is used for directly recommending courses to the user by filtering and processing the historical data; the content-based recommendation method stores the characteristics of a specific user in a corresponding data set by methods of observing, testing, collecting data and the like of the user, and then recommends courses for the user by a domain method and a modeling method. The two recommendation methods are good and bad respectively, but if the two recommendation methods are combined for use, the problems of large data size, difficulty in processing and the like exist, and the recommendation effect is poor.
Disclosure of Invention
In view of this, the invention provides an online course recommendation method and system, which are used for solving the problem of low accuracy of online course recommendation.
In a first aspect of the present invention, an online course recommendation method is provided, where the method includes:
acquiring historical learning data of a user and preprocessing the historical learning data;
respectively extracting knowledge from the preprocessed historical learning data to form a historical course sequence;
encoding the historical course sequence through an encoder to generate a historical course vector and a score;
constructing a mixed neural network model based on an attention mechanism, and training the mixed neural network model based on the attention mechanism through historical course vectors and scores to obtain a course prediction model;
and carrying out course score prediction and recommendation through the course prediction model.
On the basis of the technical scheme, preferably, the historical learning data of the user comprises historical course learning records of the user on the online education platform, and the historical course learning records comprise course IDs, course names, course introduction, chapter catalogues, teacher IDs for teaching, learning completeness and course evaluation.
On the basis of the above technical solution, preferably, the respectively extracting knowledge from the preprocessed historical learning data to form a historical course sequence specifically includes:
sequencing the historical learning data according to the sequence of the learning time;
extracting key knowledge points in the course introduction and chapter catalogues in the historical learning data sequentially through the TF-IDF technology;
and (4) forming a historical course sequence by the course ID, the course name, the key knowledge point, the teacher ID, the learning completion degree and the course score of each course according to the sequence of the learning time.
On the basis of the above technical solution, preferably, the encoder uses One-Hot encoding.
On the basis of the above technical solution, preferably, the attention-based hybrid neural network model includes a convolutional neural network unit, a feature fusion unit, a long-term and short-term memory network unit, an attention unit, and an output layer, which are connected in sequence;
the convolutional neural network unit comprises an input layer convolutional layer, a pooling layer and a full-connection layer, processes a historical course sequence input by the network, performs convolutional operation, acquires spatial characteristics and inputs the spatial characteristics obtained by fusion into the characteristic fusion unit;
the feature fusion unit comprises a Concatenate layer and is used for performing feature fusion on vectors input by the input layer and spatial features output by the convolutional neural network unit and inputting the fusion features into the long-term and short-term memory network unit;
the long and short term memory network unit comprises an LSTM layer, a Dropout layer and a full connection layer and is used for acquiring space-time characteristics according to the fusion characteristics acquired by the characteristic fusion unit;
the attention unit is used for calculating the weight of the features acquired by the convolutional neural network unit and the long-term and short-term memory network unit, acquiring the complete feature representation of the sequence, and calculating a prediction value according to the complete feature representation.
On the basis of the technical scheme, preferably, the historical course vector comprises a vector sequence consisting of a course ID, a course name, a key knowledge point, a teacher ID and a learning completion degree after each course is coded; the grading is the course evaluation after each course is coded;
in the process of training the attention mechanism-based hybrid neural network model through the historical course vectors and the scores, the attention mechanism-based hybrid neural network model takes the historical course vectors as input and the scores as output.
On the basis of the above technical solution, preferably, the predicting and recommending the course score by the course prediction model specifically includes:
forming a candidate course set from courses relevant to the key knowledge points screened by the online education platform;
predicting the grade of each course in the candidate course set through the course prediction model, and taking the courses with the grade larger than the preset forecast to form a course recommendation set;
analyzing key knowledge points in the course recommendation set, matching corresponding textbooks, sequencing courses in the course recommendation set according to the learning sequence of the key knowledge points in the textbooks, and generating a recommendation list;
and recommending courses for the user according to the recommendation list.
In a second aspect of the present invention, an online course recommendation system is provided, where the system includes:
a data acquisition module: the system is used for acquiring historical learning data of a user and preprocessing the historical learning data;
a vector generation module: the system is used for extracting knowledge from the preprocessed historical learning data respectively to form a historical course sequence; encoding the historical course sequence through an encoder to generate a historical course vector and a score;
a model construction module: the system comprises a learning system, a learning system and a learning system, wherein the learning system is used for constructing a mixed neural network model based on an attention mechanism, and training the mixed neural network model based on the attention mechanism through historical course vectors and scores to obtain a course prediction model;
the course recommending module: the method is used for carrying out course scoring prediction and recommendation through a course prediction model.
In a third aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor which are invoked to implement the method according to the first aspect of the invention.
In a fourth aspect of the invention, a computer-readable storage medium is disclosed, which stores computer instructions that cause a computer to implement a method according to the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) The method comprises the steps of processing and encoding historical learning data of a user to generate historical course vectors and scores, training a hybrid neural network model based on an attention mechanism through the historical course vectors and the scores to obtain a course prediction model, and performing course score prediction through the course prediction model to generate a course recommendation list; the method converts the course recommendation problem into the scoring prediction problem, can learn the historical learning behavior characteristics and the key knowledge point characteristics of the user through the deep learning model, and simultaneously gives consideration to both the recommendation based on historical data and the recommendation based on content, thereby improving the recommendation accuracy;
2) The mixed neural network model based on the attention mechanism is established, the convolutional neural network long-short term memory network unit and the attention mechanism are fused, the time characteristics and the space characteristics of historical learning data can be extracted, long-term dependence between the data is captured, the key characteristics of the sequence are extracted through the attention mechanism, and the prediction precision is improved;
3) Before the recommendation list is generated, the key knowledge points in the course recommendation set are analyzed, the corresponding textbook is matched, the courses in the course recommendation set are sequenced according to the learning sequence of the key knowledge points in the textbook, and the problem that the recommendation result is not ideal as a learner needs to master the leading course before starting learning a new course is avoided.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an online course recommendation method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, the present invention provides an online course recommendation method, including:
s1, acquiring historical learning data of a user and preprocessing the historical learning data;
specifically, historical learning data of the user is obtained from the online education platform, the historical learning data of the user is historical course learning records of the user on the online education platform, and the historical course learning records include a course ID, a course name, a course introduction, a chapter directory, a teacher ID, learning completeness, course evaluation and the like.
S2, respectively extracting knowledge from the preprocessed historical learning data to form a historical course sequence;
the step S2 specifically comprises the following sub-steps:
s21, sequencing the historical learning data according to the sequence of the learning time;
s22, extracting key knowledge points in the course brief introduction and chapter directory in the historical learning data sequentially through the TF-IDF technology;
and S23, forming a historical course sequence by the course ID, the course name, the key knowledge point, the teacher ID, the learning completion degree and the course score of each course according to the sequence of the learning time.
The knowledge extraction mainly aims at the course introduction and the chapter catalog and aims at extracting key knowledge points of the courses learned by the user in time sequence so as to predict and screen the courses matched with the key knowledge points through the course prediction model in the following process.
S3, encoding the historical course sequence through an encoder to generate a historical course vector and a score;
specifically, the encoder may employ One-Hot encoding. The One-Hot encoding is in a binary vector form, so that the relevant course data of the historical course sequence needs to be mapped into an integer value, for example, each course corresponds to One course id, the course id needs to be represented as a binary vector, the element value in the vector with the index of id number is marked as 1, and other elements are all 0, for example, {0,0,0,1,0, …,0} represents the course with the course id of 4.
In the embodiment of the invention, the historical course vector comprises a vector sequence consisting of a course ID, a course name, a key knowledge point, a teacher ID and a learning completion degree after each course code; the score is the coded course evaluation for each course, and the course evaluation may be the user's score for the course.
S4, constructing a mixed neural network model based on an attention mechanism, and training the mixed neural network model based on the attention mechanism through historical course vectors and scores to obtain a course prediction model;
specifically, the hybrid neural network model based on the attention mechanism established by the invention comprises a convolutional neural network unit, a feature fusion unit, a long-term and short-term memory network unit, an attention unit and an output layer which are connected in sequence;
the convolutional neural network unit comprises an input layer convolutional layer, a pooling layer and a full-connection layer, processes a historical course sequence input by the network, performs convolutional operation, acquires spatial characteristics and inputs the spatial characteristics obtained by fusion into the characteristic fusion unit;
the feature fusion unit comprises a Concatenate layer and is used for performing feature fusion on vectors input by the input layer and spatial features output by the convolutional neural network unit and inputting the fusion features into the long-term and short-term memory network unit;
the long and short term memory network unit comprises an LSTM layer, a Dropout layer and a full connection layer and is used for acquiring space-time characteristics according to the fusion characteristics acquired by the characteristic fusion unit;
the attention unit is used for calculating the weight of the features acquired by the convolutional neural network unit and the long-term and short-term memory network unit, acquiring the complete feature representation of the sequence, and calculating a prediction value according to the complete feature representation.
The attention-based hybrid neural network model is trained through the historical course vectors and the scores, the historical course vectors are used as input, the scores are used as output, and the training process is completed.
The method establishes a hybrid neural network model based on the attention mechanism, integrates a long-term and short-term memory network unit of the convolutional neural network and the attention mechanism, can extract time characteristics and space characteristics of historical learning data, captures long-term dependence among the data, extracts key characteristics of a sequence through the attention mechanism, and improves prediction accuracy;
and S5, carrying out course grading prediction and recommendation through a course prediction model.
S51, screening courses relevant to the key knowledge points from the online education platform to form a candidate course set;
s52, predicting the grade of each course in the candidate course set through the course prediction model, and taking the courses with the grade larger than the preset grade to form a course recommendation set;
s53, analyzing key knowledge points in the course recommendation set, matching corresponding textbooks, sequencing courses in the course recommendation set according to the learning sequence of the key knowledge points in the textbooks, and generating a recommendation list;
and S54, recommending courses for the user according to the recommendation list.
Before the recommendation list is generated, the key knowledge points in the course recommendation set are analyzed, the corresponding textbook is matched, the courses in the course recommendation set are sequenced according to the learning sequence of the key knowledge points in the textbook, and the problem that the recommendation result is not ideal as a learner needs to master the leading course before starting learning a new course is avoided.
Corresponding to the above method embodiment, the present invention further provides an online course recommendation system, where the system includes:
a data acquisition module: the system is used for acquiring historical learning data of a user and preprocessing the historical learning data;
a vector generation module: the system is used for extracting knowledge from the preprocessed historical learning data respectively to form a historical course sequence; encoding the historical course sequence through an encoder to generate a historical course vector and a score;
a model construction module: for constructing a hybrid neural network model based on an attention mechanism, training a mixed neural network model based on an attention mechanism through historical course vectors and scores to obtain a course prediction model;
course recommending module: the method is used for carrying out course scoring prediction and recommendation through a course prediction model.
The above system embodiments and method embodiments are in one-to-one correspondence, and please refer to the method embodiments for brief description of the system embodiments.
The present invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor which invokes the method of the invention as described above.
The invention also discloses a computer readable storage medium which stores computer instructions for causing the computer to implement all or part of the steps of the method of the embodiment of the invention. The storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a read-only memory ROM, a random access memory RAM, a magnetic disk, or an optical disk.
The above-described system embodiments are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts shown as units may or may not be physical units, i.e. may be distributed over a plurality of network units. Without creative labor, a person skilled in the art can select some or all of the modules according to actual needs to achieve the purpose of the solution of the embodiment.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An online course recommendation method, the method comprising:
acquiring historical learning data of a user and preprocessing the historical learning data;
respectively extracting knowledge from the preprocessed historical learning data to form a historical course sequence;
encoding the historical course sequence through an encoder to generate a historical course vector and a score;
constructing a mixed neural network model based on an attention mechanism, and training the mixed neural network model based on the attention mechanism through historical course vectors and scores to obtain a course prediction model;
and carrying out course grading prediction and recommendation through a course prediction model.
2. The method of claim 1, wherein the historical learning data of the user comprises historical learning records of the user on the online education platform, and the historical learning records comprise a course ID, a course name, a course introduction, a chapter directory, a teacher ID, a learning completion level and a course evaluation.
3. The method of claim 2, wherein the step of extracting knowledge from the preprocessed historical learning data to form a historical lesson sequence specifically comprises:
sequencing the historical learning data according to the sequence of the learning time;
extracting key knowledge points in the course introduction and chapter catalogues in the historical learning data sequentially through the TF-IDF technology;
and (4) forming a historical course sequence by the course ID, the course name, the key knowledge point, the teacher ID, the learning completion degree and the course score of each course according to the sequence of the learning time.
4. The online course recommendation method of claim 2, wherein said encoder employs One-Hot encoding.
5. The online course recommendation method according to claim 2, wherein the hybrid neural network model based on attention mechanism comprises a convolutional neural network unit, a feature fusion unit, a long-short term memory network unit, an attention unit and an output layer which are connected in sequence;
the convolutional neural network unit comprises an input layer convolutional layer, a pooling layer and a full-connection layer, processes a historical course sequence input by the network, performs convolutional operation, acquires spatial characteristics and inputs the spatial characteristics obtained by fusion into the characteristic fusion unit;
the feature fusion unit comprises a Concatenate layer and is used for performing feature fusion on vectors input by the input layer and spatial features output by the convolutional neural network unit and inputting the fusion features into the long-term and short-term memory network unit;
the long and short term memory network unit comprises an LSTM layer, a Dropout layer and a full connection layer and is used for acquiring space-time characteristics according to the fusion characteristics acquired by the characteristic fusion unit;
the attention unit is used for calculating the weight of the features acquired by the convolutional neural network unit and the long-term and short-term memory network unit, acquiring the complete feature representation of the sequence, and calculating a prediction value according to the complete feature representation.
6. The online course recommendation method of claim 2, wherein said historical course vector comprises a vector sequence consisting of course ID, course name, key knowledge point, teacher ID and learning completion level after each course code; the grading is the course evaluation after each course is coded;
in the process of training the attention mechanism-based hybrid neural network model through the historical course vectors and the scores, the attention mechanism-based hybrid neural network model takes the historical course vectors as input and the scores as output.
7. The online course recommendation method of claim 1, wherein said predicting and recommending course scores by a course prediction model specifically comprises:
forming a candidate course set from courses relevant to the key knowledge points screened by the online education platform;
predicting the grades of all courses in the candidate course set through the course prediction model, and taking the courses with the grades larger than the preset grade to form a course recommendation set;
analyzing the key knowledge points in the course recommendation set, matching the corresponding textbook, sequencing the courses in the course recommendation set according to the learning sequence of the key knowledge points in the textbook, and generating a recommendation list;
and recommending courses for the user according to the recommendation list.
8. An online course recommendation system, the system comprising:
a data acquisition module: the system is used for acquiring historical learning data of a user and preprocessing the historical learning data;
a vector generation module: the system is used for respectively extracting knowledge from the preprocessed historical learning data to form a historical course sequence; encoding the historical course sequence through an encoder to generate a historical course vector and a score;
a model construction module: the system comprises a learning system, a learning system and a learning system, wherein the learning system is used for constructing a mixed neural network model based on an attention mechanism, and training the mixed neural network model based on the attention mechanism through historical course vectors and scores to obtain a course prediction model;
course recommending module: the method is used for carrying out course scoring prediction and recommendation through a course prediction model.
9. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, the processor invoking the program instructions to implement the method of any one of claims 1-7.
10. A computer-readable storage medium, characterized in that it stores computer instructions which cause a computer to implement the method of any one of claims 1 to 7.
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