CN110334204A - A kind of exercise similarity calculation recommended method based on user record - Google Patents
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Abstract
The exercise similarity calculation recommended method based on user record that the invention discloses a kind of, the present invention is by utilizing item2vec thought and convolutional neural networks respectively advantage, the two is effectively combined, it solves in current exercise recommendation since topic includes that a large amount of formal notation content structures are complicated, the problem of semantic complications are to be difficult to match similar topic type, and can segment exercise in the angle of natural language processing, learn the specific grammer meaning of exercise, similar topic type is matched on the meaning of a word.It finally allows exercise recommender system preferably to recommend more matched similar topic type, promotes exercise and recommend matter.
Description
The technical field is as follows:
the invention belongs to the field of software, and particularly relates to a exercise similarity calculation recommendation method based on user records.
Background art:
the most common algorithms TIIDF, LSA, LDA and the like for detecting text similarity based on a machine learning mode can obtain certain accuracy under the condition that comparison of data formats and cleaning is in place, but the algorithms are only similar on weak semanteme, so the effect is not very good in practical recommendation use, recommended subjects are basically very similar (belong to subjects with one meaning), how to improve the understanding of the algorithms on the semantic level is very important for really obtaining the correlation on the semanteme of the problem, the algorithm based on deep learning is used by a plurality of scenes, the models based on LSTM and CNN can learn and express the semanteme of sentences to a certain extent, therefore, the effect of matching text similarity by using the algorithm based on deep learning is better than that of the traditional machine learning method, but the problem is very different from the original text sentence, the meaning of the sentence is more zigzag and various texts are mixed (mathematical symbols, formulas and the like), and the texts are basically filtered out on the model, so that the matching accuracy of the sentence is greatly reduced. The existing deep learning-based approach also has difficulty in achieving satisfactory results.
The noun explains:
word2 vec: the word embedding model proposed in the year 2013 of Google is actually a shallow neural network model, and has two network structures, namely CBOW and Skip-gram. The method mainly uses a network model of word2vec as Skip-gram.
item2 vec: the method of word2vec is mainly used in a recommendation system, commodity items are used as words in the word2vec, and a commodity item set purchased by a user at one time is used as sentences in the word2 vec.
skip-gram network model: the context word is obtained by inferring a context through a target word, namely inputting the target word.
softmax: to normalize the exponential function, the output of a plurality of neurons is mapped into the (0,1) interval, which can be considered as a probability.
Cross entropy: the difference distance between the actual output and the expected output is mainly calculated as a loss function of the model. I.e., the smaller the cross-entropy value, the closer the actual output and the desired output.
One exercise is a set of exercises that the customer does in a certain period of time or a set number of exercises that the customer does at one time.
The same type of problem: that is, two problems are the same form of problem under one subject or one knowledge point.
The invention content is as follows:
the invention discloses a exercise similarity calculation recommendation method based on user records; the problem that the problem of inaccurate exercise recommendation is caused by improper similarity calculation due to the complex structure of exercise content can be well solved, and the exercise recommendation accuracy is effectively improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for calculating and recommending exercise similarity based on user records comprises the following steps:
step one, taking each exercise as a sentence to perform word segmentation processing to obtain word embedding vectors of the segmented words in the exercise, connecting the word embedding vectors of all words in each exercise into a matrix according to the sequence of the words appearing in the exercise to obtain an exercise matrix representing exercise information, and processing the exercise matrix by using a convolutional neural network model: the convolutional neural network model adopts filters with different sizes to carry out convolution to obtain a plurality of output characteristics, and the results of the output characteristics are subjected to pooling processing and spliced into a vector 1;
step two, taking the exercises as a whole, and calculating the similarity between the exercises: taking the exercises as a word, and taking a set of the exercises which are once done by each user as a sentence; calculating the probability of two exercises appearing in the same exercise set at the same time as the similarity of the two exercises; finally, obtaining an embedded vector of each problem, namely a vector 2;
step three, splicing the vector1 and the vector2 to obtain a final vector, and training through the vector to obtain a trained model;
step four, inputting the latest exercise made by the user into the trained model, outputting a result as a recommendation probability that the probability that all exercises made by the user are in the same category corresponding to the exercises in the exercise library, sequencing the probabilities of all exercises in the result, and selecting a exercise which has the maximum recommendation probability and has not been made by the user to display to the user to complete a recommendation task; a is the set exercise recommendation number.
In a further improvement, the first step comprises the following steps:
step one, segmenting each exercise by using a third-party library jieba Chinese segmentation component, training the obtained segmentation by using a skip-gram network model of word2vec, mapping each word in the exercise into a d-dimensional word vector, and connecting the word vectors of all the segments in each exercise according to the semantic sequence in the exercise to obtain a representative exercise matrix; taking the exercise with the maximum number of words, wherein the number of words is n; processing each problem into an n-d matrix, and performing 0 complementing operation on the problems with the word number less than n to ensure that the dimensionality of input data is consistent; learning a problem matrix by using a convolution model, setting three sizes of 2 x d, 3 x d and 5 x d, performing convolution operation on each size by using three filters, and performing maximum pooling operation on output features; the results of the nine output features processed are spliced into a vector1 containing the semantic information of the problem.
In a further improvement, the second step includes the following steps:
obtaining an embedded vector of each problem by using a skip-gram network model: firstly, taking exercises done in one exercise of a user as a set, and setting the number of the exercises done in one exercise of the user as S, wherein the exercises are respectively W1, W2 and W3 … … WS; selecting a current target problem Wi, and outputting other problems co-occurring with the current target problem Wi in a problem set by using a skip-gram network model, namely a positive sample; the model is trained such that the conditional probability of the co-occurrence of the target problem Wi with every other problem in the user's one exercise is maximized in all problem sets, i.e.Maximum;
wherein,
wherein u isiIs the vector of the target problem Wi, vjIs a problem that appears in the collection simultaneously with the target problem Wi
The vector of (a); i represents a question bank containing all the questions; k represents the problem of the input question bank; wj represents in use
The problem in the problem which is practised by the user at one time is different from the target problem Wi;
a negative sampling method is applied, namely a plurality of exercises which are not in the same set with the target exercise Wi, namely negative samples are randomly extracted to optimize output, and the training calculated amount of the model is reduced; finally, the embedded vector expression of the problem itself is obtained: vector 2.
In a further improvement, the third step includes the following steps:
splicing the vector1 and the vector2 to obtain a final vector, inputting the vector into a fully-connected neural network, and then performing learning training through the final vector: and taking the same type of exercises as a training set, taking a plurality of training sets as a training set, inputting the target exercises, wherein the exercises expected to be output are other exercises belonging to the same type of exercises as the target exercises, so that the probability of outputting the other exercises which are the same type as the previous target exercises is the maximum, and the calculated probability of the other exercises which are not the same type as the current target exercises is the minimum, thereby obtaining the trained model.
Further improvement, a negative sampling method is adopted in the training process to accelerate the training, namely, for the input of a target exercise, e exercises which are not in the same set with the target exercise, namely negative samples, are randomly extracted to optimize the updating process of the parameters, the calculated amount is reduced, and the training speed of the network is accelerated.
The invention has the beneficial effects that: the problem that the problem of inaccurate exercise recommendation is caused by improper similarity calculation due to the complex structure of exercise content can be well solved, and the exercise recommendation accuracy is effectively improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a schematic flow chart of step one.
Fig. 2 is a schematic of the final flow of the present invention.
The specific implementation mode is as follows:
the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are for purposes of illustration and explanation, and are not intended to limit the invention.
Example 1
The specific steps of the invention are shown in fig. 1 and fig. 2:
1) firstly, segmenting each exercise by adopting Chinese segmentation in a third-party library jieba, and training the obtained segmentation by using a skip-gram network model of word2vec so as to map each word into a d-dimensional word vector. And connecting the word vectors of all the participles of the problem to obtain a matrix representing the problem. And taking the exercise with the maximum word number, wherein the word number is n. And processing each problem into an n-d matrix, and keeping the dimension of input by complementing 0 for the problem with the word number less than n. All the problems are finally represented as n x d matrices. And then learning the problem matrix by using a convolution neural network, setting three filters with the sizes of 2 x d, 3 x d and 5 x d for convolution operation, and outputting the maximum value of the output characteristics by using a maximum pooling operation. The results of processing the nine output features are spliced into a vector1 containing the semantic information of the problem.
2) Taking the exercises as a whole, trying to obtain an embedded vector of each exercise by using a skip-gram network model through the idea of item2vec, taking the exercises which are done by the user once as a set, and setting the number of the exercises which are done by the user this time as S and the exercises as W1, W2 and W3 … … WS. We select current target problem Wi, then the other problems that require the network output of the skip-gram to co-occur with the current target problem in one set, namely positive samples, while the problems that do not occur in one set are negative samples. The model is trained such that the conditional probability of two co-occurring problems in a set of problems is maximized. The corresponding objective function of the model is as follows:
where p (Wj | Wi) is a softmax function:
wherein u isiIs the vector of the target problem Wi, vjIs a vector of problems that appear in the collection concurrently with target problem Wi; i represents the question bank containing all the exercises(ii) a k represents the problem of the input question bank;
and (2) a negative sampling method is applied, namely a plurality of problems which are not in the current set, namely negative samples, are randomly extracted to optimize output, and only a small number of parameters need to be updated each time to accelerate training, so that the embedded vector expression of the problems is finally obtained: vector 2.
3) Splicing the vector1 and the vector2 to obtain a final vector, inputting the vector into a fully-connected neural network, and then performing learning training: inputting a target problem, wherein the problem expected to be output is other problems belonging to a same category as the target problem, specifically, inputting a target problem vector, and obtaining the probability that each problem in the problem library is the problem of the same type as the current problem after normalization through a multilayer neural network and a softmax function, wherein the fitting target of the model is to maximize the calculated probability of other problems of the same type as the current target problem and minimize the calculated probability of other problems of the same type as the current target problem. After training is finished, the model can calculate the probability that other problems in the problem base and the problem are the same type according to the target problem vector, namely the probability of recommending the problems.
The problem quantity is great, if the training mode of normal sampling needs a large amount of calculation and time, the output optimization is carried out by adopting the thought of negative sampling, the specific measure is that a plurality of negative samples (generally the quantity is set to be 3-7) are randomly selected for a target problem, the training is carried out by adopting the form of cross entropy, thereby the training of the model is completed, and the training calculation cost and the training time are saved compared with the training of the total quantity of problems.
4) And (3) inputting the model trained in the step (3) into a problem sample made by the user, outputting a result as the probability that all problems are recommended to the problem, sequencing the probabilities of all problems in the result according to the probability representative and the probability that the problems belong to the same category, and selecting the largest problem which is not made by the user to display to the user to complete a recommendation task.
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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A method for calculating and recommending exercise similarity based on user records is characterized by comprising the following steps:
step one, taking each exercise as a sentence to perform word segmentation processing to obtain word embedding vectors of the segmented words in the exercise, connecting the word embedding vectors of all words in each exercise into a matrix according to the sequence of the words appearing in the exercise to obtain an exercise matrix representing exercise information, and processing the exercise matrix by using a convolutional neural network model: the convolutional neural network model adopts filters with different sizes to carry out convolution to obtain a plurality of output characteristics, and the results of the output characteristics are subjected to pooling processing and spliced into a vector 1;
step two, taking the exercises as a whole, and calculating the similarity between the exercises: taking the exercises as a word, and taking a set of the exercises which are once done by each user as a sentence; calculating the probability of two exercises appearing in the same exercise set at the same time as the similarity of the two exercises; finally, obtaining an embedded vector of each problem, namely a vector 2;
step three, splicing the vector1 and the vector2 to obtain a final vector, and training through the vector to obtain a trained model;
step four, inputting the latest exercise made by the user into the trained model, outputting a result as a recommendation probability that the probability that all exercises made by the user are in the same category corresponding to the exercises in the exercise library, sequencing the probabilities of all exercises in the result, and selecting a exercise which has the maximum recommendation probability and has not been made by the user to display to the user to complete a recommendation task; a is the set exercise recommendation number.
2. The method of claim 1, wherein the step one comprises the steps of:
step one, segmenting each exercise by using a third-party library jieba Chinese segmentation component, training the obtained segmentation by using a skip-gram network model of word2vec, mapping each word in the exercise into a d-dimensional word vector, and connecting the word vectors of all the segments in each exercise according to the semantic sequence in the exercise to obtain a representative exercise matrix; taking the exercise with the maximum number of words, wherein the number of words is n; processing each problem into an n-d matrix, and performing 0 complementing operation on the problems with the word number less than n to ensure that the dimensionality of input data is consistent; learning a problem matrix by using a convolution model, setting three sizes of 2 x d, 3 x d and 5 x d, performing convolution operation on each size by using three filters, and performing maximum pooling operation on output features; the results of the nine output features processed are spliced into a vector1 containing the semantic information of the problem.
3. The exercise similarity calculation recommendation method based on user records of claim 2, wherein the second step comprises the steps of:
obtaining an embedded vector of each problem by using a skip-gram network model: firstly, taking exercises done in one exercise of a user as a set, and setting the number of the exercises done in one exercise of the user as S, wherein the exercises are respectively W1, W2 and W3 … … WS; selecting a current target problem Wi, and outputting other problems co-occurring with the current target problem Wi in a problem set by using a skip-gram network model, namely a positive sample; the model is trained such that the conditional probability of the co-occurrence of the target problem Wi with every other problem in the user's one exercise is maximized in all problem sets, i.e.Maximum;
wherein,
wherein u isiIs the vector of the target problem Wi, vjIs a vector of problems that appear in the collection concurrently with target problem Wi; i represents an inclusionA question bank with exercises; k represents the problem of the input question bank; wj represents a problem different from the target problem Wi in the problem that the user exercises at one time;
a negative sampling method is applied, namely a plurality of exercises which are not in the same set with the target exercise Wi, namely negative samples are randomly extracted to optimize output, and the training calculated amount of the model is reduced; finally, the embedded vector expression of the problem itself is obtained: vector 2.
4. The exercise similarity calculation recommendation method based on user records of claim 3, wherein the third step comprises the steps of:
splicing the vector1 and the vector2 to obtain a final vector, inputting the vector into a fully-connected neural network, and then performing learning training through the final vector: and taking the same type of exercises as a training set, taking a plurality of training sets as a training set, inputting the target exercises, wherein the exercises expected to be output are other exercises belonging to the same type of exercises as the target exercises, so that the probability of outputting the other exercises which are the same type as the previous target exercises is the maximum, and the calculated probability of the other exercises which are not the same type as the current target exercises is the minimum, thereby obtaining the trained model.
5. The method as claimed in claim 4, wherein the training process is accelerated by using a negative sampling method, that is, for the input of a target problem, e problems that do not appear in the same set as the target problem, that is, negative samples, are randomly extracted to optimize the parameter updating process, so as to reduce the amount of computation and accelerate the training speed of the network.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111143604A (en) * | 2019-12-25 | 2020-05-12 | 腾讯音乐娱乐科技（深圳）有限公司 | Audio similarity matching method and device and storage medium |
| CN117688248A (en) * | 2024-02-01 | 2024-03-12 | 安徽教育网络出版有限公司 | Online course recommendation method and system based on convolutional neural network |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107832453A (en) * | 2017-11-24 | 2018-03-23 | 重庆科技学院 | Virtual test paper recommendation method oriented to personalized learning scheme |
| US20180218429A1 (en) * | 2017-01-31 | 2018-08-02 | Wal-Mart Stores, Inc. | Systems and methods for utilizing a convolutional neural network architecture for visual product recommendations |
| CN109271401A (en) * | 2018-09-26 | 2019-01-25 | 杭州大拿科技股份有限公司 | Method, apparatus, electronic equipment and storage medium are corrected in a kind of search of topic |
| CN109299380A (en) * | 2018-10-30 | 2019-02-01 | 浙江工商大学 | Exercise personalized recommendation method in online education platform based on multidimensional characteristic |
| CN109635100A (en) * | 2018-12-24 | 2019-04-16 | 上海仁静信息技术有限公司 | A kind of recommended method, device, electronic equipment and the storage medium of similar topic |
-
2019
- 2019-05-27 CN CN201910444120.3A patent/CN110334204B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180218429A1 (en) * | 2017-01-31 | 2018-08-02 | Wal-Mart Stores, Inc. | Systems and methods for utilizing a convolutional neural network architecture for visual product recommendations |
| CN107832453A (en) * | 2017-11-24 | 2018-03-23 | 重庆科技学院 | Virtual test paper recommendation method oriented to personalized learning scheme |
| CN109271401A (en) * | 2018-09-26 | 2019-01-25 | 杭州大拿科技股份有限公司 | Method, apparatus, electronic equipment and storage medium are corrected in a kind of search of topic |
| CN109299380A (en) * | 2018-10-30 | 2019-02-01 | 浙江工商大学 | Exercise personalized recommendation method in online education platform based on multidimensional characteristic |
| CN109635100A (en) * | 2018-12-24 | 2019-04-16 | 上海仁静信息技术有限公司 | A kind of recommended method, device, electronic equipment and the storage medium of similar topic |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111143604A (en) * | 2019-12-25 | 2020-05-12 | 腾讯音乐娱乐科技（深圳）有限公司 | Audio similarity matching method and device and storage medium |
| CN111143604B (en) * | 2019-12-25 | 2024-02-02 | 腾讯音乐娱乐科技（深圳）有限公司 | Similarity matching method and device for audio frequency and storage medium |
| CN117688248A (en) * | 2024-02-01 | 2024-03-12 | 安徽教育网络出版有限公司 | Online course recommendation method and system based on convolutional neural network |
| CN117688248B (en) * | 2024-02-01 | 2024-04-26 | 安徽教育网络出版有限公司 | Online course recommendation method and system based on convolutional neural network |
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