CN111506810A - Course information pushing method, system, equipment and storage medium - Google Patents
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Abstract
The invention provides a course information pushing method, a system, equipment and a storage medium, wherein the method comprises the following steps: receiving learning plan data of a user from a user terminal, the learning plan data including a planned learning time and a planned lesson category of the user; inquiring matched alternative courses and teacher information of each alternative course in a course information base according to the learning plan data; acquiring teacher preference data of a user from an evaluation database; calculating the matching degree of the teacher information of each alternative course and the teacher preference data; and sequencing the alternative courses according to the matching degree, and pushing the sequenced alternative courses and the corresponding teacher information to the user terminal. By adopting the scheme of the invention, the course information is pushed for the user in a targeted manner by combining the learning plan of the user and the preference of the teacher, and the course booking efficiency and the user experience are improved.
Description
Technical Field
The invention relates to the technical field of online education, in particular to a course information pushing method, a course information pushing system, course information pushing equipment and a storage medium.
Background
With the rapid development of online education technology, more and more people choose to participate in various curriculum learning related knowledge online. In the existing course management process, firstly, a user needs to order courses on line, when the user orders the courses, the online education platform can push some course information to a terminal of the user, the user browses the course information on the terminal and selects the required courses according to the needs and the idle time of the user, then, the user puts the courses at the appointed time of the selected courses, and the courses can be evaluated after the courses are put. However, this approach has the following drawbacks:
(1) after a user enters an online education platform, most of received course information is mass course information which is not screened, the user needs to screen courses needed by the user from a mass course list, the user needs to schedule the courses, and course booking efficiency is not high.
(2) The existing course list only shows course time and course types, after a user orders a course, a teacher can be arranged for the user, the course and the teacher cannot be matched in advance, the teacher is often randomly distributed during later-stage distribution, the requirement of the user for independently selecting the teacher cannot be met, and the user affects a pre-course arrangement plan if the user sees that the arranged teacher is unsatisfied and the situation of course quitting is likely to happen.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a course information pushing method, a system, equipment and a storage medium, which are used for pushing course information for a user in a targeted manner by combining the learning plan of the user and the preference of a teacher, so that the course booking efficiency and the user experience are improved.
The embodiment of the invention provides a course information pushing method, which comprises the following steps:
receiving learning plan data of a user from a user terminal, the learning plan data including a planned learning time and a planned lesson category of the user;
inquiring matched alternative courses and teacher information of each alternative course in a course information base according to the learning plan data;
acquiring teacher preference data of a user from an evaluation database;
calculating the matching degree of the teacher information of each alternative course and the teacher preference data;
and sequencing the alternative courses according to the matching degree, and pushing the sequenced alternative courses and the corresponding teacher information to the user terminal.
Optionally, the obtaining teacher preference data of the user from the evaluation database includes the following steps:
acquiring historical course evaluation data of a user from an evaluation database;
and selecting evaluation data with the user evaluation higher than or equal to a preset evaluation standard from the historical course evaluation data, and acquiring teacher information corresponding to the selected evaluation data as teacher preference data.
Optionally, the teacher information of each alternative course includes attribute values of respective attributes of the teacher of each alternative course, and the teacher preference data includes attribute values of respective attributes of each teacher;
calculating the matching degree of the teacher information of each alternative course and the teacher preference data, wherein the matching degree is determined according to the attribute value similarity;
the step of determining the matching degree according to the similarity of the attribute values comprises the following steps:
counting attribute values with the most occurrence times of each attribute of the teacher in the teacher preference data, and taking the attribute values as preference attribute values of corresponding attributes;
and calculating the similarity between the attribute value of the teacher of each alternative course and the preference attribute value in the teacher preference data as the matching degree.
Optionally, in the calculating the matching degree between the teacher information of each candidate course and the teacher preference data, before determining the matching degree according to the similarity of the attribute values, the method further includes the following steps:
judging whether a teacher matching model of the user is stored in the matching model library;
if so, inputting teacher information of each alternative course into the teacher matching model to obtain the output matching degree;
and if not, executing the step of determining the matching degree according to the similarity of the attribute values.
Optionally, the course information pushing method further includes a step of constructing a matching degree model, where the step of constructing the matching degree model includes the following steps:
acquiring historical course evaluation data of a user from an evaluation database;
judging whether the evaluation number m higher than a preset evaluation standard in the historical course evaluation data of the user is larger than a first threshold value or not;
if yes, a teacher matching model based on machine learning is built according to teacher information corresponding to m evaluation data higher than a preset evaluation standard, the teacher matching model is stored in a matching model library, the input of the teacher matching model is teacher information, and the output of the teacher matching model is matching degree.
Optionally, the constructing a teacher matching model based on machine learning includes the following steps:
determining the matching degree corresponding to the evaluation data corresponding to each teacher information according to the preset mapping relation between the evaluation data and the matching degree;
and adding each teacher information and the corresponding matching degree into a training set, and training the teacher matching model by adopting the training set.
Optionally, the calculating the similarity between the attribute value of the teacher of each candidate course and the preference attribute value in the teacher preference data includes the following steps:
and for each candidate teacher, judging whether the candidate teacher has each preference attribute value, and summing the occurrence times of the preference attribute values of the candidate teacher to serve as the similarity of the candidate teacher.
Optionally, before determining, for each candidate teacher, whether the candidate teacher has each preference attribute value, the method further includes the following steps:
sequentially counting the occurrence frequency a of the ith preference attribute value in n evaluation data which are higher than the preset evaluation standardiI ∈ (1, p), p being the number of attributes;
counting the number b of preference attribute values with the occurrence times larger than a second threshold value, and calculating the ratio b/n of the number b to n;
judging whether b/n is larger than a third threshold value;
if so, judging whether each candidate teacher has each preference attribute value or not for each candidate teacher, and summing the occurrence times of the preference attribute values of the candidate teachers to serve as the similarity of the candidate teachers;
if not, calculating a teacher preference feature vector c according to the preference attribute value, calculating a teacher feature vector d of each alternative course according to the attribute value of the teacher of each alternative course, and calculating the similarity between the teacher feature vector d of each alternative course and the teacher preference feature vector c.
Optionally, after obtaining teacher preference data of the user from the evaluation database, the method further includes the following steps:
counting attribute values with the most occurrence times of each attribute of the teacher in the teacher preference data, and taking the attribute values as preference attribute values of corresponding attributes;
sequentially calculating the occurrence times a of the ith preference attribute valueiA ratio a to the number n of evaluation data for which the user evaluation is higher than a preset evaluation criterioniN, i ∈ (1, p), p being the number of attributes;
corresponding ratio a to each preference attribute valueiAdding/n to obtain a preference degree value of the user;
judging whether the preference degree value is larger than a fourth threshold value or not;
if yes, calculating the matching degree of teacher information of each alternative course and the teacher preference data, and sequencing the alternative courses according to the matching degree;
otherwise, calculating the ratio of the selected course quantity and the total discharged courses of the teachers of all the alternative courses in the future preset time period, and sequencing the alternative courses according to the calculated ratio.
Optionally, after querying the matching candidate lesson in the lesson information base according to the learning plan data, the method further includes the following steps:
acquiring historical course data of a user, wherein the historical course data comprises a class and a progress of a taken course of the user;
deleting the completed lesson of the user in the alternative lesson from the alternative lesson.
Optionally, after the alternative courses are sorted according to the matching degree, the method further includes the following steps:
and arranging the alternative courses which are matched with the existing course categories in the alternative courses before other courses.
The embodiment of the invention also provides a course information pushing system, which is applied to the course information pushing method, and the system comprises:
the system comprises a plan acquisition module, a learning planning module and a learning planning module, wherein the plan acquisition module is used for receiving learning planning data of a user from a user terminal, and the learning planning data comprises planning learning time and planning course classes of the user;
the course selection module is used for inquiring matched alternative courses and teacher information of each alternative course in a course information base according to the learning plan data;
the course sequencing module is used for acquiring teacher preference data of the user from the evaluation database, calculating the matching degree of the teacher information of each alternative course and the teacher preference data, and sequencing the alternative courses according to the matching degree;
and the course pushing module is used for pushing the sequenced alternative courses and the corresponding teacher information to the user terminal.
An embodiment of the present invention further provides a course information pushing device, including:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to execute the steps of the course information pushing method via executing the executable instructions.
The embodiment of the present invention further provides a computer-readable storage medium, configured to store a program, where the program, when executed, implements the steps of the course information pushing method.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
The course information pushing method, the course information pushing system, the course information pushing equipment and the course information pushing storage medium have the following advantages:
the invention solves the problems in the prior art, and the course information is pushed for the user in a targeted manner by combining the learning plan of the user and the preference of a teacher, so that the course ordering efficiency and the user use experience are improved; when the user selects lessons, the user can see the course information and the teacher information, so that the user can select courses more meeting the needs of the user, and the class returning rate in the later period is reduced.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
Fig. 1 is a flowchart of a course information pushing method according to a first embodiment of the present invention;
FIG. 2 is a schematic illustration of a learning plan fill-in page of a first embodiment of the present invention;
FIG. 3 is a flowchart of the first embodiment of the present invention for calculating the matching of teacher information for each alternative lesson to the teacher preference data;
FIG. 4 is a flowchart of constructing a matching degree model according to the first embodiment of the present invention;
FIG. 5 is a flowchart of calculating attribute similarity according to the first embodiment of the present invention;
FIG. 6 is a schematic view of an interface for pushing lessons to a user according to a first embodiment of the present invention;
FIG. 7 is a schematic structural diagram of a course pushing system according to a first embodiment of the present invention;
FIG. 8 is a diagram of an interface for push after reordering based on category matching according to a second embodiment of the present invention;
FIG. 9 is a flowchart of the third embodiment of the present invention for ordering lessons according to preference;
fig. 10 is a schematic view of a course information pushing apparatus according to an embodiment of the present invention;
fig. 11 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
As shown in fig. 1, in order to solve the above technical problem, a first embodiment of the present invention provides a course information pushing method, which includes the following steps:
s100: receiving learning plan data of a user from a user terminal, the learning plan data including a planned learning time and a planned lesson category of the user; the user terminal can be a mobile terminal such as a mobile phone and a tablet computer of a user;
s200: inquiring matched alternative courses and teacher information of each alternative course in a course information base according to the learning plan data, wherein the teacher information of each alternative course can comprise attribute values of various attributes of the teacher of each alternative course, and the attributes can comprise gender, age group, school calendar, working age, hobbies, character characteristics and the like;
s300: acquiring teacher preference data of a user from an evaluation database;
s400: calculating the matching degree of the teacher information of each alternative course and the teacher preference data;
s500: and sequencing the alternative courses according to the matching degree, and pushing the sequenced alternative courses and the corresponding teacher information to the user terminal.
According to the course information pushing method, the optional courses are firstly screened according to the learning plan data through the steps S100 and S200, then the optional courses are sequenced on the basis of the preference of the user to the teacher through the steps S300 to S500, so that the course information is pushed for the user in a targeted manner by combining the learning plan of the user and the preference of the user to the teacher, and the course booking efficiency and the user use experience are improved. For the user, because the pushed courses are screened according to the study plan, the courses most relevant to the study plan can be seen when the courses are selected, and the pushed courses are sequenced according to the preference of the user, so that the required courses can be found more intuitively and more quickly, and the course booking efficiency is greatly improved; in addition, in step S500, the sorted courses and the teacher information are pushed to the user together, so that the user can see not only the course information but also the teacher information when selecting a course, which is helpful for the user to select a course more suitable for the user' S needs, and reduces the rate of course quitting in the later period.
In step S100, when the learning plan data of the user is received from the user terminal, a learning plan filling page may be first pushed to the user terminal, and the learning plan filling page may be as shown in fig. 2. The learning plan filling page includes a filling box or a selection box of the planned learning time of the user in a future period of time, and the planned learning time can be which time period or time periods of which days of the week or which time periods of which days of the week. The subject category, subject subcategory, and stage category in the class category are each hierarchical relationships that decline layer by layer, such as subject categories including english, chinese, math, etc., under english subject categories subject subcategories including teenager's spoken language, jab, blessing, english fourth six, etc., under english fourth six stage categories may include fourth stage beginners, fourth stage reinforcement, fourth stage sprint, sixth stage beginners, etc., if the user does not select a subject subcategory, all subject subcategories under the selected subject category are included by default, and if the user does not select a stage category, all stage categories under the selected subject subcategory are included by default.
The planned lesson categories may be one or more lesson categories which are planned and selected by a specific user, when the user selects a plurality of lesson categories, the priority level of each lesson category may be simultaneously labeled, when the alternative lessons are ranked according to the matching degree in step S500, the alternative lessons of each lesson category are ranked according to the matching degree, and then the alternative lesson of the lesson category with a high priority level is ranked behind the alternative lesson of the lesson category with a low priority level, and if the user selects a plurality of lesson categories without labeling the priority level of each lesson category, the alternative lessons of all lesson categories are ranked together according to the matching degree.
In the step S200, the course information base stores basic information of all currently selectable courses, where the basic information of a course includes a time of the course, a category of the course, teacher information of the course, a limited number of students of the course, and a selected number of students of the course, and if a student of a course is selected, the corresponding course is screened out from the alternative courses.
In the step S200, the matched candidate courses and the teacher information of each candidate course are queried in the course information base according to the learning plan data, including screening the courses in the course information base according to the category of the courses, and then comparing the screened courses with the planned learning time set by the user, as long as the planned learning time set by the user can cover most of the time of a course, specifically, a threshold S% can be set, and the time that S% of a course corresponding to the category of the planned courses exceeds the planned learning time set by the user coincides, that is, the course is used as the candidate course.
In this embodiment, the step S300: obtaining teacher preference data of the user from the evaluation database, comprising the following steps:
s310: acquiring historical course evaluation data of a user from an evaluation database;
s320: and selecting evaluation data with the user evaluation higher than or equal to a preset evaluation standard from the historical course evaluation data, and acquiring teacher information corresponding to the selected evaluation data as teacher preference data. The teacher information in the teacher preference data includes attribute values of respective attributes of respective teachers, and the attributes may include gender, age, academic calendar, working age, hobbies, character characteristics, and the like.
For example, if the rating system is adopted, the preset rating criterion is a specific score threshold, if the rating system is adopted, the preset rating criterion is a star number threshold, and if the rating system is adopted, the good rating, the medium rating and the poor rating are selected, the preset rating criterion is the good rating.
As shown in fig. 3, the step S400: calculating the matching degree of the teacher information of each alternative course and the teacher preference data, including S420: determining the matching degree according to the similarity of the attribute values;
the step S420: the step of determining the matching degree according to the similarity of the attribute values comprises the following steps:
s421: counting attribute values with the most occurrence times of each attribute of the teacher in the teacher preference data, and taking the attribute values as preference attribute values of corresponding attributes;
for example, there are 5 evaluation data higher than the preset evaluation criterion, the first attribute is gender, wherein the teacher corresponding to 3 evaluation data is female, the preference attribute value of the attribute is female, the second attribute is age group, wherein 1 evaluation data is 25-30, 2 evaluation data is 20-25, and 3 evaluation data is 30-35, the preference attribute value of the attribute is 30-35.
S422: and calculating the similarity between the attribute value of the teacher of each alternative course and the preference attribute value in the teacher preference data as the matching degree.
Therefore, in this embodiment, the matching degree between the teacher of each candidate course and the user preference may be determined according to the similarity between the characteristic attribute value of the teacher of each candidate course and the characteristic attribute value of the teacher preference data, so as to preferentially recommend a teacher with a higher matching degree for the user.
As shown in fig. 3, in this embodiment, the step S400: in calculating the matching degree between the teacher information of each candidate course and the teacher preference data, S420: before determining the matching degree according to the similarity of the attribute values, the method further comprises the following steps:
s411: judging whether the matching model base stores a teacher matching model of the user, if so, continuing to step S412, and if not, continuing to step S420;
s412: and inputting the teacher information of each optional course into the teacher matching model to obtain the output matching degree.
Therefore, when the teacher matching model of the user is stored in the matching model library in advance, the matching degree is preferably calculated by using the teacher matching model, and the attribute values of the attributes of the teacher in each candidate course are directly combined into the feature vector and then input to the teacher matching model. The teacher matching model is a model obtained based on machine learning training, and the feature vector composed of the attribute values of the teacher is input and the matching degree of the teacher is output.
As shown in fig. 4, in this embodiment, the course information pushing method further includes step S600: and constructing a matching degree model, wherein the matching degree model is a machine learning model obtained by training by adopting a training set. Specifically, the step S600: the method for constructing the matching degree model comprises the following steps:
s610: acquiring historical course evaluation data of a user from an evaluation database;
s620: because a large amount of data is needed for constructing the matching degree model, if the data size is small, a more perfect and accurate model cannot be obtained, it is necessary to determine whether the historical data of the user meets the requirement for constructing the model, and specifically, it is determined whether the evaluation number m higher than a preset evaluation standard in the historical course evaluation data of the user is greater than a first threshold; the value of the first threshold value can be set according to needs;
s630: if yes, a teacher matching model based on machine learning is built according to teacher information corresponding to m evaluation data higher than a preset evaluation standard, the teacher matching model is stored in a matching model library, the input of the teacher matching model is the teacher information, and the output of the teacher matching model is the matching degree, wherein the teacher matching model can adopt a model built based on deep learning, such as a convolutional neural network model and the like, and can also adopt classifier models such as a support vector machine and the like;
s640: if not, the step S610 is resumed after waiting for the preset interval time.
Therefore, the step S600 may be executed by concentrating the processing for some users who have not constructed the matching degree model at regular intervals, determining whether the user has reached the requirement for constructing the matching degree model according to the step S620, if so, executing the step S630 to construct and train the model, and if not, waiting for the next processing. For the user who has constructed the matching degree model, it is not meant to update the matching degree model at intervals, and the matching degree model is trained again according to the latest data, so as to improve the matching accuracy of the matching degree model.
In this embodiment, the step S630: the method for constructing the teacher matching model based on the machine learning comprises the following steps:
s631: determining the matching degree corresponding to the evaluation data corresponding to each teacher information according to the preset mapping relation between the evaluation data and the matching degree;
for example, the matching degree may be divided into a plurality of matching levels, the higher the matching level is, the larger the corresponding matching degree value is, each matching level corresponds to a numerical point or a numerical range of a matching degree value, for a star rating system, different star numbers correspond to different matching levels, the more the star number is, the higher the matching level is, the higher the matching degree value is, for the rating system, different score ranges correspond to different matching levels, the higher the score is, the higher the matching level is, the higher the matching degree value is;
therefore, when the teacher matching model is constructed and trained, after the teacher information of the training set is acquired, manual marking of the matching degree of the teacher information is not needed manually, the more objective and accurate matching degree of the teacher can be obtained automatically according to the evaluation data, the teacher information is marked, and the accuracy and the efficiency of model training are improved;
s632: adding each teacher information and the corresponding matching degree into a training set, training the teacher matching model by adopting the training set, and after repeated iterative training, indicating that the model loss is less than a preset loss threshold value, and training the model until the model is converged to obtain the trained teacher matching model. The matching degree marked for each teacher information may be a matching level, or may be a numerical point or a numerical range corresponding to the matching level.
And when the teacher matching model is updated subsequently, newly-added user evaluation data from the model training completion to the current time is obtained, the teacher information and the corresponding matching grade are added into the training set, and the teacher matching model is retrained by adopting the updated training set.
Therefore, the matching degree calculation method based on the teacher matching model greatly improves the efficiency of matching teachers. Considering the problem that a teacher matching model needs a large amount of data support to obtain a relatively accurate model, the method firstly judges whether a condition for constructing the model is met before the teacher matching model is established, and when the teacher is matched, if the teacher matching model is not found, the matching degree is calculated according to the similarity between the attribute value of the preference data and the attribute value of the teacher of each optional course, so that the most appropriate teacher course is preferentially pushed for a user.
In this embodiment, the step S422: calculating the similarity between the attribute value of the teacher of each alternative course and the preference attribute value in the teacher preference data, comprising the following steps:
s422-4: and for each candidate teacher, judging whether the candidate teacher has each preference attribute value, and summing the occurrence times of the preference attribute values of the candidate teacher to serve as the similarity of the candidate teacher.
The teacher preference data includes attribute values for p attributes, for example, the first attribute being gender,wherein the preference attribute value is a female attribute value, and the occurrence frequency of the female attribute value in n evaluation data higher than the preset evaluation standard is a1The second attribute is an age group whose preference attribute value appears a times in n evaluation data higher than a preset evaluation criterion2And so on. The attribute values of the 1 st, 3 rd and 5 th attributes of one candidate teacher are the same as the preference attribute values of the 1 st, 3 rd and 5 th attributes in the preference attributes of the teacher, then a is1+a3+a5And obtaining the result as the similarity of the alternative teacher.
Further, in this embodiment, as shown in fig. 5, the step S422-4: before judging whether each alternative teacher has each preference attribute value, the method further comprises the following steps:
s422-1: sequentially counting the occurrence frequency a of the ith preference attribute value in n evaluation data which are higher than the preset evaluation standardiI ∈ (1, p), p being the number of attributes;
s422-2: counting the number b of the preference attribute values with the occurrence times larger than a second threshold value, and calculating the ratio b/n of the number b to the number n, wherein the preference attribute values with the occurrence times larger than the second threshold value are preference attribute values with strong tendency presented by users, and the numerical value of the second threshold value can be set as required;
s422-3: judging whether b/n is larger than a third threshold value, if so, continuing to step S422-4, and if not, continuing to step S422-5; the numerical value of the third threshold value can be set according to needs;
s422-4: if b/n is greater than the third threshold, the user shows a strong tendency to some preference attribute values, for example, the user is obviously more inclined to female teachers in past courses and is inclined to be seemingly lively teachers in character, and whether b preference attribute values with the occurrence frequency greater than the second threshold are matched or not plays a greater role in the matching degree compared with other preference attribute values with the occurrence frequency less than the second threshold;
therefore, in this case, for each candidate teacher, it is determined whether the candidate teacher has each preference attribute value, and the number of occurrences of the preference attribute value that the candidate teacher has is summed up to be used as the similarity of the candidate teacher, where the number of occurrences of the preference attribute value has a relatively large effect on the similarity of the candidate teacher;
s422-5: in this case, the tendencies of the users to the respective preference attribute values are relatively average, and therefore, the degrees of matching of the respective preference attribute values are comprehensively considered to calculate the degrees of similarity, specifically, the teacher preference feature vector c is calculated from the preference attribute values, the teacher feature vector d of the alternative course is calculated from the attribute values of the teacher of the respective alternative courses, and the degrees of similarity between the teacher feature vector d of the alternative course and the teacher preference feature vector c are calculated.
Here, the similarity of the feature vectors may be calculated as euclidean distance or cosine similarity of the feature vectors c and d, or the like. In this case, the influence of the various preference attribute values on the similarity is relatively average.
Fig. 6 is a schematic diagram of an interface for pushing a course to a user according to a first embodiment of the present invention. In step S500, when the course information is pushed, the corresponding teacher information is pushed to the user terminal. When the user clicks on the teacher's position under the course display picture, a short teacher profile is displayed, such as the teacher's picture, the teacher's personality traits, the disciplines for which the teacher is responsible, the teacher's preferences, and so on. The convenience of customers checks teacher information when checking optional course information, and the course information and the teacher information are integrated to select courses needed by the users, so that the situation that the students leave the courses due to the fact that the teachers are not suitable after the students leave the courses in the later period is greatly reduced. In addition, in this embodiment, the displayed courses may be screened according to different categories of courses, for example, when category 1 is clicked, only the course of category 1 is displayed, or screening may be performed according to the progress of the courses, and a course that has been opened is selected to be viewed, or a course that has not been opened is selected to be viewed.
As shown in fig. 7, an embodiment of the present invention further provides a course information pushing system, which is applied to the course information pushing method, and the system includes:
the plan obtaining module M100 is configured to interact with a user terminal, receive learning plan data of a user from the user terminal, where the learning plan data includes a planned learning time and a planned course category of the user, and before receiving the learning plan data, push an interface formulated by a learning plan to the user terminal, where the interface may be as shown in fig. 2;
the course selection module M200 is configured to query, according to the learning plan data, matched alternative courses and teacher information of each alternative course in a course information base;
the course sequencing module M300 is configured to acquire teacher preference data of the user from the evaluation database, calculate a matching degree between teacher information of each alternative course and the teacher preference data, and sequence the alternative courses according to the matching degree;
the course pushing module M400 is configured to interact with a user terminal, and push the sorted optional courses and the corresponding teacher information to the user terminal, where a pushed course display interface may be as shown in fig. 6.
According to the course information pushing system, the plan obtaining module M100 and the course selecting module M200 are used for screening the alternative courses according to the learning plan data, and then the course sorting module M300 is used for sorting the alternative courses based on the preference of the user to the teacher, so that the course information is pushed for the user in a targeted manner by combining the learning plan of the user and the preference of the user to the teacher, and the course booking efficiency and the user use experience are improved. For the user, because the pushed courses are screened according to the study plan, the courses most relevant to the study plan can be seen when the courses are selected, and the pushed courses are sequenced according to the preference of the user, so that the required courses can be found more intuitively and more quickly, and the course booking efficiency is greatly improved; in addition, the invention pushes the sequenced courses and the teacher information to the user through the course pushing module M400, so that the user can see not only the course information but also the teacher information when selecting the course, the invention is beneficial to the user to select the course which is more in line with the needs of the user, and the course returning rate in the later period is reduced.
For example, the plan obtaining module M100 may obtain learning plan data by using a specific implementation manner of the step S100, the course selecting module M200 may filter the alternative courses by using a specific implementation manner of the step S200, the course sorting module M300 may sort the alternative courses according to preferences of teachers by using a specific implementation manner of the steps S300 to S500, and the course pushing module M400 may push the sorted alternative courses and the corresponding teacher information to the user terminal by using a pushing manner as in the step S500, which is not described herein again.
The present invention also provides a course information pushing method and system of the second embodiment, and the difference between the embodiment and the first embodiment is: and screening and sequencing the alternative courses by combining the progress of the user on the courses, so that the course pushing more meeting the requirement of the user can be obtained. In this embodiment, the course information pushing system further includes a progress query module.
In this embodiment, the step S200: after the matched alternative courses are inquired in the course information base according to the learning plan data, the method further comprises the following steps:
s230: the progress query module acquires historical course data of a user, wherein the historical course data comprises the class and progress of the taken course of the user;
s240: and deleting the completed courses of the users in the alternative courses from the alternative courses, thereby avoiding repeated pushing of the courses, reducing the data volume of the alternative courses, reducing the calculation volume of subsequent calculation matching degree and improving the course pushing efficiency.
Further, when deleting the completed lessons of the user, the lessons with the lower level than the completed lessons of the user are included, for example, if the lessons with the middle level of the six English levels are completed by the user, the lessons with the first level of the four English levels and the six English levels are screened out.
In this embodiment, the step S500: after the alternative courses are sorted according to the matching degree, the method further comprises the following steps:
the course sorting module M300 sorts the alternative courses matching the selected course category among the alternative courses before other courses, and then pushes the rearranged alternative courses and the corresponding teacher information to the user terminal, so that the user can more preferentially see the course that best meets the current stage requirement of the user. Matching in the alternative lesson with the class of the lesson that has been taken means that the alternative lesson is the same as or similar to the class of the lesson that has been taken and that the alternative lesson does not belong to the lesson that the user has already completed. For example, the class of the previous lesson is the first class of the fourth class of english, and the user has completed the class of the first class of the fourth class of english, then the first class of the fourth class of english has been screened out in step S240, and the class of the class similar to the first class of the fourth class of english needs to be selected, where the similarity relationship of the classes can be preset, for example, the class of the first class of the fourth class of english has the middle class of the fourth class of english, the high class of the fourth class of english, the middle class of the sixth class of english, the six class of english, etc., and the class of the similar class is arranged before other classes, thereby improving.
As shown in fig. 8, a schematic diagram of an interface pushed after reordering according to category matching according to a second embodiment of the present invention. In this embodiment, the class 1 matches the class already taken by the user, the classes of the class 1 are all advanced to the front of other classes, and further, the classes of the class 1 can be marked with colors or characters to remind the user of the attention.
The present invention also provides a course information pushing method and system of the third embodiment, and the difference between the embodiment and the first embodiment is: the preference degree of the user to the teacher is judged according to the past selection of the user to the teacher, if the preference degree of the user to the teacher is low, the user generally selects the teacher randomly, and the teacher does not have obvious tendency to the teacher, under the condition, the courses can be sequenced by combining the selected amount of the courses of the teacher, so that the workload of the teacher is effectively subjected to automatic balanced arrangement, and the situation that some courses of the teacher are particularly full and some courses of the teacher are empty is avoided. If the preference degree of the user to the teacher is higher, the selection tendency of the user to the teacher is higher, so that the courses are still selected to be sorted according to the preference of the teacher, and a pushing result which meets the requirements of the user better is obtained. In this embodiment, the course information pushing system further includes a preference degree calculating module, configured to calculate a preference degree value of the user, and the course sorting module is further configured to sort the courses selected by the teacher according to a ratio of the total courses when the preference degree value of the user is relatively low.
As shown in fig. 9, specifically, in this embodiment, the step S300: after teacher preference data of the user is acquired from the evaluation database, the method further comprises the following steps:
s330: counting attribute values with the most occurrence times of each attribute of the teacher in the teacher preference data, and taking the attribute values as preference attribute values of corresponding attributes;
s340: sequentially calculating the occurrence times a of the ith preference attribute valueiA ratio a to the number n of evaluation data for which the user evaluation is higher than a preset evaluation criterioniN, i ∈ (1, p), where p is the number of attributes, e.g. for the first attribute, a total of 10 users evaluate the evaluation data above a preset evaluation criterion, where the preference attribute value appears in 8 evaluation data, the ratio a1The/n is 8/10;
s350: corresponding ratio a to each preference attribute valueiAdding/n to obtain a preference degree value of the user;
s360: judging whether the preference degree value is larger than a fourth threshold value, wherein the value of the fourth threshold value can be set according to needs;
if the preference degree value is greater than the fourth threshold, it indicates that the preference attribute values of different attributes of the user for the teacher are still relatively large, that is, the user has a certain preference for the teacher, and the courses are sorted by preferentially referring to the preference of the user for the teacher, so the steps S400 and S500 are continuously executed;
if the preference degree value is less than or equal to the fourth threshold, it indicates that the tendency of the user to the preference attribute values of different attributes of the teacher is relatively small, that is, the user generally selects a course and the teacher at random, and does not have obvious tendency to the requirement of the teacher, that is, it indicates that the user does not have a specific requirement to the teacher, then the steps S370 and S380 are continued;
s370: under the condition that a user has no special requirements on teachers, in order to automatically balance the course arrangement amount of each teacher and realize the automatic adjustment balance of the workload of each teacher, the teachers are sequenced according to the proportion of the selected course amount of the teacher in the total arranged courses, and the teachers with less selected course amount at present are preferentially recommended;
specifically, calculating the ratio of the number of selected courses to the total discharged courses of teachers of various alternative courses in a future preset time period;
s380: and sequencing the alternative courses according to the calculated ratio, and then pushing the sequenced alternative courses and teacher information to the user terminal.
Here, in step S370, the statistics of the number of selected courses for the teacher of the candidate course may be performed by taking into account the types of different courses to perform case statistics, if a course is a course of 1V1, the number of selected courses for the teacher is increased by 1 after the course is selected, and if a course is a course with more than 1V, that is, a teacher gives a course to a plurality of students at the same time, the number of selected courses for the teacher is increased by the ratio of the number of students with the selected course to the number of students with the course after the course is selected. The total number of exhausted lessons for a teacher of an alternative lesson is the total number of in-progress lessons and outstanding lessons that have been scheduled for that teacher explicitly. Therefore, the unbalanced problem of workload distribution among teachers can be avoided, the unstable problem of courses caused by unbalanced workload of teachers is further avoided, and the courses selected by users can be arranged.
The embodiment of the invention also provides a course information pushing device, which comprises a processor; a memory having stored therein executable instructions of the processor; wherein the processor is configured to execute the steps of the course information pushing method via executing the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" platform.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 10. The electronic device 600 shown in fig. 10 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 10, the electronic device 600 is embodied in the form of a general purpose computing device. The combination of the electronic device 600 may include, but is not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting different platform combinations (including memory unit 620 and processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code, which can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the above-mentioned course information pushing method section of this specification. For example, the processing unit 610 may perform the steps as shown in fig. 1. Specifically, when the processing unit 610 executes each step in fig. 1, a specific step execution manner may adopt a specific implementation manner of each step of the course information pushing method, which is not described again.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The embodiment of the present invention further provides a computer-readable storage medium, configured to store a program, where the program, when executed, implements the steps of the course information pushing method. In some possible embodiments, the various aspects of the present invention may also be implemented in the form of a program product, which includes program code for causing a terminal device to perform the steps according to the various exemplary embodiments of the present invention described in the above-mentioned course information pushing method section of this specification, when the program product is run on the terminal device.
Referring to fig. 11, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, C + +, or the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages.
In summary, compared with the prior art, the course information pushing method, system, device and storage medium provided by the present invention have the following advantages:
the invention solves the problems in the prior art, and the course information is pushed for the user in a targeted manner by combining the learning plan of the user and the preference of a teacher, so that the course ordering efficiency and the user use experience are improved; when the user selects lessons, the user can see the course information and the teacher information, so that the user can select courses more meeting the needs of the user, and the class returning rate in the later period is reduced.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (14)
1. A course information pushing method is characterized by comprising the following steps:
receiving learning plan data of a user from a user terminal, the learning plan data including a planned learning time and a planned lesson category of the user;
inquiring matched alternative courses and teacher information of each alternative course in a course information base according to the learning plan data;
acquiring teacher preference data of a user from an evaluation database;
calculating the matching degree of the teacher information of each alternative course and the teacher preference data;
and sequencing the alternative courses according to the matching degree, and pushing the sequenced alternative courses and the corresponding teacher information to the user terminal.
2. The course information pushing method as claimed in claim 1, wherein said step of obtaining teacher preference data of the user from the evaluation database comprises the steps of:
acquiring historical course evaluation data of a user from an evaluation database;
and selecting evaluation data with the user evaluation higher than or equal to a preset evaluation standard from the historical course evaluation data, and acquiring teacher information corresponding to the selected evaluation data as teacher preference data.
3. The lesson information pushing method according to claim 2, wherein the teacher information of each alternative lesson includes attribute values of respective attributes of the teacher of each alternative lesson, and the teacher preference data includes attribute values of respective attributes of each teacher;
calculating the matching degree of the teacher information of each alternative course and the teacher preference data, wherein the matching degree is determined according to the attribute value similarity;
the step of determining the matching degree according to the similarity of the attribute values comprises the following steps:
counting attribute values with the most occurrence times of each attribute of the teacher in the teacher preference data, and taking the attribute values as preference attribute values of corresponding attributes;
and calculating the similarity between the attribute value of the teacher of each alternative course and the preference attribute value in the teacher preference data as the matching degree.
4. The course information pushing method as claimed in claim 3, wherein, in calculating the matching degree between the teacher information of each candidate course and the teacher preference data, before determining the matching degree according to the similarity of the attribute values, the method further comprises the following steps:
judging whether a teacher matching model of the user is stored in the matching model library;
if so, inputting teacher information of each alternative course into the teacher matching model to obtain the output matching degree;
and if not, executing the step of determining the matching degree according to the similarity of the attribute values.
5. The course information pushing method as claimed in claim 4, further comprising a step of constructing a matching degree model, wherein said step of constructing a matching degree model comprises the steps of:
acquiring historical course evaluation data of a user from an evaluation database;
judging whether the evaluation number m higher than a preset evaluation standard in the historical course evaluation data of the user is larger than a first threshold value or not;
if yes, a teacher matching model based on machine learning is built according to teacher information corresponding to m evaluation data higher than a preset evaluation standard, the teacher matching model is stored in a matching model library, the input of the teacher matching model is teacher information, and the output of the teacher matching model is matching degree.
6. The course information pushing method as claimed in claim 5, wherein said constructing a teacher matching model based on machine learning comprises the following steps:
determining the matching degree corresponding to the evaluation data corresponding to each teacher information according to the preset mapping relation between the evaluation data and the matching degree;
and adding each teacher information and the corresponding matching degree into a training set, and training the teacher matching model by adopting the training set.
7. The course information pushing method as claimed in claim 3, wherein said calculating the similarity between the attribute value of the teacher of each candidate course and the preference attribute value in the teacher preference data comprises the following steps:
and for each candidate teacher, judging whether the candidate teacher has each preference attribute value, and summing the occurrence times of the preference attribute values of the candidate teacher to serve as the similarity of the candidate teacher.
8. The curriculum information pushing method of claim 7, wherein before determining, for each candidate teacher, whether the candidate teacher has each preference attribute value, further comprising the following steps:
sequentially counting the occurrence frequency a of the ith preference attribute value in n evaluation data which are higher than the preset evaluation standardiI ∈ (1, p), p being the number of attributes;
counting the number b of preference attribute values with the occurrence times larger than a second threshold value, and calculating the ratio b/n of the number b to n;
judging whether b/n is larger than a third threshold value;
if so, judging whether each candidate teacher has each preference attribute value or not for each candidate teacher, and summing the occurrence times of the preference attribute values of the candidate teachers to serve as the similarity of the candidate teachers;
if not, calculating a teacher preference feature vector c according to the preference attribute value, calculating a teacher feature vector d of each alternative course according to the attribute value of the teacher of each alternative course, and calculating the similarity between the teacher feature vector d of each alternative course and the teacher preference feature vector c.
9. The course information pushing method as claimed in claim 2, further comprising the following steps after obtaining teacher preference data of the user from the evaluation database:
counting attribute values with the most occurrence times of each attribute of the teacher in the teacher preference data, and taking the attribute values as preference attribute values of corresponding attributes;
sequentially calculating the occurrence times a of the ith preference attribute valueiA ratio a to the number n of evaluation data for which the user evaluation is higher than a preset evaluation criterioniN, i ∈ (1, p), p being the number of attributes;
corresponding ratio a to each preference attribute valueiAdding/n to obtain a preference degree value of the user;
judging whether the preference degree value is larger than a fourth threshold value or not;
if yes, calculating the matching degree of teacher information of each alternative course and the teacher preference data, and sequencing the alternative courses according to the matching degree;
otherwise, calculating the ratio of the selected course quantity and the total discharged courses of the teachers of all the alternative courses in the future preset time period, and sequencing the alternative courses according to the calculated ratio.
10. The course information pushing method as claimed in claim 1, wherein after said matching candidate course is queried in the course information base according to said learning plan data, further comprising the steps of:
acquiring historical course data of a user, wherein the historical course data comprises a class and a progress of a taken course of the user;
deleting the completed lesson of the user in the alternative lesson from the alternative lesson.
11. The method for pushing lesson information as claimed in claim 10, further comprising, after said sorting said alternative lessons according to said matching degree, the steps of:
and arranging the alternative courses which are matched with the existing course categories in the alternative courses before other courses.
12. A course information pushing system applied to the course information pushing method of any one of claims 1 to 11, the system comprising:
the system comprises a plan acquisition module, a learning planning module and a learning planning module, wherein the plan acquisition module is used for receiving learning planning data of a user from a user terminal, and the learning planning data comprises planning learning time and planning course classes of the user;
the course selection module is used for inquiring matched alternative courses and teacher information of each alternative course in a course information base according to the learning plan data;
the course sequencing module is used for acquiring teacher preference data of the user from the evaluation database, calculating the matching degree of the teacher information of each alternative course and the teacher preference data, and sequencing the alternative courses according to the matching degree;
and the course pushing module is used for pushing the sequenced alternative courses and the corresponding teacher information to the user terminal.
13. A course information pushing apparatus, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the course information pushing method of any of claims 1 to 11 via execution of the executable instructions.
14. A computer-readable storage medium storing a program, wherein the program is executed to implement the steps of the course information pushing method of any one of claims 1 to 11.
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