CN117892001A - Auxiliary recommendation method and system for college entrance examination volunteer filling scheme - Google Patents
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Abstract
The invention relates to the field of college entrance examination volunteer filling, in particular to an auxiliary recommendation method and an auxiliary recommendation system for a college entrance examination volunteer filling scheme, wherein the auxiliary recommendation method comprises the steps of obtaining personal information of an examinee; analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee, and screening out the university professions conforming to the condition of the examinee; acquiring the university profession conforming to the condition of the examinee, and recording data of the calendar year college entrance according to the university profession; deducing the recording probability of the university profession conforming to the condition of the examinee according to the recording data; analyzing trend data of current and future employment markets according to the university professions, and predicting employment prospects of the university professions by combining professional preference and trend data; sorting the screened university professions by combining the admission probability and the trend data to generate a recommendation scheme; and displaying the generated recommended scheme to the candidate end and the parent end. The method can comprehensively consider a plurality of factors and provide a recommendation result with higher practicability.
Description
Technical Field
The application relates to the technical field of college entrance examination volunteer filling, in particular to an auxiliary recommendation method and an auxiliary recommendation system for college entrance examination volunteer filling schemes.
Background
College entrance examination volunteers are important links facing college entrance examination students, and are related to study and life for several years or even longer in the future. The volunteer gradient is set reasonably, so that both 'one-punch' volunteers and 'bottom-protecting' volunteers are needed. With the continuous perfection and popularity of college and universities, more and more students choose to take high-level notes in hopes of being able to enter ideal universities and professions. However, due to the complexity and variability of college entrance examination volunteers, many examinees and parents often have some blindness and uncertainty in filling the volunteers. Therefore, how to provide an accurate and efficient college entrance examination volunteer filling scheme for examinees is a current urgent problem to be solved. At present, auxiliary recommendation methods for college entrance examination volunteer filling are mainly divided into two types: the analysis based on historical data provides reference for examinees by mining and analyzing the data of the college entrance examination and employment data of the past year; the other category is based on personalized recommendation algorithm, and the suitable institutions and professions are recommended for the examinees by combining factors such as personal information, interest preference and the like of the examinees.
In the prior art, although the analysis method based on the historical data in the prior art can provide a certain degree of reference for examinees, the accuracy of the method in practical application is greatly influenced due to the fluctuation of factors such as the score line of the college entrance examination, the recruitment plan and the like; secondly, the prior art often cannot effectively combine various factors such as personal information, historical data and employment market trend of the examinee, and provides a comprehensive and accurate recommendation scheme for the examinee.
Accordingly, the prior art has drawbacks and needs improvement.
Disclosure of Invention
In order to solve one or more problems in the prior art, a main object of the present application is to provide an auxiliary recommendation method and system for college entrance examination volunteer filling schemes.
In order to achieve the above object, the present application proposes an auxiliary recommendation method for college entrance examination volunteer reporting scheme, the method comprising:
obtaining personal information of an examinee, wherein the personal information comprises achievements, university positions and professional preferences;
analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee, and screening out the university professions conforming to the condition of the examinee;
acquiring the university profession conforming to the condition of an examinee, and recording data of the calendar year college entrance according to the university profession;
deducing the admission probability of the university profession conforming to the condition of the examinee according to the admission data;
analyzing trend data of current and future employment markets according to the university professions, and predicting employment prospects of the university professions by combining the professional preferences and the trend data;
sorting the screened university professions by combining the admission probability and the trend data to generate a recommendation scheme;
And displaying the generated recommended scheme to the student end and the parent end.
Further, the deducing the admission probability of the university profession conforming to the condition of the examinee according to the admission data comprises the following steps:
acquiring the recording data of colleges and universities of colleges and the successful recording examinee information of the calendar year;
extracting first characteristics of successful recording examinee information in the past, wherein the first characteristics comprise subject achievements, superfine projects and volunteer filling sequences;
analyzing reference data between the first characteristic of the calendar year examinee and the profession of the institution;
extracting personal information of the examinee, and analyzing the similarity between the personal information of the examinee and the reference data through the preset similarity comparison method;
based on a plurality of groups of the similarity, extracting a plurality of groups of successful parameter groups with higher similarity;
and predicting the probability of the current professional admission of the examinee by the universities and colleges according to the success parameter set.
Further, the step of analyzing and modeling the personal information according to the personal information of the examinee through a personalized learning algorithm, and screening out the university professions conforming to the examinee conditions includes:
converting the personal information of the examinee into numerical characteristics, and enabling the numerical characteristics to belong to a trained personalized learning model;
Analyzing and modeling through the personalized learning model, and determining a batch line of the numerical feature in the region;
and outputting the university professions with the numerical characteristics conforming to the batch line to obtain the university professions with the screening conforming to the examinee conditions.
Further, the analyzing trend data of the current and future employment market according to the profession of the institution, and predicting employment prospects of the profession of the institution in combination with the professional preference and trend data includes:
collecting employment data of the university profession in the employment market according to the university profession, wherein the employment data comprises employment rates and employment growth rates of the university profession in a plurality of industries;
analyzing and generating demand data of the profession of the institution in the industry according to the employment rate and the employment growth rate;
generating matching data according to the matching degree of professional skills of the profession of the universities and current employment and future employment;
and forecasting the employment prospect of the university profession based on the demand data and the matching data.
Further, the method further comprises:
acquiring change data of the profession of the universities in the employment market in real time;
and periodically updating the employment data according to the change data.
Further, the method further comprises:
establishing a behavior analysis system, and collecting behavior data of an examinee in a volunteer form filling process through the behavior analysis system, wherein the behavior data comprise clicking behaviors, residence time and search keywords;
preprocessing the behavior data, and dividing the preprocessed behavior data into different group data through a clustering algorithm;
optimizing the personalized algorithm through the population data.
Further, after predicting the probability that the candidate is recorded by the institution specialty according to the success parameter set, the method further includes:
carrying out quantitative analysis on competition degree of the recorded data in the past year;
determining a time sequence factor according to the data of the competition degree recorded in the past year;
adding the time sequence factors to the reference data;
and updating the probability of predicting the professional admission of the examinee by the universities and colleges based on the time series factors.
The embodiment of the application also provides an auxiliary recommendation system for a college entrance examination volunteer filling scheme, which comprises the following steps:
the acquisition module is used for acquiring personal information of the examinee, wherein the personal information comprises achievements, university positions and professional preferences;
The screening module is used for analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee and screening out the university professions conforming to the condition of the examinee;
the matching module is used for acquiring the university professions conforming to the condition of the examinee and carrying out matching on the calendar year college entrance data according to the university professions;
the inference module is used for inferring the recording probability of the university profession conforming to the condition of the examinee according to the recording data;
the analysis module is used for analyzing trend data of the current and future employment market according to the university profession and predicting employment prospects of the university profession by combining the professional preference and the trend data;
the generation module is used for combining the admission probability and the trend data, sorting the screened university professions and generating a recommendation scheme;
and the display module is used for displaying the generated recommended scheme to the study end and the parent end.
The present application also provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the computer program is executed by the processor.
The present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of the above.
According to the auxiliary recommendation method and system for the college entrance aspiration reporting scheme, personal information of the examinee is acquired, and the personalized learning algorithm is adopted for analysis and modeling, so that the university profession meeting the condition of the examinee can be screened out according to factors such as the score of the examinee, the position of the university and professional preference. Compared with the traditional method based on historical recorded data, personalized recommendation can be more accurately recommended according to the characteristics and requirements of examinees, and the accuracy and practicability of recommendation are improved. By matching the recording data of the college and university, the recording probability of the university profession meeting the condition of the examinee can be deduced according to the personal information of the examinee and the professional of the university. The time sequence analysis can better reflect the dynamic change of the recording probability, so that the recommendation result has more reference value. And by combining trend data of the current and future employment markets and predicting employment prospects of the universities and universities, more comprehensive information and more accurate recommended schemes can be provided for examinees. The traditional method only focuses on the score of the examinee and the admission situation of the universities and the universities, but ignores the change of employment market and professional employment prospect, and the method can comprehensively consider a plurality of factors and provide more practical recommendation results. The generated recommendation scheme is displayed to the examinee and the parents, so that the examinee and the parents can be helped to better know the recording probability and employment prospect of the university profession conforming to the conditions of the examinee. The recommendation scheme not only provides decision references, but also can increase the confidence of examinees and parents in selecting departments and professions, and reduces risks caused by information asymmetry.
Drawings
Fig. 1 is a flowchart of an auxiliary recommendation method for a college entrance examination volunteer filling scheme according to an embodiment of the present application;
FIG. 2 is a flow chart of an auxiliary recommendation method for college entrance examination volunteer filling scheme according to an embodiment of the present application;
FIG. 3 is a block diagram illustrating a configuration of an auxiliary recommendation system for a college entrance examination volunteer filling scheme according to an embodiment of the present application;
fig. 4 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Referring to fig. 1, an auxiliary recommendation method for a college entrance examination volunteer filling scheme is provided in an embodiment of the present application, including a heat exchanger, the method including:
s1, acquiring personal information of an examinee, wherein the personal information comprises achievements, university positions and professional preferences;
s2, analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee, and screening out the university professions conforming to the condition of the examinee;
S3, acquiring the university professions meeting the condition of the examinee, and recording data of the college university in the past year according to the university professions;
s4, deducing the recording probability of the university profession conforming to the condition of the examinee according to the recording data;
s5, analyzing trend data of current and future employment markets according to the university professions, and predicting employment prospects of the university professions by combining the professional preferences and the trend data;
s6, sorting the screened university professions by combining the admission probability and the trend data to generate a recommendation scheme;
and S7, displaying the generated recommended scheme to a study end and a parent end.
As described in the above steps S1-S2, by acquiring the personal information, the basic information of the examinee is known according to the personal information, and the personal information of the examinee including the examination score, the literature and science category, the area code, the contact information, etc. can be collected through the data interface provided by the examinee input or education examination mechanism. This information provides the underlying data for subsequent analysis. The method comprises the steps of intelligently analyzing the score of the examinee by adopting a machine learning algorithm (such as a decision tree, a neural network and the like), and predicting the university level which can be reached by the examinee by combining the education level of the region where the examinee is located, the score line recorded in the past year and other factors. And screening out the institutions and the professions thereof which possibly meet the conditions of the examinees by combining the professional preferences of the examinees, the location preferences of the institutions and the prediction results of the last step. The machine learning algorithm is used for analyzing the score of the examinee, so that the level of the institution possibly reached by the examinee can be predicted more accurately, and the accuracy of recommendation is improved. And the university professions meeting the conditions are screened according to the preference of the examinee, so that the search range is reduced, and the pertinence and the efficiency of recommendation are improved.
And (3) acquiring data such as score lines of historical years and the number of persons recorded in the related institutions and professions through the related databases as described in the steps S3-S4. These data can be used for subsequent probability prediction of admission. Statistical methods such as regression analysis, logistic regression and the like can be utilized to predict the probability of professional admission of the screened institution of the examinee by combining the score of the examinee with the data of the admission in the past years. Predicting the probability of the screened academic profession admission of the examinee is helpful for the examinee to know own admission opportunities and improves the accuracy of filling the volunteers.
As described in the above step S5, employment market related data such as industry employment rate, average salary, etc., and professional-to-professional matching degree data are collected. These data can be used to predict employment prospects for different professions. And estimating the future employment situation of each specialty through a prediction model by combining the specialty preference and employment trend data. These predicted outcomes may provide a reference for the test taker. Knowing the employment prospect of different professions helps the examinee to make a more intelligent decision and improves the knowledge of future professional planning. And combining professional preference and employment trend data, future employment condition prediction of each professional is provided for the examinee, and the examinee is helped to know the future employment prospect of the selected profession.
And (3) comprehensively scoring the two indexes of the admission probability and the employment prospect according to a certain weight, and sequencing the professions of the universities and the colleges as described in the steps S6-S7. The ordered list can be provided as a recommendation to the examinee and parents. The recommended solution can be presented to the examinee and the parents in an easy-to-understand manner through a platform such as a Web application and a mobile application. The recommended proposal can comprise the information of names of institutions, professions, admission probabilities, employment prospects and the like, and is used for examinees and parents to refer and decide.
Specifically, by acquiring personal information of the examinee and analyzing and modeling by adopting a personalized learning algorithm, the university profession conforming to the condition of the examinee can be screened out according to factors such as the score of the examinee, the position of the university and professional preference. Compared with the traditional method based on historical recorded data, personalized recommendation can be more accurately recommended according to the characteristics and requirements of examinees, and the accuracy and practicability of recommendation are improved. By matching the recording data of the college and university, the recording probability of the university profession meeting the condition of the examinee can be deduced according to the personal information of the examinee and the professional of the university. The time sequence analysis can better reflect the dynamic change of the recording probability, so that the recommendation result has more reference value. And by combining trend data of the current and future employment markets and predicting employment prospects of the universities and universities, more comprehensive information and more accurate recommended schemes can be provided for examinees. The traditional method only focuses on the score of the examinee and the admission situation of the universities and the universities, but ignores the change of employment market and professional employment prospect, and the method can comprehensively consider a plurality of factors and provide more practical recommendation results. The generated recommendation scheme is displayed to the examinee and the parents, so that the examinee and the parents can be helped to better know the recording probability and employment prospect of the university profession conforming to the conditions of the examinee. The recommendation scheme not only provides decision references, but also can increase the confidence of examinees and parents in selecting departments and professions, and reduces risks caused by information asymmetry.
In a possible embodiment, it is assumed that there is a senior graduate who wishes to obtain a personalized recommendation when filling college entrance examination volunteers. Specific examples: personal information: achievement: the total college entrance examination of Xiaoming is divided into 600 minutes, 120 minutes in Chinese, 150 minutes in mathematics, 140 minutes in English, 90 minutes in heddle management and 100 minutes in heddle management. Institution location: the location of the institution where the reading is expected to be in the Ming's mind is Beijing city. Professional preference: the Ming's interest in both computer science and business management is relatively high. Time series data: data recorded for college entrance examination over the years: the system can infer the small and bright admission probability according to the analysis of the admission data of the college entrance examination in the past year. Employment market trend: current employment market data: the system analyzes trend data of the current employment market, and considers employment prospects of various professions. Parameter setting: recording probability weights: the system can give different weights to the score and total score of different orders according to the data recorded in the past year so as to reflect the small-scale recording probability. Employment prospect evaluation weight: the system can give different weights to the employment prospects of different professions according to employment market trend data so as to evaluate the employment prospects of professions selected in the Ming dynasty. Based on the above information and parameter settings, the system may do the following: and analyzing the score and professional preference of the small and bright, and establishing a model according to a personalized learning algorithm. Matching the data of the college entrance examination in the past year, and deducing the probability of recording the professions of the university of Beijing in the Ming dynasty. And analyzing the current employment market trend, and predicting the employment prospect of two professions, namely computer science and industry and commerce management. And ordering two professions of computer science and business management in Beijing university of Ming according to the admission probability weight and employment prospect evaluation weight. Generating a recommendation, for example: recommendation scheme one: university 1 computer science specialty, admission probability is 80%, employment prospect is evaluated to be excellent. And a recommendation scheme II: university 2 industry and commerce management profession, the admission probability is 75%, and the employment prospect is evaluated to be good.
In one embodiment, the deducing the admission probability of the institution profession meeting the condition of the examinee according to the admission data comprises:
acquiring the recording data of colleges and universities of colleges and the successful recording examinee information of the calendar year;
extracting first characteristics of successful recording examinee information in the past, wherein the first characteristics comprise subject achievements, superfine projects and volunteer filling sequences;
analyzing reference data between the first characteristic of the calendar year examinee and the profession of the institution;
extracting personal information of the examinee, and analyzing the similarity between the personal information of the examinee and the reference data through the preset similarity comparison method;
based on a plurality of groups of the similarity, extracting a plurality of groups of successful parameter groups with higher similarity;
and predicting the probability of the current professional admission of the examinee by the universities and colleges according to the success parameter set.
As described above, it is necessary to collect data recorded in each institution over the years and information of the examinee who successfully recorded over the years. Such information includes score lines, numbers of persons to be recorded, information of the examinees to be recorded, etc. of each professional of each institution. By analyzing the examinee information successfully recorded in the past year, key features closely related to the recorded are extracted, such as discipline achievements, special long projects, volunteer filling sequences and the like. Matching the first characteristics of successful examinees in the past year with the recorded information among the professions of the institutions, analyzing the relativity and influence factors among the professions of different institutions and the first characteristics of the examinees, and establishing a relativity model and rules. Comparing the personal information of the current examinee with the first characteristics of the successful examinee in the past year, and analyzing the similarity between the personal information of the current examinee and the successful examinee in the past year based on a preset similarity comparison method. And analyzing the multiple groups of similarity comparison results, and screening out multiple groups of data with higher similarity as success parameter groups of the prediction model. Predicting the probability of professional admission of the current examinee by the universities according to the information of the successful examinee in the past year and the personal information of the current examinee. The predictive model may be built and optimized using machine learning, statistics, and the like. The personal information of the current examinee is extracted, and compared and analyzed with the successful examinee in the past year, the successful parameter group with higher similarity can be obtained. The method is helpful for providing personalized school selection and volunteer guidance for the examinee, recommending proper institutions and professions according to the characteristics and conditions of the examinee, and improving the admission opportunity of the examinee. By collecting and analyzing the data of the colleges and universities of the calendar year and the information of successful examinees and establishing a prediction model, scientific admission reference bases and decision support can be provided for the recruitment departments, education institutions and examinees. This helps the recruitment departments to better formulate the recruitment plan, the educational institutions provide targeted training and instruction, and the examinees make more rational and scientific choice, school and filling volunteer decisions.
Referring to fig. 2, in an embodiment, according to the personal information of the examinee, analyzing and modeling the personal information by a personalized learning algorithm, screening the university profession meeting the condition of the examinee includes:
s21, converting personal information of the examinee into numerical characteristics, and enabling the numerical characteristics to belong to a trained personalized learning model;
s22, analyzing and modeling through the personalized learning model, and determining a batch line of the numerical characteristic in the region;
s23, outputting the university professions with the numerical characteristics conforming to the batch line to obtain the university professions with screening conforming to the examinee conditions.
As described in the above steps, personal information of the examinee, such as subject score, specialty item, volunteer filling order, etc., is converted into a numerical feature. For example, the subject performance may be converted to a score, the specialty item to a binary variable, and the volunteer fill order to a ranking number. The personalized learning model is a model obtained by learning and modeling through training data, and can be analyzed and predicted according to the input numerical characteristics. This step inputs the numerical features into a trained personalized learning model; the personalized learning model analyzes the personal condition of the examinee according to the historical data and the input numerical characteristics, and predicts the recording probability of the examinee on different batches of lines in the region. The method comprises the steps of analyzing and modeling the digital characteristics based on a personalized learning model to obtain the admission probability of the examinee on each batch line in the region. And screening out the university professions of which the numerical characteristics of the examinees accord with the batch lines of the region according to the analysis result of the personalized learning model. The method comprises the steps of matching a prediction result of a personalized learning model with recorded data of an institution, finding out the specialized of the institution meeting the condition of an examinee (numerical characteristics meet batch lines), and outputting the specialized of the institution. And screening out the university professions meeting the condition of the examinee as output according to the result of the personalized learning model. Therefore, personalized school advice can be provided, and the examinee is helped to select the university profession conforming to the self conditions more accurately.
In one embodiment, the analyzing trend data of current and future employment markets according to the professions of the institutions, and predicting employment prospects of the professions of the institutions in combination with the professional preferences and trend data, includes:
collecting employment data of the university profession in the employment market according to the university profession, wherein the employment data comprises employment rates and employment growth rates of the university profession in a plurality of industries;
analyzing and generating demand data of the profession of the institution in the industry according to the employment rate and the employment growth rate;
generating matching data according to the matching degree of professional skills of the profession of the universities and current employment and future employment;
and forecasting the employment prospect of the university profession based on the demand data and the matching data.
As described above, employment data provided by schools and related institutions is collected, and is collated and analyzed to obtain employment rates and employment growth rates of the university professions in various industries. The data can reflect the current and future employment market demands, and basic data support is provided for subsequent analysis; and according to employment data obtained in the first step, combining factors such as development trend, policy, market demand and the like of related industries, and further analyzing and generating demand data of the university profession in each industry. This step may help predict employment prospects of the institution profession in different industries, providing directional references for subsequent analysis. And matching the professional skills of the universities and the professional market demands in the current and future to obtain matching data. And predicting employment prospects of the universities and universities according to the matching data and the demand data, wherein the employment prospects comprise indexes such as employment rates, employment growth rates and the like. The step can help the examinee and the education institution to evaluate the employment prospect of the profession of the institution more accurately, provide better professional planning and selection guidance for the students, and provide reference for the education institution to optimize the professional setting. By analyzing the employment rate, employment growth rate and other data of the university profession in the employment market and combining the industry development trend and the requirements, the employment prospect of the university profession can be predicted more accurately. Therefore, students and educational institutions can be helped to carry out more scientific professional selection, and accuracy of the professional selection of the institutions is improved. By predicting the employment prospect of the university profession, more accurate professional planning and selection guidance can be provided for students, and the students can be helped to better plan careers. Thus, the students can be prevented from blindly following wind and blindly selecting professions, and the employment competitiveness and professional development level of the students are improved.
In an embodiment, the method further comprises:
acquiring change data of the profession of the universities in the employment market in real time;
and periodically updating the employment data according to the change data.
As described above, the change data of the profession of the institution in the employment market is obtained in real time, and the employment data is updated periodically according to the change data. Therefore, the change trend and the demand of the current employment market can be reflected more accurately, and more accurate data support is provided for subsequent analysis and prediction. Meanwhile, professional settings can be timely adjusted and optimized, and employment competitiveness and adaptability of students are guaranteed.
In an embodiment, the method further comprises:
establishing a behavior analysis system, and collecting behavior data of an examinee in a volunteer form filling process through the behavior analysis system, wherein the behavior data comprise clicking behaviors, residence time and search keywords;
preprocessing the behavior data, and dividing the preprocessed behavior data into different group data through a clustering algorithm;
optimizing the personalized algorithm through the population data.
As described above, by establishing the behavior analysis system, behavior data of the examinee in the volunteer filling process is collected, including click behavior, residence time, search keywords and other information. These behavioral data reflect the preferences and needs of the test taker in selecting professions and can provide data support for subsequent personalized algorithms. Preprocessing the collected behavior data, including data cleaning, data conversion and other operations, and then dividing the preprocessed behavior data into different group data through a clustering algorithm. Through the division of the group data, differences and similar points among different groups can be found, and basic data support is provided for a subsequent personalized algorithm. And optimizing the personalized algorithm according to the different group data. Specifically, different personalized algorithms can be designed for each group according to the preferences and requirements of different groups so as to meet the personalized requirements of different groups. Therefore, the individuation degree of the volunteer filling system can be improved, and the requirements and willingness of examinees can be better met. Through collecting the behavior data of the examinee in the process of filling the volunteers, preprocessing the data and dividing groups, and finally designing different personalized algorithms, the personalized degree of the volunteer filling system can be improved. Therefore, personalized requirements and willingness of the examinee can be better met, and satisfaction of the examinee is improved. By establishing a behavior analysis system and processing behavior data, the selection preference and the requirement of the examinee can be known more accurately. Meanwhile, through division of group data and optimization of a personalized algorithm, volunteer selection of examinees can be predicted more accurately, and accuracy of a volunteer filling system is improved.
In one embodiment, after predicting the probability that the candidate is recorded by the institution specialty according to the success parameter set, the method further includes:
carrying out quantitative analysis on competition degree of the recorded data in the past year;
determining a time sequence factor according to the data of the competition degree recorded in the past year;
adding the time sequence factors to the reference data;
and updating the probability of predicting the professional admission of the examinee by the universities and colleges based on the time series factors.
As described above, the recording data of the past year is collected, including the score, ranking, professional volunteer, and other information of the examinee. Then, the ratio of the number of entries to the number of candidate entries for each specialty can be calculated as an indicator of the degree of competition. The degree of competition may be measured using a similar ratio of head-space to head-space or other statistical indicator. Through analysis of the calendar data, competition level data of each specialty can be obtained. Time series factors may include annual changes in the recruitment plan, socioeconomic factors, policy adjustments, etc. These factors may affect the competitive extent and probability of recording. By analyzing the calendar data and the related data, it is possible to determine which factors have a large influence on the recording probability and take it into consideration as time series factors. When predicting the recorded probability of the examinee, the time sequence factor needs to be considered in addition to the success parameter set. The time series factors in the calendar data can be combined with the personal information of the examinee to form complete reference data. When predicting the recorded probability of the candidate, a model can be established by using a machine learning algorithm, such as logistic regression, decision trees, and the like, and combining the successful parameter set and the time sequence factors. By inputting personal information and time sequence factors of the examinee, a probability prediction result of the examinee being recorded can be obtained. For example, one year may compete more strongly, while another year is relatively loose. Thus, when building the predictive model, the introduction of time series factors may be considered to better reflect the dynamic changes in the recording probability.
According to the auxiliary recommendation method for the college entrance examination volunteer filling scheme, personal information of the examinee is acquired, and analysis and modeling are carried out by adopting a personalized learning algorithm, so that the university profession meeting the condition of the examinee can be screened out according to factors such as the score of the examinee, the position of the university and professional preference. Compared with the traditional method based on historical recorded data, personalized recommendation can be more accurately recommended according to the characteristics and requirements of examinees, and the accuracy and practicability of recommendation are improved. By matching the recording data of the college and university, the recording probability of the university profession meeting the condition of the examinee can be deduced according to the personal information of the examinee and the professional of the university. The time sequence analysis can better reflect the dynamic change of the recording probability, so that the recommendation result has more reference value. And by combining trend data of the current and future employment markets and predicting employment prospects of the universities and universities, more comprehensive information and more accurate recommended schemes can be provided for examinees. The traditional method only focuses on the score of the examinee and the admission situation of the universities and the universities, but ignores the change of employment market and professional employment prospect, and the method can comprehensively consider a plurality of factors and provide more practical recommendation results. The generated recommendation scheme is displayed to the examinee and the parents, so that the examinee and the parents can be helped to better know the recording probability and employment prospect of the university profession conforming to the conditions of the examinee. The recommendation scheme not only provides decision references, but also can increase the confidence of examinees and parents in selecting departments and professions, and reduces risks caused by information asymmetry.
Referring to fig. 3, an auxiliary recommendation system for a college entrance examination volunteer filling scheme is further provided in the embodiment of the present application, including:
an acquisition module 1, configured to acquire personal information of an examinee, where the personal information includes a score, an institution location, and a professional preference;
the screening module 2 is used for analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee and screening out the university professions conforming to the condition of the examinee;
a matching module 3, which is used for acquiring the university professions conforming to the condition of the examinee, and recording data for the calendar year college entrance according to the university professions;
the inference module 4 is used for inferring the admission probability of the university profession conforming to the condition of the examinee according to the admission data;
the analysis module 5 is used for analyzing trend data of the current and future employment market according to the professions of the universities and predicting employment prospects of the professions of the universities by combining the professional preference and the trend data;
the generation module 6 is used for combining the admission probability and the trend data, sorting the screened university professions and generating a recommendation scheme;
and the display module 7 is used for displaying the generated recommended scheme to the study end and the parent end.
As described above, it may be understood that each component of the auxiliary recommendation system for college entrance examination volunteer filling schemes set forth in the present application may implement the function of any one of the auxiliary recommendation methods for college entrance examination volunteer filling schemes set forth above, and the specific structure is not repeated.
Referring to fig. 4, a computer device is further provided in the embodiment of the present application, where the computer device may be a server, and the internal structure of the computer device may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing data such as monitoring data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to implement an auxiliary recommendation method for college entrance examination volunteer reporting schemes.
The processor executes the auxiliary recommendation method for the college entrance examination volunteer filling scheme, which comprises the following steps: obtaining personal information of an examinee, wherein the personal information comprises achievements, university positions and professional preferences; analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee, and screening out the university professions conforming to the condition of the examinee; acquiring the university profession conforming to the condition of an examinee, and recording data of the calendar year college entrance according to the university profession; deducing the admission probability of the university profession conforming to the condition of the examinee according to the admission data; analyzing trend data of current and future employment markets according to the university professions, and predicting employment prospects of the university professions by combining the professional preferences and the trend data; sorting the screened university professions by combining the admission probability and the trend data to generate a recommendation scheme; and displaying the generated recommended scheme to the student end and the parent end.
According to the auxiliary recommendation method for the college entrance application filling scheme, personal information of the examinee is acquired, and the personalized learning algorithm is adopted for analysis and modeling, so that the university profession meeting the condition of the examinee can be screened out according to factors such as the score of the examinee, the position of the university and professional preference. Compared with the traditional method based on historical recorded data, personalized recommendation can be more accurately recommended according to the characteristics and requirements of examinees, and the accuracy and practicability of recommendation are improved. By matching the recording data of the college and university, the recording probability of the university profession meeting the condition of the examinee can be deduced according to the personal information of the examinee and the professional of the university. The time sequence analysis can better reflect the dynamic change of the recording probability, so that the recommendation result has more reference value. And by combining trend data of the current and future employment markets and predicting employment prospects of the universities and universities, more comprehensive information and more accurate recommended schemes can be provided for examinees. The traditional method only focuses on the score of the examinee and the admission situation of the universities and the universities, but ignores the change of employment market and professional employment prospect, and the method can comprehensively consider a plurality of factors and provide more practical recommendation results. The generated recommendation scheme is displayed to the examinee and the parents, so that the examinee and the parents can be helped to better know the recording probability and employment prospect of the university profession conforming to the conditions of the examinee. The recommendation scheme not only provides decision references, but also can increase the confidence of examinees and parents in selecting departments and professions, and reduces risks caused by information asymmetry.
An embodiment of the present application further provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements an auxiliary recommendation method for college entrance examination volunteer filling schemes, including the steps of: obtaining personal information of an examinee, wherein the personal information comprises achievements, university positions and professional preferences; analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee, and screening out the university professions conforming to the condition of the examinee; acquiring the university profession conforming to the condition of an examinee, and recording data of the calendar year college entrance according to the university profession; deducing the admission probability of the university profession conforming to the condition of the examinee according to the admission data; analyzing trend data of current and future employment markets according to the university professions, and predicting employment prospects of the university professions by combining the professional preferences and the trend data; sorting the screened university professions by combining the admission probability and the trend data to generate a recommendation scheme; and displaying the generated recommended scheme to the student end and the parent end.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided herein and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Claims (10)
1. An auxiliary recommendation method for a college entrance examination volunteer filling scheme, the method comprising:
obtaining personal information of an examinee, wherein the personal information comprises achievements, university positions and professional preferences;
Analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee, and screening out the university professions conforming to the condition of the examinee;
acquiring the university profession conforming to the condition of an examinee, and recording data of the calendar year college entrance according to the university profession;
deducing the admission probability of the university profession conforming to the condition of the examinee according to the admission data;
analyzing trend data of current and future employment markets according to the university professions, and predicting employment prospects of the university professions by combining the professional preferences and the trend data;
sorting the screened university professions by combining the admission probability and the trend data to generate a recommendation scheme;
and displaying the generated recommended scheme to the student end and the parent end.
2. The aided recommendation method for a college entrance volunteer filling program of claim 1, wherein said deducing from said enrollment data an enrollment probability of an institution specialty meeting an examinee's condition comprises:
acquiring the recording data of colleges and universities of colleges and the successful recording examinee information of the calendar year;
extracting first characteristics of successful recording examinee information in the past, wherein the first characteristics comprise subject achievements, superfine projects and volunteer filling sequences;
Analyzing reference data between the first characteristic of the calendar year examinee and the profession of the institution;
extracting personal information of the examinee, and analyzing the similarity between the personal information of the examinee and the reference data through the preset similarity comparison method;
based on a plurality of groups of the similarity, extracting a plurality of groups of successful parameter groups with higher similarity;
and predicting the probability of the current professional admission of the examinee by the universities and colleges according to the success parameter set.
3. The aided recommendation method for a college entrance application program according to claim 1, wherein the step of analyzing and modeling the personal information according to the personal information of the examinee by a personalized learning algorithm to screen out the university profession conforming to the examinee's condition comprises the steps of:
converting the personal information of the examinee into numerical characteristics, and enabling the numerical characteristics to belong to a trained personalized learning model;
analyzing and modeling through the personalized learning model, and determining a batch line of the numerical feature in the region;
and outputting the university professions with the numerical characteristics conforming to the batch line to obtain the university professions with the screening conforming to the examinee conditions.
4. The aided recommendation method for a college volunteer filling program according to claim 1, wherein said analyzing trend data of current and future employment markets according to the profession of the college, and predicting employment prospects of the profession of the college in combination with the professional preferences and trend data, comprises:
collecting employment data of the university profession in the employment market according to the university profession, wherein the employment data comprises employment rates and employment growth rates of the university profession in a plurality of industries;
analyzing and generating demand data of the profession of the institution in the industry according to the employment rate and the employment growth rate;
generating matching data according to the matching degree of professional skills of the profession of the universities and current employment and future employment;
and forecasting the employment prospect of the university profession based on the demand data and the matching data.
5. The auxiliary recommendation method for college entrance examination volunteer filling schemes according to claim 4, further comprising:
acquiring change data of the profession of the universities in the employment market in real time;
and periodically updating the employment data according to the change data.
6. The auxiliary recommendation method for college entrance examination volunteer filling schemes according to claim 1, further comprising:
Establishing a behavior analysis system, and collecting behavior data of an examinee in a volunteer form filling process through the behavior analysis system, wherein the behavior data comprise clicking behaviors, residence time and search keywords;
preprocessing the behavior data, and dividing the preprocessed behavior data into different group data through a clustering algorithm;
optimizing the personalized algorithm through the population data.
7. The aided recommendation method for a college entrance aspiration filling program according to claim 2, further comprising, after predicting the probability of the subject being enrolled by an institution professional according to the success parameter set:
carrying out quantitative analysis on competition degree of the recorded data in the past year;
determining a time sequence factor according to the data of the competition degree recorded in the past year;
adding the time sequence factors to the reference data;
and updating the probability of predicting the professional admission of the examinee by the universities and colleges based on the time series factors.
8. An auxiliary recommendation system for college entrance examination volunteer filling programs, comprising:
the acquisition module is used for acquiring personal information of the examinee, wherein the personal information comprises achievements, university positions and professional preferences;
The screening module is used for analyzing and modeling the personal information through a personalized learning algorithm according to the personal information of the examinee and screening out the university professions conforming to the condition of the examinee;
the matching module is used for acquiring the university professions conforming to the condition of the examinee and carrying out matching on the calendar year college entrance data according to the university professions;
the inference module is used for inferring the recording probability of the university profession conforming to the condition of the examinee according to the recording data;
the analysis module is used for analyzing trend data of the current and future employment market according to the university profession and predicting employment prospects of the university profession by combining the professional preference and the trend data;
the generation module is used for combining the admission probability and the trend data, sorting the screened university professions and generating a recommendation scheme;
and the display module is used for displaying the generated recommended scheme to the study end and the parent end.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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