# Employment Recommendation for Education Talents Based on Big Data Precision Technology

Authors: Xiuqin Jiang; Darchia Maia
Journal: Security and Communication Networks
 (2022)

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## Abstract

The cornerstone of future social education is the development of skills. This work enhances the big data algorithm and presents an effective association algorithm that may boost relevance in order to improve the accuracy of educational talent employment. Furthermore, this paper employs data mining association analysis technology to identify educational talent attributes that are disproportionately important in determining the problem of student employment, as well as to eliminate attributes that are unrelated to the problem, in order to achieve the goal of screening educational talent attributes. In addition, this paper combines an improved algorithm to build an education talent employment recommendation system, builds a system function structure based on the actual situation, and combines experimental research to verify the effect of the model built in this paper. From the experimental research, it can be seen that the education talent employment recommendation system based on big data precision technology constructed in this paper can play an important auxiliary role in the education talent employment recommendation.


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## Body

## 1. Introduction

With the increasing downward pressure of China’s socialist market economy and the adverse effects of the international economic situation, the popularization of higher education has prompted the continuous expansion of college enrollment, which has resulted in many problems such as difficulties for graduates to find employment.Obtaining employment information is the basis for graduates’ employment. The employment information channels for college graduates in my country generally include: the recruitment market, various job fairs held by the employment service center, introductions by private intermediaries, internship and practice units providing internship opportunities, college campus recruitment, and employment information platform [1]. Therefore, graduates need to extract effective information from the huge recruitment information, which causes the students to spend more time and energy costs. From the perspective of the overall employment success rate, students have a greater degree of blindness in obtaining recruitment information and participating in the recruitment process [2].The fast development of Internet technology has resulted in an explosion of data. The problem of individuals finding it difficult to extract trustworthy and usable information from huge volumes of data in a succinct and effective manner is known as information overload [3]. Recruitment information, as a kind of internet information, is widely used in the Internet. There are many recruitment and employment information websites, and it is difficult to distinguish between true and false. There are many well-known employment websites, such as Zhaopin Recruitment, Chinese Talents, 51job, and 58.com. When college students who are about to graduate want to listen to a lecture, or job seekers want to check a certain type of job, they also need to check on each school or third-party employment website, and on each different employment website. Registering and filling in resumes are often repetitive and time-consuming tasks, which are very troublesome. For information overload, commonly used solutions are: information retrieval and information filtering. Among them, the results of information retrieval are generalized and will not be changed by people with different external and internal factors, which is not enough to meet the needs of individualization and cannot further enhance the user experience. Information filtering can automatically formulate filtering rules based on user needs and current environment and other internal and external factors, which is an information processing method that can further enhance user experience. The commonly used information filtering method is a personalized recommendation. Therefore, in the processing of employment information, it is necessary to make full use of the two methods of information retrieval and information filtering to provide job seekers with real and useful information as concisely and efficiently as possible.This article combines big data technology to analyze the employment recommendation of education talents and constructs a corresponding data model to improve the employment recommendation effect of education talents.


## 2. Related Work

Undergraduates’ recognition of their majors is directly related to their interest and enthusiasm for studying in university, which in turn affects their professional value and learning effect. Understanding the status quo of pedagogical students’ identification with their majors can help them better their professional learning and development [4]. At the same time, improving the professional identity of pedagogy students will also help to improve the training model of pedagogy talents, and promote the improvement of student learning effectiveness and employment quality [5]. Literature [6] pointed out that at present, undergraduates majoring in education generally do not have a high degree of recognition of their major, which is mainly manifested in the aspects of low professional emotion, low professional learning investment, and low professional identification. For the problems of professional identity of undergraduates majoring in education, there are both pedagogical majors and students’ own reasons. According to the literature [7], the total score of professional identification of undergraduates majoring in education, as well as the average score of each dimension, are in the lower-middle range, and it compared the professional identification of students across several dimensions, including gender, grade, and voluntary choice. Professional identity is separated into three subdimensions in the literature [8]: cognition, emotion, and perseverance. Undergraduates studying in education have a specific professional identity that influences their mental health, professional growth, and job search, among other things. It also affects the status of pedagogy as a discipline and the quality of higher education. The literature [9] believes that compared with other disciplines, the employment rate of undergraduates majoring in education is low, graduates are not highly related to occupations and professions, and the overall employment situation of undergraduates majoring in education is not optimistic. The literature [10] pointed out that the employment rate of undergraduates majoring in education is low, the employment situation is severe, and the employment prospects are not optimistic. According to reports, the salaries of undergraduates majoring in education are lower than those of other majors. In terms of job selection, graduates prefer public primary and secondary schools and educational administrative agencies [11]. There are many reasons for the difficulty of undergraduates majoring in education. The main reasons are the continuous expansion of higher education in our country and the backward training mechanism of undergraduate talents in education. The literature [12] believed that pedagogy is a specialty of higher normal colleges, but the employment situation of pedagogy has been very severe in recent years. The literature [13] analyzed the reasons for the difficulty of undergraduates majoring in education from three aspects: society, school, and students.


## 3. Big Data Accurate Recommendation Algorithm

Although association rules are the end outcome of all association rule mining and the manifestation of the results, regular item set mining must be done before mining association rules. The generation of frequent item sets is an indispensable prerequisite for association rule mining. The following describes the related concepts of frequent item sets one by one. We setI to be the set I=i1,i2,…,in of items, assuming that there are n items in total when mining frequent item sets.Definition 1.
Transaction.
Ift is a transaction, then t must satisfy: t⊆I, and t is a nonempty subset of item set I [14].Definition 2.
Transaction database.
The transaction database is a database composed of a series of transactions. If the number of transactions in a transaction databaseD is m, then the transaction database can be written as D=t1|t1⊆I,i=1,2,…,m.Definition 3.
sup (support).
The support of an item set IS (item set) can be expressed as the ratio of the number of transactions containing the item set in the databaseD to the total number of transactions in the database. D1 is any subset of D [15]:(1)supIS=counterDcounter=D1.
The frequency of frequent item sets IS in the database is greater than the minimum support threshold set by the user, so IS is called frequent item sets. We assume that the minimum support threshold set by the user ismin_sup0≤min_sup≤1. If supIs≥min_sup, the item set IS can be considered as a frequent item set.
In the mining of frequent item sets, the Apriori method was first invented by Agrawal, which is based on the nature of frequent item sets.
Nature: All nonempty subsets of any frequent item set must also be frequent item sets, and all supersets of infrequent item sets must also be infrequent item sets.
The Apriori algorithm uses this property to first generate a frequentk item set of a data set, and then generate a candidate k + 1 item set on the basis of this frequent k item set. Then, it searches the data set, finds the specific support of all candidate k + 1 item sets, and then finds frequent k + 1 item sets, and iterates until no new frequent item sets are generated.
Association rules are expressions of the formA ⟶ B, where A and B are both nonempty subsets of item set I, and A and B must satisfy A∩B=∅, that is, the intersection of A and B is an empty set. In most of the previous literature and the research on association rules in this paper, A and B are further restricted [16].
IfC=A∪B, then we stipulate that A ⟶ B is an association rule. The premise is that C must be a frequent item set, that is, supC≥min_sup. Therefore, it can be obtained from the property that both A and B are frequent item sets. In fact, not all rules of this form are useful, and the most basic association rules must also be satisfied: the confidence of the association rules must reach a certain value. In most studies, the confidence of association rules is required to reach a certain value, that is, its confidence must not be less than the minimum confidence threshold. The definition of the confidence (conf (A ⟶ B)) of association rule A ⟶ B is given below [17]:(2)confA⟶B=supA∪BsupA.
If an association ruleA ⟶ B is an accepted association rule, then we can conclude from this rule: when A occurs, then pattern B will occur with the probability conf (A ⟶ B).
We always pay attention to the frequency of association rules in the whole transaction set while constructing association rules. Support is used to gauge this. Secondly, we are concerned about the credibility of the rule, that is, the correct rate of the rule’s following is derived from the rule’s antecedents. This indicator is evaluated by the rule’s confidence. In practice, these two indicators alone cannot evaluate a rule well. In particular, confidence can sometimes give us an illusion. Next, we introduce the following examples [18]:
If we assume that the number of transactions in the entire database is 100, the number of transactions containing eventA is 95, the number of transactions containing event B is 65, the number of transactions containing events A and B is 50, and the minimum support threshold is 0.45, and the minimum confidence threshold is 0.75, then we can get from the confidence and support formulas, the patterns A, B, and AB are all frequent patterns, and we can get: confB⟶A=50/65=0.77>0.75. Therefore, we can accept the association rule B ⟶ A. However, we know that mode A’s individual support is 0.95. Now, we found a contradiction, that is, the occurrence of B not only did not increase the incidence of A, but also greatly reduced the incidence of pattern A. However, we mistakenly believe that the emergence of mode B promotes the emergence of mode A from the B ⟶ A association rule. Therefore, we find that the support and confidence association rule evaluation system is not so perfect. In view of this, many scholars have proposed various other evaluation criteria for association rules, but most of them add other evaluation indicators on the basis of support and confidence. In order to obtain more beneficial association rules, this paper also introduces other evaluation indicators of association rules based on previous studies. Another association rule evaluation index introduced in this paper is improved lift. The traditional rule lift index is expressed by the formula:(3)liftA⟶B=pABpApB.
Among them,AB represents the union of sets A and A∩B=∅, and. p (A), p (B), and p (AB), respectively, represent the probability of patterns A, B, and A∪B occurring in the data set.
For formula (3), we can understand the degree of promotion of mode A to mode B, that is, how much the occurrence of mode A can promote or improve the occurrence of mode B. Now, in turn, we consider the lift degree of mode B to mode A, and from formula (3) we can get the following formula [19]:(4)liftB⟶A=pBApBpA=pABpApB=liftA⟶B.
From formula (4), we can see that if formula (3) is used as the calculation formula of lift, then the lift of A to B is the same as the lift of B to A. Obviously, this is contrary to real-life experience. Therefore, many scholars have proposed a new lift measurement formula based on this lift formula. Taking this factor into account, a new lift calculation formula (newLift) is generally accepted and recognized as the formula.(5)newliftA⟶B=liftA⟶BliftA¯⟶B=pAB/pApBpA¯B/pA¯pBpABpA¯pA¯BpA.
In formula (5), pA¯ represents the probability of pattern A not occurring. Similarly, pA¯B represents the probability of a pattern that includes pattern B but does not include pattern A in the data set. From formula (5), we can obtain a relatively reasonable lift.
This article uses this calculation formula to evaluate the mutual promotion between the two modes. Unless otherwise specified, the lift mentioned in this article refers to the lift calculated by the formula (5). Like support and confidence, in order for the lift to play a certain role, a minimum lift threshold must be specified in advance, so as to filter out the association rules with particularly small lift [20].
Because the majority of data individuals come into touch with production and everyday life is time-related, mining time-related data is critical. When working with time-related data, there are two primary concepts to consider: the first is time. The first is to treat the data as streaming data, and then do mining on it; the second is to serialise the data as streaming data, and then process it as a time series. In order to mine the sequence of time-related data, this paper adopts the second method to process time-related data. The relevant definitions of time series are explained below. The form of the data flow is shown in Figure1. There are three streams in the picture.
If we assume that there aren data streams, each data stream is a unary time series. Therefore, there are n-ary time series. The combined set of multivariate time series can be defined as follows: if S=S1,S2,…,Si,…,Sn is assumed to be a collection of multiple time series, the value of a single time series Si at time point Tj can be expressed as Sij, and the value of multiple time series collections at time point Tj is Sj=S1j,S2j,…,Sij,…,Snj.
In this article, all the operations we do are based on the combined set of time series. In the multiple time series merge set, each set of observation values is assigned a globally unique transaction label TID. In the mining process, if only the association rules within the transaction are mined, then we will useD as the direct transaction set for mining. Yes, but such mining results have very limited guidance for practical applications because the association rules mined in this way are association rules between different sequences at the same time. In order to get the association rules between different sequences at different times, we must perform mining association rules across transactions [21].
Then, we can define the combined set of multivariate time series asD=S0,S1,…,Si,…,.
We assume that the set of all transactions can be expressed as the following set:ε=e10,e11,…,e2ω−1,e20,e21,…,e2ω−1,…,en0,en1,…,enω−1ε is a data set in a sliding window of the multivariate time series merge set D, and the size of the sliding window is ω. The sorting order of each time series is sorted according to the occurrence time of the event.
Mining the association rules between cross-transactions in a multivariate time series is essentially mining the association rules between distinct time points.
There are rulesA ⟶ B, where A and B are subsets of item sets, and the two sets do not have any nonempty common subsets, namely, A∩B=∅. If A ⟶ B is a cross-transaction association rule, then the following conditions must be met: ∀eij∈A,∀em∈B, i<m,∃j=0. The condition is to ensure that any item of the antecedents of the association rules between cross-transactions must occur before any item of the subsequents, and there must be an item with a transaction sequence number of 0 in the antecedents. In fact, from the analysis of the restriction conditions, as long as the latest item in the antecedent occurs earlier than the earliest item in the latter, it is enough.
In general association rule mining methods, we need to artificially set a subjective minimum support threshold min_sup. There are two extremes in the process of setting this parameter.
When the min_sup setting is too small, too many association rules may be mined, resulting in many invalid association rules being mined, and at the same time increasing the workload.
When min_sup is set too large, some valid ones may be missed, which may be the association rules we are interested in [22].
We have performed statistics on multiple data sets and found that the support count has a rough relationship with the number of frequent item sets generated under this support as shown in Figure2:
But many times it is difficult for us to set an appropriate min_sup value. At the same time, there are also various problems in the evaluation of min_sup. Some people propose that the number of frequent item sets mined should be the goal under the condition that min_sup is not too small. That is, the more association rules, the better. The average support count of frequent item sets mined under a certain min_sup condition is used as the evaluation criterion.
Both of these evaluation criteria have their advantages and disadvantages: First, if a large min_sup value is given in the mining process, the average support count of the association rules will be very large, and the mining efficiency will be very high. Because the mining results are all with support greater than or equal to min_sup, the overall average support count will be very high, and because min_sup is very large, only a few rules will be mined, and the time required will be relatively small. The biggest disadvantage of this evaluation standard is that it may miss some rules that have low support but may be meaningful. Secondly, if you set a relatively small min_sup value, although almost all meaningful rules can be obtained, the disadvantage is that the mining efficiency is very low.
It can be seen from Figure2 that the number of rules mined is in an inverse correlation with the minimum support threshold. Due to the positive correlation between mining efficiency and the lowest support threshold, mining efficiency and the number of rules mined might be inversely connected. As a consequence, we need to come up with a suitable assessment index that takes both mining efficiency and mining outcomes into consideration. According to the above two association rules evaluation criteria, this paper proposes an evaluation index such as formula.(6)fmin_sup=min_sup×lnconter.
This evaluation index takes into account the mining efficiency and the number of mining results, so it is more objective than the rule evaluation index proposed by previous scholars. After statistics of some data sets, it is found that the relationship between the value off (min_sup) and min_sup is in the form of Figure 3. All our purpose is to find the extreme points of the curve.
We can see from Figure3 that the relationship between the number of supports and the minimum support threshold can be roughly fitted with several inverse proportional lines of different proportions. Therefore, in order to fit this line, this paper proposes to use the negative high-order inverse proportional line to fit:(7)y=a0+a1x+a2x2+a3x3.
Among them,x represents the support threshold, and y represents the number of frequent item sets generated under this threshold.
In the process of finding the weight of each item, we can make the following transformations:(8)y1=x−1,(9)y2=x−2,(10)y3=x−3.
Therefore, by substituting (8)–(10) into (7), we can get the formula:(11)y=a0+a1y1+a2y2+a3y3.
Wheny0 is always equal to 1, the following formula can be obtained:(12)y=∑i=03aiyi.
The main task below is to find the coefficients of each item. We use the least square method to find the coefficients of each item. In the previous article, the least square method has been used when fitting a point with a straight line, so I will not introduce it here.
After obtaining the coefficients of the polynomial, we must maximizef (min_sup). Since we have obtained the relationship between the minimum support threshold and the number of association rules generated within this minimum support threshold through the above part, f (min_sup) can be substituted into formula (5) to obtain the following formula:(13)fmin_sup=lna0+a1min_sup+a2min_sup+a3min_sup×min_sup.
Therefore, the task now is to take the maximum value of formula (13) in the interval (0, 1), and formula (13) is the curve of min_sup. Therefore, we can use the rule of thirds that introduces random factors to find its extreme value.
Certain qualities of association rules may be utilised to remove certain inefficient association rules in advance, according to research into the process of generating association rules, in order to accomplish the goal of successfully improving the time efficiency of producing association rules. If it is assumed that the preset minimum support threshold is min_sup, and the preset minimum confidence threshold is min_conf, the following modes are set:
ABCDE assumes that ABCDE is a frequent pattern, then supABCDE⟶min_sup. The following is an improvement to the association rules generated by this model.
In other papers and methods related to frequent pattern generation association rules, any nonempty proper subset of frequent patterns must be tested. Therefore, the number of tests required is2len−2 times, where len is the pattern length of frequent patterns. However, after inference, it is found that there are many modes that can be determined without testing whether they meet the minimum support threshold rule. The following uses ABCDE as an example to explain.
If we assume that the rule:ABCD ⟶ E does not satisfy the minimum confidence, that is, confABCD⟶E<min_conf, then we can make an inference. We assume that the set S1 is any nonempty subset of the set ABCD, and the set S2 is the complement of S1 in the frequent pattern, that is, S2=ABCDE−S1. We can infer that for all S1 ⟶ S2, it does not meet the minimum confidence threshold, that is, confS1⟶S2<min_conf. Below, we will prove it:(14)confABCD⟶E=supABCDEsupABCD=counterABCDE/ncounterABCDE/n=counterABCDEcounterABCD≤min_conf,confS1⟶S2=supS1∪S2supS1.
BecauseS2 is the complement of S1 on the set {ABCDE}, then(15)Si∪S2=ABCDE.
Then, it can be obtained from formulas (14) and (15):(16)confS1⟶S2=supABCDEsupS1=counterABCDEcounterS1.
SinceS1 is any nonempty proper subset of the set {ABCD}, then we can know(17)counterS1≥counterABCDE.
Therefore, it can be obtained by formula (16) and formula (17):(18)confS1⟶S2=supABCDEsupS1≤counterABCDEcounterABCD≤min_conf.
Among them, conf (K ⟶ M) is the confidence of the association rule k ⟶ M, counter (S) is the support count of the set S in the database, n is the size of the transaction set, and Sup (S) is the support of the set (mode) S. Therefore, the conclusion is proved.
Efficiency inference: If the length of a frequent pattern isn, and the association rule generated from this frequent pattern is AR_NUM, then the number of times we can reduce the test is zn−2−AR_NUN. The statistical results show that as the length of the frequent pattern increases, the probability that the rule generated by the corresponding length can meet the condition decreases quickly, and the value of AR_NUM decreases rapidly as n increases.Figure 1
Basic window with a time length of 20.Figure 2
Statistical diagram of the minimum support threshold and the number of association rules.Figure 3
The statistical relationship between the minimum support threshold and the evaluation function.


## 4. Educational Talent Employment Recommendation System Based on Big Data Precision Technology

The recommendation system for college graduates designed in this paper provides two recommendation methods: individual optimal and global optimal. Individual optimum selection strategy is realised by the customised recommendation algorithm, and global optimal selection strategy is realised by the big data precision algorithm. During the use of the system, users can choose between these two recommended methods according to their own needs. The recommended process of this system is shown in Figure4.Figure 4
The process of the education talent employment recommendation system.The specific recommendation process is shown in Figure5. First of all, college graduates fill in the job resume information according to their own situation in this system. Secondly, users should make judgments and settings on employment recommendation methods based on the current employment environment. Finally, the system uses related recommendation algorithms to generate a recommendation list according to the set recommendation method and display it to the user.Figure 5
Function module diagram of education talent employment recommendation system.This post builds a student database using Microsoft’s business intelligence tools’ April correlation analysis module to pick student attributes that are highly relevant to the topic in this paper. It not only guarantees the objectivity of student attribute data analysis but also preliminarily guarantees the reliability, feasibility, and scientificity of problem research. Figure6 shows the road map for the establishment of the education talent employment recommendation system database.Figure 6
Roadmap for the establishment of the education talent employment recommendation system database.This paper uses data mining association analysis technology to find the student attributes that have a relatively large degree of relevance to the problem of determining student employment units, and also removes attributes that are not related to the problem to finally achieve the purpose of screening student data attributes. The core idea of the screening is: under a certain degree of support and confidence, apply business intelligence data mining tools to analyze the data corresponding to the many attribute values of the students and the attributes of the enterprise, respectively. Therefore, it is necessary to perform an association analysis to remove the attribute values that are not associated with the research object, as shown in Figure7.Figure 7
Attribute map of students screened by association algorithm.After constructing an education talent employment recommendation system based on big data precision technology, the system is tested. Moreover, this paper uses the relevant information of college education talents in 2020 to simulate employment recommendation and calculates the accuracy of employment recommendation, education talent satisfaction and employer satisfaction. The results are shown in Table1 and Figures 8 and 9.Table 1
Educational talent recommendation system based on large-scale data precision technology.
No.Employment recommendation accuracy rateEducational talent satisfactionEmployer satisfactionNo.Employment recommendation accuracy rateEducational talent satisfactionEmployer satisfaction189.091.986.13189.884.986.3282.590.386.63275.788.073.7379.479.685.93390.980.776.4485.191.188.93479.280.879.3576.588.489.83579.890.480.2689.892.588.93685.685.374.8782.185.486.93778.185.774.0881.889.778.03875.180.773.1989.779.384.43982.988.080.71085.681.278.64074.284.873.81175.592.576.74181.378.780.41285.682.276.14289.993.391.51374.589.890.34375.888.981.41474.291.188.24491.781.984.91574.494.473.34589.680.981.71685.180.887.34683.583.490.71791.289.273.94787.085.381.51880.382.980.54880.581.690.41990.485.475.74979.390.074.52090.785.674.15091.387.382.22181.492.486.05190.983.176.02276.590.884.35288.482.376.12388.091.792.95384.889.291.82485.083.780.05479.584.689.22576.285.273.45587.687.973.02675.292.973.65683.392.378.22781.387.993.05783.184.489.92875.892.473.35875.784.074.52976.084.892.95979.993.784.73088.784.891.06081.278.485.7Figure 8
Accuracy rate of education talent employment recommendation.Figure 9
Educational talent satisfaction and employer satisfaction.From the above research, it can be seen that the education talent employment recommendation system based on big data precision technology constructed in this paper can play an important auxiliary role in the education talent employment recommendation.


## 5. Conclusion

How to effectively synthesize the recruitment information of various employment websites and provide job seekers with the real and useful information they most need as efficiently and concisely as possible to enable job seekers to save more precious time for job search preparation is a problem that needs to be solved urgently. With the recent fast expansion in the number of students majoring in education at colleges and universities, the problem of undergraduates majoring in education at colleges and universities has always been a source of concern for all areas of society. This study offers a reciprocal employment recommendation algorithm that includes historical information on the school, based on the real employment condition of students majoring in education in colleges and universities. Moreover, this paper uses data mining association analysis technology to find student attributes that have a relatively large degree of relevance to the problem of determining student employment units and also removes attributes that are not related to the problem to finally achieve the purpose of screening student data attributes. Finally, this paper combines experimental research to verify the effect of the model in this paper. From the experimental research, it can be seen that the education talent employment recommendation system based on big data precision technology constructed in this paper can play an important auxiliary role in the education talent employment recommendation.



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