CN112381510A - Salary recommendation method based on machine learning - Google Patents

Abstract

The invention provides a machine learning-based salary recommendation method, which comprises the following steps: collecting and establishing a candidate sample library; for the ith candidate sample, reading the value of the compensation element before entry, the value of the compensation element after entry and the value of the number of the added items of the ith candidate; training the compensation model to obtain each factor influence factor so as to obtain a final compensation model; and calculating the salary of the candidate by adopting a salary model to obtain the amplitude salary and the characteristic salary of the candidate, and further finally obtaining the salary recommendation range. According to the salary recommendation method based on machine learning, provided by the invention, based on a salary model obtained by machine learning, the interference of human factors on salary is avoided; the compensation method is beneficial to different departments to reach the consistency of compensation of candidates; embodying the personal characteristics of the candidate; the operation is simple, and the full automation can be realized. Thereby improving the effectiveness and efficiency of the recruitment process.

Description

Salary recommendation method based on machine learning

Technical Field

The invention belongs to the technical field of machine learning application, and particularly relates to a compensation recommendation method based on machine learning.

Background

The compensation measurement and calculation of the current candidate are completely based on the statistical significance of market data, and the moderate expansion is controlled according to certain random factors; or passively according to the division of the company grades, determining the candidate meeting the conditions, which cannot completely reflect the individual characteristics of the candidate, so that more uncertain factors exist in the salary customization process of the candidate, the credibility of the salary customization result is reduced, and the requirement of the current unit for accurately formulating the salary of the candidate is difficult to meet.

Disclosure of Invention

Aiming at the defects in the prior art, the invention provides a salary recommendation method based on machine learning, which can effectively solve the problems.

The technical scheme adopted by the invention is as follows:

the invention provides a machine learning-based salary recommendation method, which comprises the following steps of:

step 1, collecting and establishing a candidate person sample library; the candidate sample library comprises a plurality of candidate samples, and any ith candidate sample is represented as: f_i＝{(f(C_i),N_i,f(S_i)}；

Wherein:

f(C_i) Represents the pre-entry compensation element C of the ith candidate_iValue f (C) of_i)；

N_iThe number of bonus items representing the ith candidate;

f(S_i) Represents the post-entry compensation element S of the ith candidate_iValue f (S) of_i)；

Step 2, dividing candidate samples in a candidate sample library into a training set and a testing set; wherein the training set has m₁A sample of individual candidates; test set has m₂A sample of individual candidates;

step 3, for m₁The individual candidate sample performs the following compensation model training process:

step 3.1, changing i to 1;

setting initial values, including: setting the 1 st market element

Value of

Market factor 2

Value of

Index element E of academic calendar_iValue f (E) of_i) Adding the value added by the item Q_iValue of (A) f (Q)_i) And a factor V of the result of investigation_iValue f (V) of_i)；

Step 3.2, for the ith candidate sample, reading the pre-entry compensation element C of the ith candidate_iValue f (C) of_i) And the compensation element S after work_iValue f (S) of_i) Number of bonus items N_iAnd company subscription element M_iValue f (M)_i) (ii) a Wherein, company subscribes element M_iValue f (M)_i) Is a known fixed value;

step 3.3, adopting an adjusting algorithm to examine the result element V_iValue f (V) of_i) Adjusting to obtain an element V of the investigation result_iAnd finally obtaining a survey result element V_iAverage value of (2)

Element V of the investigation result_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.3.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.3.2, determine the amplitude compensation f (Y)_i1) And characteristic compensation f (Y)_i2) Whether the following relation is satisfied:

f(S_i)-δ≤f(Y_i1)≤f(S_i)+δ

f(S_i)-δ≤f(Y_i2)≤f(S_i)+δ

wherein: delta is the market impact coefficient, which is a known set value;

if yes, executing step 3.3.3; if not, executing step 3.3.4;

step 3.3.3, the element V of the investigation result of the current i-th candidate is used_iValue f (V) of_i) Marked as the significant point, then, changing f (V) by a set adjustment step size + -f (delta V)_i) To obtain a new f (V)_i) The step 3.3.1 is repeated, and the process is circulated continuously until the set times are reached, and the step 3.3.5 is executed;

step 3.3.4, the element V of the investigation result of the current i-th candidate is used_iValue f (V) of_i) Marking as an invalid point; then, f (V) is changed by a set adjustment step + -f (Δ V)_i) To obtain a new f (V)_i) The step 3.3.1 is repeated, and the process is circulated continuously until the set times are reached, and the step 3.3.5 is executed;

step 3.3.5, the distribution of the effective points and the invalid points is counted to obtain the range of the effective point concentration, so that the element V of the investigation result is obtained_iThe adjustment range of (a);

for the element V of the investigation result_iThe adjustment range of (A) is averaged to obtain

Step 3.4, adopting an adjusting algorithm to the 1 st market element

Value of

Is adjusted to obtainMarket elements 1

And finally obtaining the 1 st market element

Average value of (2)

Market 1 element

Average value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.4.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.4.2, compensate f (Y) according to the amplitude of fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relational expression of the step 3.3.2 is satisfied and further the 1 st market element is satisfied

Value of

Adjusting to obtain the 1 st market element meeting the relation of the step 3.3.2

And finally obtainingTo market item 1

Average value of (2)

Step 3.5, adopting an adjusting algorithm to the No. 2 market element

Value of

Making adjustment to obtain the 2 nd market element

And finally obtaining the 2 nd market element

Average value of (2)

Market factor 2

Average value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.5.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.5.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relational expression of the step 3.3.2 is satisfied or not, and further the 2 nd market element

Value of

Adjusting to obtain the 2 nd market element meeting the relation of the step 3.3.2

And finally obtaining the 2 nd market element

Average value of (2)

Step 3.6, adopting an adjusting algorithm to learn the index element E of the calendar_iValue f (E) of_i) Regulating to obtain the academic calendar index element E_iAnd finally obtaining the academic index element E_iAverage value of (2)

Will learn the calendar index element E_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.6.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.6.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relation formula of the step 3.3.2 is satisfied or not, and further the academic index element E_iValue f (E) of_i) Adjusting to obtain the academic calendar index element E meeting the relational expression of the step 3.3.2_iAnd finally obtaining the academic index element E_iAverage value of (2)

Step 3.7, adding value Q to the added items by adopting an adjusting algorithm_iValue of (A) f (Q)_i) Adjusting to obtain the added value Q_iAnd finally obtaining the added value Q of the added item_iAverage value of (2)

Adding the added items to the value Q_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.7.1, calculate the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.7.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relation formula of the step 3.3.2 is satisfied or not, and further adding the value Q to the added items_iValue of (A) f (Q)_i) Adjusting to meet the requirement of step 3.3.2Systematic added-term value added Q_iAnd finally obtaining the added value Q of the added item_iAverage value of (2)

Step 3.8, so far, the following parameters of the ith candidate are obtained: element V of investigation result_iAverage value of (2)

Market 1 element

Average value of (2)

Market factor 2

Average value of (2)

Index element E of academic calendar_iAverage value of (2)

Sum and add value added amount Q_iAverage value of (2)

Will be provided with

Is assigned to f (V)_i)，

Is assigned to

Is assigned to

Is assigned to f (E)_i)，

Is assigned to f (Q)_i)；

Let i equal i + 1; if i is greater than m₁Then m is₁After the personal candidate samples are trained on the compensation model, executing step 4; otherwise, returning to the step 3.2 for circular execution;

step 4, for m₁Reading the result table to obtain m₁A set

Step 5, establishing the 1 st market element

The function expression:

wherein:

is the 1 st market element

Grading and grading, wherein the value range is as follows:

a₁and b₁Are respectively the 1 st market element

And the 1 st and 2 nd influence factors of (c), and a is set₁＞0，b₁＞0；

At m₁An

In, the maximum value is denoted as f_max1Minimum value is denoted as f_min1(ii) a The following equation is thus obtained:

solving the equation to obtain a₁And b₁A value of (d);

step 6, establishing the 2 nd market element

The function expression:

wherein:

is the 2 nd market element

Grading and grading, wherein the value range is as follows:

a₂and b₂Are respectively the 2 nd market element

And the 1 st and 2 nd influence factors of (c), and a is set₂＞0，b₂＞0；

At m₁An

In, the maximum value is denoted as f_max2Minimum, isThe value is denoted as f_min2(ii) a The following equation is thus obtained:

solving the equation to obtain a₂And b₂A value of (d);

similarly, the element V of the investigation result is obtained separately_i1 influence factor a₃And 2 nd influence factor b₃A value of (d);

similarly, the academic index factors E are respectively obtained_i1 influence factor a₄And 2 nd influence factor b₄A value of (d);

for m₁Individual added item increment Q_iAverage value of (2)

Obtaining the value f (Q) of the final added value Q of the added items by adopting a statistical distribution algorithm;

and 7, obtaining a final compensation recommendation model as follows:

wherein:

R_Vfor the grade scoring of the element of the investigation result, the value range is as follows: r_V∈(0,1,2,3,4,5,6,7,8,9,10)

R_EThe grade scores of the academic calendar index elements are in the following value range: r_E∈(0,1,2,3,4,5,6,7,8,9,10)

Step 8, adopting the determination of step 2m₂And (4) testing the final compensation recommendation model obtained in the step (7) by using the individual candidate sample, and for m₂Calculating the amplitude compensation f (Y) of each candidate sample in the candidate samples through a compensation recommendation model₁) And characteristic compensation f (Y)₂) And by comparing the amplitude compensation f (Y)₁) Deviation from its post-entry compensation factor S, and characteristic compensation f (Y)₂) And the deviation of the compensation element S after the compensation is performed, and whether the deviation meets the set range, so that a compensation recommendation model is tested;

and 9, after the test in the step 8 is successful, obtaining a salary recommendation result of the candidate j by adopting the following method:

step 9.1, reading compensation basic information of the candidate j; the method comprises the following parameters:

compensation element C before job entry_jThe value of (f), (C);

company customization element M_jThe value of (f), (M);

the number N of candidate addicts;

step 9.2, determining the grade score R of the element of the investigation result according to the basic information of the candidate j_VGrade score R of index elements of the academic calendar_E1 rating score R of market elements_IaGrade score R of market item 2_Ib(ii) a Reading the fixed value f (Q) of the added value Q of the added item, and obtaining the amplitude compensation f (Y) of the candidate j according to the compensation recommendation model in the step 7_j1) And characteristic compensation f (Y)_j2)；

Step 9.3, according to the following formula, obtaining the compensation recommended range of the candidate j: [ min (f (Y) ]_j1),f(Y_j1)),max(f(Y_j1),f(Y_j1)]。

The salary recommendation method based on machine learning provided by the invention has the following advantages:

according to the salary recommendation method based on machine learning, provided by the invention, based on a salary model obtained by machine learning, the interference of human factors on salary is avoided; the compensation method is beneficial to different departments to reach the consistency of compensation of candidates; embodying the personal characteristics of the candidate; the operation is simple, and the full automation can be realized. Thereby improving the effectiveness and efficiency of the recruitment process.

Drawings

Fig. 1 is a flowchart illustrating a compensation recommendation method based on machine learning according to the present invention.

Detailed Description

In order to make the technical problems, technical solutions and advantageous effects solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The invention provides a machine learning-based compensation recommendation method, which comprises the following steps of referring to fig. 1:

step 1, collecting and establishing a candidate person sample library; the candidate sample library comprises a plurality of candidate samples, and any ith candidate sample is represented as: f_i＝{(f(C_i),N_i,f(S_i)}；

Wherein:

f(C_i) Represents the pre-entry compensation element C of the ith candidate_iValue f (C) of_i)；

N_iThe number of bonus items representing the ith candidate;

f(S_i) Represents the post-entry compensation element S of the ith candidate_iValue f (S) of_i)；

Step 2, dividing candidate samples in a candidate sample library into a training set and a testing set; wherein the training set has m₁A sample of individual candidates; test set has m₂A sample of individual candidates;

step 3, for m₁The individual candidate sample performs the following compensation model training process:

step 3.1, changing i to 1;

setting initial values, including: setting the 1 st market element

Value of

Market factor 2

Value of

Index element E of academic calendar_iValue f (E) of_i) Adding the value added by the item Q_iValue of (A) f (Q)_i) And a factor V of the result of investigation_iValue f (V) of_i)；

Step 3.2, for the ith candidate sample, reading the pre-entry compensation element C of the ith candidate_iValue f (C) of_i) And the compensation element S after work_iValue f (S) of_i) Number of bonus items N_iAnd company subscription element M_iValue f (M)_i) (ii) a Wherein, company subscribes element M_iValue f (M)_i) Is a known fixed value;

step 3.3, adopting an adjusting algorithm to examine the result element V_iValue f (V) of_i) Adjusting to obtain an element V of the investigation result_iAnd finally obtaining a survey result element V_iAverage value of (2)

Element V of the investigation result_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.3.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.3.2, determine the amplitude compensation f (Y)_i1) And characteristic compensation f (Y)_i2) Whether the following relation is satisfied:

f(S_i)-δ≤f(Y_i1)≤f(S_i)+δ

f(S_i)-δ≤f(Y_i2)≤f(S_i)+δ

wherein: delta is the market impact coefficient, which is a known set value;

if yes, executing step 3.3.3; if not, executing step 3.3.4;

step 3.3.3, the element V of the investigation result of the current i-th candidate is used_iValue f (V) of_i) Marked as the significant point, then, changing f (V) by a set adjustment step size + -f (delta V)_i) To obtain a new f (V)_i) The step 3.3.1 is repeated, and the process is circulated continuously until the set times are reached, and the step 3.3.5 is executed;

step 3.3.4, the element V of the investigation result of the current i-th candidate is used_iValue f (V) of_i) Marking as an invalid point; then, f (V) is changed by a set adjustment step + -f (Δ V)_i) To obtain a new f (V)_i) The step 3.3.1 is repeated, and the process is circulated continuously until the set times are reached, and the step 3.3.5 is executed;

step 3.3.5, the distribution of the effective points and the invalid points is counted to obtain the range of the effective point concentration, so that the element V of the investigation result is obtained_iThe adjustment range of (a);

for the element V of the investigation result_iThe adjustment range of (A) is averaged to obtain

Step 3.4, adopting an adjusting algorithm to the 1 st market element

Value of

Making adjustment to obtain market element 1

And finally obtaining the 1 st market element

Average value of (2)

Market 1 element

Average value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.4.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.4.2, compensate f (Y) according to the amplitude of fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relational expression of the step 3.3.2 is satisfied and further the 1 st market element is satisfied

Value of

Adjusting to obtain the 1 st market element meeting the relation of the step 3.3.2

And finally obtaining the 1 st market element

Average value of (2)

Step 3.5, adopting an adjusting algorithm to the No. 2 market element

Value of

Making adjustment to obtain the 2 nd market element

And finally obtaining the 2 nd market element

Average value of (2)

Market factor 2

Average value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.5.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.5.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relational expression of the step 3.3.2 is satisfied or not, and further the 2 nd market element

Value of

Adjusting to obtain the 2 nd market element meeting the relation of the step 3.3.2

And finally obtaining the 2 nd market element

Average value of (2)

Step 3.6, adopting an adjusting algorithm to learn the index element E of the calendar_iValue f (E) of_i) Regulating to obtain the academic calendar index element E_iAnd finally obtaining the academic index element E_iAverage value of (2)

Will learn the calendar index element E_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.6.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.6.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relation formula of the step 3.3.2 is satisfied or not, and further the academic index element E_iValue f (E) of_i) Adjusting to obtain the academic calendar index element E meeting the relational expression of the step 3.3.2_iAnd finally obtaining the academic index element E_iAverage value of (2)

Step 3.7, adding value Q to the added items by adopting an adjusting algorithm_iValue of (A) f (Q)_i) Adjusting to obtain the added value Q_iAnd finally obtaining the added value Q of the added item_iAverage value of (2)

Adding the added items to the value Q_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.7.1, calculate the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.7.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether step 3.3.2 off is satisfiedSystem, further adding value Q to the added item_iValue of (A) f (Q)_i) Adjusting to obtain the added value Q of the added item satisfying the relational expression of the step 3.3.2_iAnd finally obtaining the added value Q of the added item_iAverage value of (2)

Step 3.8, so far, the following parameters of the ith candidate are obtained: element V of investigation result_iAverage value of (2)

Market 1 element

Average value of (2)

Market factor 2

Average value of (2)

Index element E of academic calendar_iAverage value of (2)

Sum and add value added amount Q_iAverage value of (2)

Will be provided with

Is assigned to f (V)_i)，

Is assigned to

Is assigned to

Is assigned to f (E)_i)，

Is assigned to f (Q)_i)；

Let i equal i + 1; if i is greater than m₁Then m is₁After the personal candidate samples are trained on the compensation model, executing step 4; otherwise, returning to the step 3.2 for circular execution;

step 4, for m₁Reading the result table to obtain m₁A set

Step 5, establishing the 1 st market element

The function expression:

wherein:

is the 1 st market element

Grading and grading, wherein the value range is as follows:

a₁and b₁Are respectively the 1 st market element

And the 1 st and 2 nd influence factors of (c), and a is set₁＞0，b₁＞0；

At m₁An

In, the maximum value is denoted as f_max1Minimum value is denoted as f_min1(ii) a The following equation is thus obtained:

solving the equation to obtain a₁And b₁A value of (d);

step 6, establishing the 2 nd market element

The function expression:

wherein:

is the 2 nd market element

Grading and grading, wherein the value range is as follows:

a₂and b₂Are respectively the 2 nd market element

And the 1 st and 2 nd influence factors of (c), and a is set₂＞0，b₂＞0；

At m₁An

In, the maximum value is denoted as f_max2Minimum value is denoted as f_min2(ii) a The following equation is thus obtained:

solving the equation to obtain a₂And b₂A value of (d);

similarly, the element V of the investigation result is obtained separately_i1 influence factor a₃And 2 nd influence factor b₃A value of (d);

similarly, the academic index factors E are respectively obtained_i1 influence factor a₄And 2 nd influence factor b₄A value of (d);

for m₁Individual added item increment Q_iAverage value of (2)

Obtaining the value f (Q) of the final added value Q of the added items by adopting a statistical distribution algorithm;

and 7, obtaining a final compensation recommendation model as follows:

wherein:

R_Vfor the grade scoring of the element of the investigation result, the value range is as follows: r_V∈(0,1,2,3,4,5,6,7,8,9,10)

R_ETo grade the academic degree index elements,the value range is as follows: r_E∈(0,1,2,3,4,5,6,7,8,9,10)

Step 8, adopting m determined in step 2₂And (4) testing the final compensation recommendation model obtained in the step (7) by using the individual candidate sample, and for m₂Calculating the amplitude compensation f (Y) of each candidate sample in the candidate samples through a compensation recommendation model₁) And characteristic compensation f (Y)₂) And by comparing the amplitude compensation f (Y)₁) Deviation from its post-entry compensation factor S, and characteristic compensation f (Y)₂) And the deviation of the compensation element S after the compensation is performed, and whether the deviation meets the set range, so that a compensation recommendation model is tested;

and 9, after the test in the step 8 is successful, obtaining a salary recommendation result of the candidate j by adopting the following method:

step 9.1, reading compensation basic information of the candidate j; the method comprises the following parameters:

compensation element C before job entry_jThe value of (f), (C);

company customization element M_jThe value of (f), (M);

the number N of candidate addicts;

step 9.2, determining the grade score R of the element of the investigation result according to the basic information of the candidate j_VGrade score R of index elements of the academic calendar_E1 rating of market elements

Rating scores for market items 2

Reading the fixed value f (Q) of the added value Q of the added item, and obtaining the amplitude compensation f (Y) of the candidate j according to the compensation recommendation model in the step 7_j1) And characteristic compensation f (Y)_j2)；

Step 9.3, according to the following formula, obtaining the compensation recommended range of the candidate j: [ min (f (Y) ]_j1),f(Y_j1)),max(f(Y_j1),f(Y_j1)]。

The salary recommendation method based on machine learning provided by the invention has the following advantages:

(1) establishing a compensation model comprising a plurality of elements, which specifically comprises the following steps: market 1 element

Market factor 2

Index element E of academic calendar_iAnd element V of investigation result_iThe number of added items N_iAnd company customized element M_i(ii) a Wherein, company subscribes element M_iIs a fixed value; the value added amount Q of the added items is obtained by training the compensation model_iThe value of (b) is a fixed value, which is applicable to all candidates.

Therefore, when the user uses the trained salary model, the value added by the added items Q only needs to be obtained according to the basic information of the candidate_iThe salary before the job, the number of candidate added items and the value of the company customized element, and then the grade scores of each element of the candidate are obtained, so that the salary recommendation range can be quickly obtained. The use is simple and convenient. The finally obtained salary recommendation range is more consistent with the basic information of the candidate through inspection, and has higher credibility.

(2) When training the compensation model, for the 1 st candidate, firstly, the elements V of the investigation result are obtained in sequence_iAverage value of (2)

Market 1 element

Average value of (2)

Market factor 2

Average value of (2)

Index element E of academic calendar_iAverage value of (2)

Sum and add value added amount Q_iAverage value of (2)

In addition, in the case of training the five elements, since the average value of the previous element is used as the known value in the case of training the next element, the accuracy of the model training is enhanced in a progressive relationship.

And starting from the second candidate, each candidate adopts the training result of the previous candidate as the initial known value, so that the training result of the previous candidate is the known value when the next candidate trains, and the accuracy of the model training is further enhanced.

(3) Through the automatic training process of the compensation model, the determined values of the factor influencing each element can be trained. Thereby obtaining an accurate compensation model with universal significance. The salary model carries out salary recommendation on a plurality of interviewees, and the accuracy and the reliability of the use result of the salary model are verified through a large amount of data verification and evaluation of a plurality of experts.

(4) For compensation information, further mining can be performed:

1) two compensation calculation results obtained by final calculation, namely amplitude compensation f (Y)₁) And characteristic compensation f (Y)₂) The closer the numbers, the more normal the candidate is developing;

2) when amplitude salary f (Y)₁) Is obviously greater than the characteristic compensation f (Y)₂) When the time is short, the development of the candidate is beyond the general situation;

3) characteristic compensation f (Y)₂) Is obviously greater than the amplitude compensation f (Y)₁) Time, indicates that the candidate is developing less than normal.

In the latter two cases, the compensation model may be further trained by increasing the number of training samples.

The compensation model obtained by the final training of the invention adopts excel expression, and when in use, the compensation model can be directly used for filling in a form, thus being simple and convenient

Taking a compensation model of a king of a certain candidate as an example, through calculation, the amplitude compensation calculation is 12279 yuan, and the characteristic compensation calculation is 11633 yuan, so the reasonable compensation range is [11633,12279], the optimal compensation is 11956 yuan, so the recommended compensation is 12000 yuan, and the amplitude is 19%.

The invention provides a set of analysis methods for training of complete compensation influence factors, which can reflect compensation characteristics of interview candidates and the contribution degree of each characteristic in compensation suggestions, and finally generate a reasonable compensation suggestion range.

The invention provides a machine learning-based salary recommendation method, which has the following advantages:

(1) interference of human factors on compensation is avoided based on a compensation model obtained by machine learning; the compensation method is beneficial to different departments to reach the consistency of compensation of candidates; embodying the personal characteristics of the candidate; the operation is simple, and the full automation can be realized. Thereby improving the effectiveness and efficiency of the recruitment process.

(2) The model provides a machine learning-based salary factor model and a factor training method, and calculates salary range and advice salary according to two different dimensions, and is used for guiding salary amount evaluation in the recruitment process.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.

Claims (1)

1. A compensation recommendation method based on machine learning is characterized by comprising the following steps:

step 1, collecting and establishing a candidate person sample library; the candidate sample library comprises a plurality of candidate samples, and any ith candidate sample is represented as: f_i＝{(f(C_i),N_i,f(S_i)}；

Wherein:

f(C_i) Represents the pre-entry compensation element C of the ith candidate_iValue f (C) of_i)；

N_iThe number of bonus items representing the ith candidate;

f(S_i) Represents the post-entry compensation element S of the ith candidate_iValue f (S) of_i)；

Step 2, dividing candidate samples in a candidate sample library into a training set and a testing set; wherein the training set has m₁A sample of individual candidates; test set has m₂A sample of individual candidates;

step 3, for m₁The individual candidate sample performs the following compensation model training process:

step 3.1, changing i to 1;

setting initial values, including: setting the 1 st market element

Value of

Market factor 2

Value of

Index element E of academic calendar_iValue f (E) of_i) Adding the value added by the item Q_iValue of (A) f (Q)_i) And a factor V of the result of investigation_iValue f (V) of_i)；

Step 3.2, for the ith candidate sample, reading the pre-entry compensation element C of the ith candidate_iValue f (C) of_i) And the compensation element S after work_iValue f (S) of_i) Number of bonus items N_iAnd company subscription element M_iValue f (M)_i) (ii) a Wherein, company subscribes element M_iValue f (M)_i) Is a known fixed value;

step 3.3, adopting an adjusting algorithm to examine the result element V_iValue f (V) of_i) Adjusting to obtain an element V of the investigation result_iAnd finally obtaining a survey result element V_iAverage value of (2)

Element V of the investigation result_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.3.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.3.2, determine the amplitude compensation f (Y)_i1) And characteristic compensation f (Y)_i2) Whether the following relation is satisfied:

f(S_i)-δ≤f(Y_i1)≤f(S_i)+δ

f(S_i)-δ≤f(Y_i2)≤f(S_i)+δ

wherein: delta is the market impact coefficient, which is a known set value;

if yes, executing step 3.3.3; if not, executing step 3.3.4;

step 3.3.3, the element V of the investigation result of the current i-th candidate is used_iValue f (V) of_i) Marked as the significant point, then, changing f (V) by a set adjustment step size + -f (delta V)_i) To obtain a new f (V)_i) Then returning to the step 3.3.1, and continuously doing soCirculating until the set times is reached, and executing the step 3.3.5;

step 3.3.4, the element V of the investigation result of the current i-th candidate is used_iValue f (V) of_i) Marking as an invalid point; then, f (V) is changed by a set adjustment step + -f (Δ V)_i) To obtain a new f (V)_i) The step 3.3.1 is repeated, and the process is circulated continuously until the set times are reached, and the step 3.3.5 is executed;

step 3.3.5, the distribution of the effective points and the invalid points is counted to obtain the range of the effective point concentration, so that the element V of the investigation result is obtained_iThe adjustment range of (a);

for the element V of the investigation result_iThe adjustment range of (A) is averaged to obtain

Step 3.4, adopting an adjusting algorithm to the 1 st market element

Value of

Making adjustment to obtain market element 1

And finally obtaining the 1 st market element

Average value of (2)

Market 1 element

Average value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.4.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.4.2, compensate f (Y) according to the amplitude of fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relational expression of the step 3.3.2 is satisfied and further the 1 st market element is satisfied

Value of

Adjusting to obtain the 1 st market element meeting the relation of the step 3.3.2

And finally obtaining the 1 st market element

Average value of (2)

Step 3.5, adopting an adjusting algorithm to the No. 2 market element

Value of

Making adjustment to obtain the 2 nd market element

And finally obtaining the 2 nd market element

Average value of (2)

Market factor 2

Average value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.5.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.5.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relational expression of the step 3.3.2 is satisfied or not, and further the 2 nd market element

Value of

Adjusting to obtain the satisfied stepStep 3.3.2 market elements of the relation 2

And finally obtaining the 2 nd market element

Average value of (2)

Step 3.6, adopting an adjusting algorithm to learn the index element E of the calendar_iValue f (E) of_i) Regulating to obtain the academic calendar index element E_iAnd finally obtaining the academic index element E_iAverage value of (2)

Will learn the calendar index element E_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.6.1, respectively calculating and obtaining the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.6.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relation formula of the step 3.3.2 is satisfied or not, and further the academic index element E_iValue f (E) of_i) Adjusting to obtain the academic calendar index elements meeting the relational expression of the step 3.3.2E_iAnd finally obtaining the academic index element E_iAverage value of (2)

Step 3.7, adding value Q to the added items by adopting an adjusting algorithm_iValue of (A) f (Q)_i) Adjusting to obtain the added value Q_iAnd finally obtaining the added value Q of the added item_iAverage value of (2)

Adding the added items to the value Q_iAverage value of (2)

Recording the data in a result table;

wherein, the adjusting algorithm is as follows:

step 3.7.1, calculate the amplitude compensation f (Y) according to the following formula_i1) And characteristic compensation f (Y)_i2)：

Step 3.7.2, compensate f (Y) according to the amplitude fluctuation_i1) And characteristic compensation f (Y)_i2) Whether the relation formula of the step 3.3.2 is satisfied or not, and further adding the value Q to the added items_iValue of (A) f (Q)_i) Adjusting to obtain the added value Q of the added item satisfying the relational expression of the step 3.3.2_iAnd finally obtaining the added value Q of the added item_iAverage value of (2)

Step 3.8, thus obtaining the ithCandidates the following parameters: element V of investigation result_iAverage value of (2)

Market 1 element

Average value of (2)

Market factor 2

Average value of (2)

Index element E of academic calendar_iAverage value of (2)

Sum and add value added amount Q_iAverage value of (2)

Will be provided with

Is assigned to f (V)_i)，

Is assigned to

Is assigned to

Is assigned to f (E)_i)，

Is assigned to f (Q)_i)；

Let i equal i + 1; if i is greater than m₁Then m is₁After the personal candidate samples are trained on the compensation model, executing step 4; otherwise, returning to the step 3.2 for circular execution;

step 4, for m₁Reading the result table to obtain m₁A set

Step 5, establishing the 1 st market element

The function expression:

wherein:

is the 1 st market element

Grading and grading, wherein the value range is as follows:

a₁and b₁Are respectively the 1 st market element

And the 1 st and 2 nd influence factors of (c), and a is set₁＞0，b₁＞0；

At m₁An

In, the maximum value is denoted as f_max1Minimum value is denoted as f_min1(ii) a The following equation is thus obtained:

solving the equation to obtain a₁And b₁A value of (d);

step 6, establishing the 2 nd market element

The function expression:

wherein:

is the 2 nd market element

Grading and grading, wherein the value range is as follows:

a₂and b₂Are respectively the 2 nd market element

And the 1 st and 2 nd influence factors of (c), and a is set₂＞0，b₂＞0；

At m₁An

In, the maximum value is denoted as f_max2Minimum value is denoted as f_min2(ii) a The following equation is thus obtained:

solving the equation to obtain a₂And b₂A value of (d);

similarly, the element V of the investigation result is obtained separately_i1 influence factor a₃And 2 nd influence factor b₃A value of (d);

similarly, the academic index factors E are respectively obtained_i1 influence factor a₄And 2 nd influence factor b₄A value of (d);

for m₁Individual added item increment Q_iAverage value of (2)

Obtaining the value f (Q) of the final added value Q of the added items by adopting a statistical distribution algorithm;

and 7, obtaining a final compensation recommendation model as follows:

wherein:

R_Vfor the grade scoring of the element of the investigation result, the value range is as follows: r_V∈(0,1,2,3,4,5,6,7,8,9,10)

R_ERating for academic index elementsThe value range is: r_E∈(0,1,2,3,4,5,6,7,8,9,10)

Step 8, adopting m determined in step 2₂And (4) testing the final compensation recommendation model obtained in the step (7) by using the individual candidate sample, and for m₂Calculating the amplitude compensation f (Y) of each candidate sample in the candidate samples through a compensation recommendation model₁) And characteristic compensation f (Y)₂) And by comparing the amplitude compensation f (Y)₁) Deviation from its post-entry compensation factor S, and characteristic compensation f (Y)₂) And the deviation of the compensation element S after the compensation is performed, and whether the deviation meets the set range, so that a compensation recommendation model is tested;

and 9, after the test in the step 8 is successful, obtaining a salary recommendation result of the candidate j by adopting the following method:

step 9.1, reading compensation basic information of the candidate j; the method comprises the following parameters:

compensation element C before job entry_jThe value of (f), (C);

company customization element M_jThe value of (f), (M);

the number N of candidate addicts;

step 9.2, determining the grade score R of the element of the investigation result according to the basic information of the candidate j_VGrade score R of index elements of the academic calendar_E1 rating score R of market elements_IaGrade score R of market item 2_Ib(ii) a Reading the fixed value f (Q) of the added value Q of the added item, and obtaining the amplitude compensation f (Y) of the candidate j according to the compensation recommendation model in the step 7_j1) And characteristic compensation f (Y)_j2)；

Step 9.3, according to the following formula, obtaining the compensation recommended range of the candidate j: [ min (f (Y) ]_j1),f(Y_j1)),max(f(Y_j1),f(Y_j1)]。

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