CN111708929B

CN111708929B - Information searching method, device, electronic equipment and storage medium

Info

Publication number: CN111708929B
Application number: CN202010552957.2A
Authority: CN
Inventors: 李垚男; 张航
Original assignee: Beijing ByteDance Network Technology Co Ltd
Current assignee: Beijing ByteDance Network Technology Co Ltd
Priority date: 2020-06-17
Filing date: 2020-06-17
Publication date: 2023-05-30
Anticipated expiration: 2040-06-17
Also published as: CN111708929A

Abstract

The disclosure provides an information searching method, an information searching device, electronic equipment and a storage medium. The information searching method provided by the disclosure comprises the following steps: acquiring a position search request, determining a target position according to position information, position keywords and a preset matching algorithm in the position search request, determining target resources according to position information of the target position and a preset resource algorithm, determining a distance value between the resources corresponding to each position in a position set and the target resources, sequencing all positions in the position set through the distance values to generate a position recommendation list, and displaying the position recommendation list. According to the information searching method, the overall competitive relationship is considered, so that the delivery efficiency of job seekers is improved, and blind application of the job seekers is avoided.

Description

Information searching method, device, electronic equipment and storage medium

Technical Field

The disclosure relates to the technical field of data processing, and in particular relates to an information searching method, an information searching device, electronic equipment and a storage medium.

Background

With the rapid development of internet technology, job seekers basically carry out delivery resume through a network recruitment platform when carrying out job seekers.

Currently, on some recruitment websites, when a job seeker searches for a job position, the job position returns a search result according to the input position information of the job seeker, and the search result is ranked according to the matching degree with the input position information.

However, the search results and the ordering mode returned in the existing job searching process are mainly based on the search keyword information input by the user, but cannot be combined with more job hunting information to match, so that the matching degree of the returned job information and the expected job hunting personnel is low, the expected job hunting personnel are difficult to search for the job hunting personnel, and the job hunting efficiency is low.

Disclosure of Invention

The disclosure provides an information searching method, an information searching device, electronic equipment and a storage medium, which are used for solving the technical problems that job information returned by a current searching mode is low in matching degree with expected job positions of job seekers, and the job seekers are difficult to search for the expected job positions.

In a first aspect, the present disclosure provides an information searching method, including:

acquiring a job search request, wherein the job search request comprises a job keyword;

determining a target position according to the position keywords, the job seeker information and a preset matching algorithm, wherein the target position belongs to a position set;

Determining target resources according to the position information of the target positions and a preset resource algorithm, and determining a distance value between the resources corresponding to each position in the position set and the target resources;

and displaying the target position and the positions in the position set in a search result list according to the distance value.

In one possible design, the determining the target position according to the position keyword, the job seeker information and the preset matching algorithm includes:

determining a desired position set according to the position keywords;

determining a delivery talent set according to the job seeker, wherein the delivery talent set comprises the job seeker, and the number of the job positions included in the expected job position set is the same as the number of the talents included in the delivery talent set;

and establishing a one-to-one mapping relation between each position in the expected position set and each talent in the delivery talent set according to the preset matching algorithm, wherein the target position is the position mapped with the job seeker.

In one possible design, the determining the desired job set according to the job keyword, and determining the delivery talent set according to the job seeker includes:

Determining a first position set according to the position keywords, wherein the first position set comprises N positions, and N is a positive integer;

determining a first talent set according to the job seeker, wherein the first talent set comprises M talents, and M is a positive integer;

and determining the expected position set and the delivery talent set according to the numerical relation between the N and the M.

In one possible design, if the N is greater than the M, selecting M positions before resource sorting from the first position set to form the desired position set, and the delivery talent set is the first talent set, where resources corresponding to each position in the first position set are calculated according to the preset resource algorithm, and descending sorting is performed according to the resources corresponding to each position; or,

if the N is equal to the M, the expected position set is the first position set, and the delivery talent set is the first talent set; or,

if the N is smaller than the M, determining a position of the first position set, which is the last position of the resource sequencing, wherein the expected position set comprises the first position set and M-N positions of the last sequencing, and the delivery talent set is the first talent set.

In one possible design, the establishing a one-to-one mapping relationship between each position in the desired position set and each talent in the talent information set according to the preset matching algorithm includes:

determining a first talent in the delivery talent set to sort a first position of each position in the expected position set, wherein the first talent is any talent in the delivery talent set, the first position sorting is determined according to the matching degree of talents and positions, and the matching degree is determined according to talent information, position information and a preset skill score calculation rule;

determining a first talent ranking of a first position in the expected position set for each position in the expected position set, wherein the first position is any position in the expected position set, and the first talent ranking is determined according to the matching degree of talents and positions;

and establishing a one-to-one mapping relation between each position in the expected position set and each talent in the talent information set according to the first position ordering and the first talent ordering.

In one possible design, the establishing a one-to-one mapping relationship between each position in the desired position set and each talent in the talent information set according to the first position order and the first talent order includes:

Determining a set of to-be-selected talents corresponding to each position according to the first position ordering;

determining a talent rejecting set according to the first talent sorting and the talent waiting set corresponding to each position;

determining a to-be-selected talent set corresponding to the positions of unsuccessful matching talents according to the first position ordering and the refused talent set, and continuously determining a refused talent set according to the first talent ordering and the to-be-selected talent set corresponding to each position of unsuccessful matching talents until the refused talent set is an empty set;

and establishing a one-to-one mapping relation between each position in the expected position set and each talent in the talent information set according to the talents successfully matched with each position.

In one possible design, the matching degree is determined according to talent information, job information and preset skill score calculation rules, including:

obtaining skill words in the job position information, and distributing corresponding preset skill scores for each skill word;

determining whether the talent information comprises the skill word;

if yes, accumulating the corresponding preset skill score to the matching degree.

In one possible design, the job information includes: one or more of yearly information, monthly salary information, public accumulation information, pension information, daily working time information and weekly working time information.

In one possible design, the preset resource algorithm is:

β＝a+log ₃₀₀₀ (b+c+d+h)-e-f+g

wherein β is a resource, a is a yearly fake number in the yearly fake information, b is an equivalent yearly firewood in the yearly fake information, c is an accumulation payment amount in the accumulation information, d is a pension payment amount in the accumulation information, e is a daily working time length in the daily working time length information, f is a weekly working time length in the weekly working time length information, g is an additional benefit amount, and h is an additional vacation time length.

In a second aspect, the present disclosure provides a job position recommendation device, comprising:

the request acquisition module is used for acquiring a position search request, wherein the position search request comprises position keywords;

the job position matching module is used for determining a target job position according to the job position keywords, job seeker information and a preset matching algorithm, wherein the target job position belongs to a job position set;

the resource determining module is used for determining target resources according to the position information of the target positions and a preset resource algorithm, and determining the distance value between the resources corresponding to each position in the position set and the target resources;

and the position display module is used for displaying the target position and the positions in the position set in the search result list according to the distance value.

In one possible design, the job matching module is specifically configured to:

determining a desired position set according to the position keywords;

In one possible design, the job matching module is specifically configured to:

In one possible design, if the N is greater than the M, selecting M positions before resource sorting from the first position set to form the desired position set, and the delivery talent set is the first talent set, where resources corresponding to each position in the first position set are calculated according to the preset resource algorithm, and descending sorting is performed according to the resources corresponding to each position;

If the N is equal to the M, the expected position set is the first position set, and the delivery talent set is the first talent set;

In one possible design, the job matching module is specifically configured to:

Determining whether the talent information comprises the skill word;

In one possible design, the preset resource algorithm is:

β＝a+log ₃₀₀₀ (b+c+d+h)-e-f+g

In a third aspect, the present disclosure also provides an electronic device, including:

a processing device; the method comprises the steps of,

a storage device for storing executable instructions of the processing device;

wherein the processing means is configured to perform any one of the possible information search methods of the first aspect via execution of the executable instructions.

In a fourth aspect, embodiments of the present disclosure further provide a storage medium having stored thereon a computer program which, when executed by a processing device, implements any one of the possible information searching methods of the first aspect.

The invention provides an information searching method, an information searching device, electronic equipment and a storage medium, wherein a job searching request is obtained, a target job position is determined according to job position key words and a preset matching algorithm in job position searching request, target resources are determined according to job position information of the target job position and a preset resource algorithm, distance values between resources corresponding to each job position in a job position set and the target resources are determined, all the job positions in the job position set are ordered according to the distance values to generate a job position recommendation list, and the job position recommendation list is displayed, so that global competitive relation is considered, delivery efficiency of job seekers is improved, and blind application of job seekers is avoided.

Drawings

In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, a brief description will be given below of the drawings that are needed in the embodiments or the description of the prior art, it being obvious that the drawings in the following description are some embodiments of the present disclosure, and that other drawings may be obtained from these drawings without inventive effort to a person of ordinary skill in the art.

FIG. 1 is an application scenario diagram of an information search method according to an example embodiment of the present disclosure;

FIG. 2 is a schematic diagram of a job recommendation list, shown in accordance with an example embodiment of the present disclosure;

FIG. 3 is a flow diagram of an information search method according to an example embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a target job determination flow diagram according to an example embodiment of the present disclosure;

FIG. 5 is a flow diagram of a method of information searching according to an example embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a position recommendation device according to an example embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an electronic device according to an example embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

Currently, on some recruitment websites, when job seekers search for job seekers, the job seekers are ranked according to the situation of the job seekers. The existing sequencing method generally sequences corresponding posts according to education features, experience features, skill features and other factors. However, the existing ordering mode basically ignores the competition situation of posts in the job hunting process according to the best to inferior mode, and blind application of job hunting personnel is easy to cause without echo.

Aiming at the problems, the information searching method is provided, and the position recommending list is generated by acquiring the position searching request, determining the target position according to the position key words and the preset matching algorithm in the position searching request, determining the target resource according to the position information of the target position and the preset resource algorithm, determining the distance value between the resource corresponding to each position in the position set and the target resource, sequencing the positions in the position set through the distance value, and displaying the position recommending list so as to consider the global competitive relationship, improve the delivery efficiency of the staff, and avoid blind application of the staff. The data query method is described in detail below in terms of several specific implementations.

Fig. 1 is an application scenario diagram of an information search method according to an example embodiment of the present disclosure. As shown in fig. 1, the data query method provided in this embodiment may be applied to a terminal device 100, where the terminal device 100 may be a personal computer, a notebook computer, a tablet computer, a smart phone, and other devices. The user may initiate a job search request in the terminal apparatus 100, wherein the job search request may be in the form of entering a job keyword on a related web page of the terminal apparatus 100, for example, "python" is entered in a search field of the web page.

The terminal device 100 may directly perform search matching in the storage system of the terminal device 100 according to the position keyword, or may send the position keyword to the server 200 to perform search matching as shown in fig. 1, so that the server 200 determines a target position according to the position keyword, the job seeker information and a preset matching algorithm, where the target position belongs to a position set.

Moreover, the server 200 may further determine a target resource according to the position information of the target position and a preset resource algorithm, and determine a distance value between the resource corresponding to each position in the position set and the target resource, where it is worth understanding that the distance value may be an absolute value of a difference value between the resource corresponding to each position in the position set and the target resource.

And then, sorting all positions in the position set according to the distance value with the target resource to generate a position recommendation list, wherein the first position of the position recommendation list is the target position, and the rest positions are sequentially arranged from small to large according to the corresponding distance value. It is worth understanding that in this embodiment, the target positions are first matched, and then the positions are ordered according to the difference between each position and the target position, so that a list of the matching degree ordering of the positions and the job seekers is generated, the delivery efficiency of the job seekers is improved, and blind application of the job seekers is avoided.

Fig. 2 is a schematic diagram of a job recommendation list, according to an example embodiment, shown by the present disclosure. As shown in fig. 2, after the job recommendation list is generated, the job recommendation list may be displayed in the terminal device 100 for job delivery by job seekers.

Fig. 3 is a flow diagram of an information search method according to an example embodiment of the present disclosure. As shown in fig. 3, the information searching method provided in this embodiment includes:

step 301, acquiring a job position search request.

Specifically, the user may initiate a job search request in the terminal device 1, where the job search request may be in the form of entering a job keyword on a relevant web page of the terminal device, for example, "python" is entered in a search field of the web page.

And 302, determining a target position according to the position keywords, the job seeker information and a preset matching algorithm.

After the position keywords are obtained, the target position can be determined according to the position keywords, the position applicant information and a preset matching algorithm, wherein the position applicant information can be a resume of the position applicant or information extracted based on resume information of the position applicant.

The target position can be understood as the position with the highest matching degree between the job seeker and the position, wherein the matching can be performed according to the professional skills and education experience of the job seeker and the skill requirements and professional requirements of the position. Optionally, when the job seeker information includes more keywords corresponding to the skill requirements or the professional requirements of the job, the matching degree between the job seeker and the job is higher.

Fig. 4 is a schematic diagram of a target job determination flow shown in accordance with an example embodiment of the present disclosure. As shown in fig. 4, in the present embodiment, for a target position determining process, the method includes:

step 3021, determining a desired job set according to the job keywords.

Step 3022, determining a delivery talent collection according to job seekers.

The delivery talents collection comprises the job seeker, the number of the job positions contained in the expected job position collection is the same as the number of the talents contained in the delivery talents collection, so that a virtual talent market is constructed, and each talent in the delivery talents collection can be matched with a unique job position in the expected job position collection.

Specifically, a first job set may be determined according to the job keyword, where the first job set includes N job positions, where N is a positive integer. And determining a first talent set according to the job seeker, wherein the first talent set comprises M talents, and M is a positive integer. The desired set of positions and the delivery talents set may then be determined based on the numerical relationship of N and M.

After the job seeker initiates a job search request, the relevant job types are found out to be similar, but the job types with different levels are different, and a first job set P_ { big }, wherein P_ { big } is provided with M job positions, if one job position is called for k persons, the job position can be expanded into k job positions, and the k job positions have the same properties except the number. It should be noted that, the specific ways of determining positions with similar types but different levels can be various ways of searching similar positions in the existing position search, and the method is not limited in this embodiment. In addition, a first talent set T_ { big } can be found in a historical talent database, wherein the talents are similar to the job seeker's job seeker, but are experienced by the job seeker's school and expected to have different salaries, and the T_ { big } has M talents.

If N is equal to M, the expected position set is a first position set, and the delivery talent set is a first talent set.

If N is greater than M, selecting the first M positions in the resource order (for example, may be a treatment order) from the first position set to form a desired position set, and the delivery talent set is the first talent set. In order to facilitate the explanation, the following will explain the treatment as a resource in detail. The method comprises the steps of calculating the treatments corresponding to each position in a first position set according to a preset treatment algorithm, and sorting in descending order according to the treatments corresponding to each position.

Specifically, if N is greater than M, selecting M positions with better treatment criteria from the N positions. The preset treatment algorithm is as follows:

β＝a+log ₃₀₀₀ (b+c+d+h)-e-f+g

wherein, beta is treatment, a is the annual leave number in the annual leave information, b is the equivalent monthly leave in the monthly leave information, c is the accumulation payment amount in the accumulation information, d is the pension payment amount in the accumulation information, e is the daily working time in the daily working time information, f is the weekly working time in the weekly working time information, g is the amount of added benefit, and h is the time of added vacation.

If N is smaller than M, determining the last position to be sequenced in the first position set, wherein the expected position set comprises the first position set and M-N last positions to be sequenced, and the delivery talent set is the first talent set.

Specifically, if N is less than M, finding out the position with the worst treatment in the P { big } set, then properly reducing the treatment of the position, generating a new position p_ { extra }, and assuming p_ { extra } recruits M-N people.

Through the processing mode, the positions included in the expected position set are the same as the talents included in the talent set, so that a virtual talent market is constructed, and each talent in the talent set can be matched with a unique position in the expected position set.

Step 3033, a one-to-one mapping relationship between each position in the expected position set and each talent in the delivery talent set is established according to a preset matching algorithm.

Specifically, after the desired talent set and the delivery talent set are determined, a first talent in the delivery talent set can be determined to rank a first position of each position in the desired talent set, the first talent is any one talent in the delivery talent set, the first position rank is determined according to a degree of matching between the talents and the positions, and the degree of matching is determined according to talent information, position information and a preset skill score calculation rule.

For example, skill words in job position information can be obtained, corresponding preset skill scores are allocated to each skill word, then whether the talent information includes the skill word is determined, and if yes, the corresponding preset skill scores are accumulated to the matching degree.

Specifically, the matching degree of talents and positions can be determined by skill scores and educational experience scores of technical posts. For example, the skill words may be obtained by summing the depth of the skill words in the skill tree, which occur in the talent information, with the number of skill words. It may be assumed that the job information requires python, and the programming language is a first level skill word, python is a second level skill word, and sk-learn is a third level skill word. Because the programming language contains python, and sk-learn is a machine learning library of python.

If a_i is the score of the ith skill word, assume sum_ { a_i } is the score of that person on that skill. And assuming that this post does not require python, the skill of sk-learn does not score.

In addition, the educational experience score may be obtained by constructing an educational experience score table, such as 9 score for 985 doctor, 7 score for 985 master, 4 score for 211 family, 3 score for non-985 and non-211. The educational score for such a person can be obtained by summing up the aligned educational experiences.

Assuming that the skill score of a person is s and the educational experience score is e, the composite score is z=ms+ne, where m, n is the weight coefficient of the skill score and the educational experience score.

After determining the degree of matching of talents with positions, a one-to-one mapping relationship between each position in the desired position set and each talent in the talent information set may be established.

In this step, the specific manner of the one-to-one mapping relationship between each position and each talent in the talent information set may be:

determining first talents in a delivery talent set to sort first positions of each position in a desired position set, wherein the first talents are any talents in the delivery talent set, the first position sorting is determined according to the matching degree of the talents and the positions, and the matching degree is determined according to talent information, position information and preset skill score calculation rules.

Wherein, can be in matrix O ^T Representing the ordering of talents to positions, wherein

Indicating i person talentsThe j-th position row is +.>

Bit (s)/(s)>

The smaller the value of (c) the higher the priority. Of these, 4 positions (A, B, C, D) and 4 talents (a, b, c, t) may be taken as examples. Fig. 5 is a flowchart of an information search method according to an exemplary embodiment of the present disclosure, and as shown in fig. 5, the information search method is described in detail:

for the situation of 4 positions and 4 talents, O ^T The method comprises the following steps:

[[0,3,2,1]，

[1,2,3,0]，

[1,0,3,2]，

[1,0,3,2]]

taking [0,3,2,1] as an example, the line is the order of talent 'A' to positions 'A', 'B', 'C', 'D', i.e. the order of talent 'A' to positions is 'A', 'D', 'C', 'B'.

For [1,2,3,0], the line is the order of talent "B" for positions "a", "B", "C", "D", i.e. the order of talent "B" for positions is "D", "a", "B", "C" in sequence.

For [1,0,3,2], the line is the order of talent "C" for positions "A", "B", "C", "D", i.e. the order of talent "C" for positions is "B", "A", "D", "C".

For [1,0,3,2], the line is the order of talent "butyl" to positions "A", "B", "C", "D", i.e. the order of talent "butyl" to positions is "B", "A", "D", "C" in sequence.

Then, determining first talents in the expected position set to rank the first talents of each position in the expected position set, wherein the first position is any position in the expected position set, and the first talents are ranked according to the matching degree of the talents and the positions. Then, a one-to-one mapping relationship between each position in the desired position set and each talent in the talent information set may be established according to the first position ordering and the first talent ordering.

Wherein matrix O is used ^P Representing the status of post to talent to order, wherein

Represents the p < th _i For the t _j Arrange->

The bit is used to indicate the position of the bit,

smaller indicates higher priority.

Continuing with the example of 4 positions (A, B, C, D) and 4 talents (A, B, C, T), the following detailed description will be given:

for the situation of 4 positions and 4 talents, O ^p The method comprises the following steps:

[[0,1,2,3],

[1,3,2,0],

[2,0,3,1],

[2,0,1,3]]

taking [0,1,2,3] as an example, the row is the order of the talents "a", "b", "c", "t" by the position "a", that is, the order of the talents by the position "a" is "a", "b", "c", "t" in sequence.

For [1,3,2,0], the line is the rank of the position "B" for talents "a", "B", "c", "t", i.e. the rank of the position "B" for talents is "t", "a", "c", "B" in order.

For [2,0,3,1], the row is the order of the talents "A", "B", "C" and "T" by the position "C", that is, the order of the talents by the position "C" is "B", "T", "A" and "C" in sequence.

For [2,0,1,3], the row is the rank of the position "D" for talents "a", "b", "c", "t", i.e. the rank of the position "D" for talents "b", "c", "a", "t" in order.

Then, for each person t _i The initial state is marked as reject, with vector T ^rs The state of the device itself is indicated,

a True indicates that the status of the ith talent is refused, and False indicates that it is not refused. For example:

initial state T ^rs ＝[True,True,True,True]

Using a matrix P ^rt Representing the rejection relationship of posts to talents,

represents the p < th _i Whether or not to reject the t _j True indicates rejection, false indicates no rejection. For example:

P ^rt ＝[[False,False,False,False]，

[False,False,False,False]，

[False,False,False,False]]

then, the method can determine the set of the selected talents corresponding to each position according to the first position order, specifically, each talent delivers each position, each state is that the rejected talents are not delivered yet, and the position with the highest priority is delivered. That is, for each job seeker t _i ，

True, then pair ∈ ->

In which each t is found in ascending order of value _j If j does not reject t _i Then t is _i Join the job seeker list of j +.>

I.e. t _i Post p _j Wherein->

Representing a vector of values of all first indices i in the ordering matrix.

The 1 st round of delivery can be carried out continuously taking the conditions of 4 positions and 4 talents as an example, and the specific steps are as follows:

it is worth noting that for

Characterization talent "A" prefers delivery position "A", for +. >

Representation of talent "B" preferred delivery position "D", for +.>

Characterization talents "C" and "T" preferably deliver position "B", for +.>

The talent-free preferred position "C" is characterized.

After each person makes a "delivery", each job will deliver the message to the delivery person in O ^T The reject status of the most appropriate delivery talent is marked False. And marking other delivery person rejection states as True, and marking the post to reject the delivery person. The status matrix after the post refuses the delivered talents is updated as follows:

P ^rt ＝[[False,False,False,Flase],

[False,False,False,Flase],

[False,True,False,Flase],

[False,False,False,Flase]]

at this time, the delivery condition of each job position is updated as follows:

T ^rs ＝[False,False,True,False]

it should be noted that, since the job "B" is "t", "a", "c", and "B" in order of talents, when talents "c" and "t" are preferably delivery job "B", job "B" is preferably talent "t" and refuses talents "c".

And determining a talent rejecting set according to the first talent sorting and the talent collecting to be selected corresponding to each position, wherein the talent rejecting set is talent "propyl".

Then, determining a to-be-selected talent set corresponding to the positions of the unsuccessful matching talents according to the first position ordering and the to-be-selected talent set, and continuously determining a to-be-selected talent set according to the first talent ordering and the to-be-selected talent set corresponding to the positions of each unsuccessful matching talents until the to-be-selected talent set is an empty set.

Specifically, the delivery of round 2 is carried out aiming at talent 'C', if

It is worth to say that, the talent "C" is "B", "A", "D" and "C" in order of job positions. After talent "C" is rejected by job "B", talent "C" delivers job "A" in round 2 delivery.

It is worth noting that for

Characterization talents "A" and "C" preferred delivery position "A" for +.>

Representation of talent "B" preferred delivery position "D", for +.>

Characterization talent "T" preferred delivery position "B", for +.>

The talent-free preferred position "C" is characterized.

P ^rt ＝[[False,False,False,Flase],

[False,False,False,Flase],

[True,True,False,Flase],

[False,False,False,Flase]]

T ^rs ＝[False,False,True,False]

it should be noted that, since the ranks of the talents by the position "a" are "a", "b", "c" and "t" in order, when the talents "a" and "c" are preferably delivered to the position "a", the position "a" is preferably the talents "a" and the talents "c" are rejected.

At this time, the refused talents are still "C", and the 3 rd round delivery is carried out aiming at the talents "C", then

It is worth to say that, the talent "C" is "B", "A", "D" and "C" in order of job positions. After talent "C" is rejected by job "B" and "A", talent "C" is delivered to job "D" in round 3 delivery.

It is worth noting that for

Characterization talent "A" prefers delivery position "A", for +.>

Characterization talents "B" and "C" preferred delivery position "D", for +.>

Characterization talent "T" preferred delivery position "B", for +.>

The talent-free preferred position "C" is characterized.

After each person makes a "delivery", each job will deliver the message to the delivery person in O ^T In the order of (2) to be most suitable for the administrationThe rejection status of the delivery talent is marked False. And marking other delivery person rejection states as True, and marking the post to reject the delivery person. The status matrix after the post refuses the delivered talents is updated as follows:

P ^rt ＝[[False,False,False,Flase],

[False,False,False,Flase],

[True,True,False,True],

[False,False,False,Flase]]

T ^rs ＝[False,False,True,False]

it should be noted that, since the ranks of the talents in the position "D" are "b", "c", "a", "t", when the talents "b" and "c" are preferably delivered in the position "D", the position "D" is preferably the talents "b" and refuses the talents "c".

At this time, the refuse talents are still "C", and the 4 th round delivery is performed for "C", so that

It is worth to say that, the talent "C" is "B", "A", "D" and "C" in order of job positions. After talent "C" is rejected by job positions "B", "A" and "D", talent "C" is delivered to job position "C" in round 4 delivery.

It is worth noting that for

Characterization talent "A" prefers delivery position "A", for +.>

Characterization talents "B" and "C" preferred delivery position "D", for +.>

Characterization talent "T" preferred delivery position "B", for +.>

And (5) characterizing talent 'T' delivery position 'C'.

According to the matching step, a one-to-one mapping relationship between each position in the expected position set and each talent in the talent information set is established according to the talents successfully matched by each position.

Step 303, determining the target resource according to the position information of the target position and a preset resource algorithm.

After the target position corresponding to the job seeker is determined, the ideal treatment can be determined according to the position information of the target position and a preset treatment algorithm. The job position information comprises: one or more of yearly information, monthly salary information, public accumulation information, pension information, daily working time information and weekly working time information.

The preset treatment algorithm is as follows:

β＝a+log ₃₀₀₀ (b+c+d+h)-e-f+g

wherein β is a treatment, a is a yearly feeble number in the yearly feeble information, b is an equivalent yearly feeble in the yearly feeble information, c is an accumulation payment amount in the accumulation information, d is a pension payment amount in the accumulation information, e is a daily working time length in the daily working time length information, f is a weekly working time length in the weekly working time length information, g is an additional benefit amount, and h is an additional vacation time length.

And 304, displaying the target position and the positions in the position set in the search result list according to the distance value.

The ideal treatment is determined according to the position information of the target position and a preset treatment algorithm, and the distance value between the treatment corresponding to each position in the position set and the ideal treatment is determined, so that the absolute value of the difference value between the treatment corresponding to each position in the position set and the ideal treatment is worth understanding.

And then, sorting all positions in the position set according to the size of the distance value with ideal treatment to generate a position recommendation list, wherein the first position of the position recommendation list is a target position, and the rest positions are sequentially arranged from small to large according to the corresponding distance value. It is worth understanding that in this embodiment, the target positions are first matched, and then the positions are ordered according to the difference between each position and the target position, so that a list of the matching degree ordering of the positions and the job seekers is generated, the delivery efficiency of the job seekers is improved, and blind application of the job seekers is avoided.

In this embodiment, a target position is determined according to position information of a job applicant and a position keyword in the position search request and a preset matching algorithm, then a target resource is determined according to position information of the target position and a preset treatment algorithm, a distance value between a treatment corresponding to each position in a position set and an ideal treatment is determined, each position in the position set is ordered according to the distance value to generate a position recommendation list, and the position recommendation list is displayed, so that a global competitive relationship is considered, delivery efficiency of the job applicant is improved, and blind application of the job applicant is avoided.

Fig. 6 is a schematic structural diagram of a position recommendation device according to an exemplary embodiment of the present disclosure. As shown in fig. 6, the job position recommendation device 400 provided in this embodiment includes:

a request acquisition module 401, configured to acquire a job search request, where the job search request includes a job keyword;

the job matching module 402 is configured to determine a target job according to the job keyword, job seeker information and a preset matching algorithm, where the target job belongs to a job set;

the resource determining module 403 is configured to determine a target resource according to the position information of the target position and a preset resource algorithm, and determine a distance value between a resource corresponding to each position in the position set and the target resource;

And a job display module 404, configured to display the target job and the job in the job set in a search result list according to the distance value.

In one possible design, the job matching module 402 is specifically configured to:

determining a desired position set according to the position keywords;

determining whether the talent information comprises the skill word;

In one possible design, the preset resource algorithm is:

β＝a+log ₃₀₀₀ (b+c+d+h)-e-f+g

wherein β is a resource, a is a yearly fake number in the yearly fake information, b is an equivalent yearly firewood in the yearly fake information, c is an accumulation payment amount in the accumulation information, d is a pension payment amount in the accumulation information, e is a daily working time length in the daily working time length information, f is a weekly working time length in the weekly working time length information, g is an additional benefit amount, and j is an additional vacation time length.

It should be noted that, the job recommendation device provided in the embodiment shown in fig. 6 may be used to execute the method provided in any of the foregoing embodiments, and the specific implementation manner and technical effects are similar, and are not repeated herein.

Fig. 7 is a schematic structural diagram of an electronic device according to an example embodiment of the present disclosure. As shown in fig. 7, a schematic diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal having an image acquisition function such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a car-mounted terminal (e.g., car navigation terminal), etc., and a fixed terminal having an image acquisition device externally connected thereto such as a digital TV, a desktop computer, etc. The electronic device shown in fig. 7 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 7, the electronic device 500 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 501, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.

In general, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 507 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 508 including, for example, magnetic tape, hard disk, etc.; and communication means 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 7 shows an electronic device 500 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or from the storage means 508, or from the ROM 502. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 501.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects an internet protocol address from the at least two internet protocol addresses and returns the internet protocol address; receiving an Internet protocol address returned by the node evaluation equipment; wherein the acquired internet protocol address indicates an edge node in the content distribution network.

Alternatively, the computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The name of the unit does not in any way constitute a limitation of the unit itself, for example the first acquisition unit may also be described as "unit acquiring at least two internet protocol addresses".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. An information search method, comprising:

displaying the target position and positions in the position set in a search result list according to the distance value;

the determining the target position according to the position keyword, the job seeker information and the preset matching algorithm comprises the following steps:

determining a desired position set according to the position keywords;

2. The information search method according to claim 1, wherein the determining a desired job set from the job keyword, determining a delivery talent set from the job seeker, includes:

3. The information searching method according to claim 2, wherein if the N is greater than the M, selecting M positions before resource ordering from the first position set to form the desired position set, and the delivery talent set is the first talent set, wherein resources corresponding to each position in the first position set are calculated according to the preset resource algorithm, and descending order ordering is performed according to the resources corresponding to each position; or,

4. The information searching method according to any one of claims 1 to 3, wherein the establishing a one-to-one mapping relationship between each position in the desired position set and each talent in the delivery talent set according to the preset matching algorithm includes:

And establishing a one-to-one mapping relation between each position in the expected position set and each talent in the delivery talent set according to the first position ordering and the first talent ordering.

5. The information search method of claim 4, wherein the establishing a one-to-one mapping relationship between each of the desired set of positions and each of the delivery talents in the set of desired positions according to the first position order and the first talent order comprises:

and establishing a one-to-one mapping relation between each position in the expected position set and each talent in the delivery talent set according to the talents successfully matched with each position.

6. The information search method according to claim 5, wherein the matching degree is determined according to talent information, job information, and a preset skill score calculation rule, comprising:

determining whether the talent information comprises the skill word;

7. The information search method according to any one of claims 1 to 3, wherein the job information includes: one or more of yearly information, monthly salary information, public accumulation information, pension information, daily working time information and weekly working time information.

8. The information searching method according to claim 7, wherein the preset resource algorithm is:

β＝a+log ₃₀₀₀ (b+c+d+h)-e-f+g

9. A job position recommendation device, comprising:

the position display module is used for displaying the target position and the positions in the position set in a search result list according to the distance value;

the job position matching module is further used for determining a desired job position set according to the job position keywords; determining a delivery talent set according to the job seeker, wherein the delivery talent set comprises the job seeker, and the number of the job positions included in the expected job position set is the same as the number of the talents included in the delivery talent set; and establishing a one-to-one mapping relation between each position in the expected position set and each talent in the delivery talent set according to the preset matching algorithm, wherein the target position is the position mapped with the job seeker.

10. An electronic device, comprising:

a processing device; and

a storage device for storing executable instructions of the processing device;

wherein the processing means is configured to perform the information search method of any one of claims 1 to 8 via execution of the executable instructions.

11. A storage medium having stored thereon a computer program, characterized in that the program, when executed by a processing device, implements the information search method of any one of claims 1 to 8.