CN107766537B - Position searching and sorting method and computing device - Google Patents
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Abstract
The invention discloses a job searching and sorting method and a computing device suitable for executing the method, wherein the method is suitable for being executed in the computing device and comprises the following steps: acquiring basic position data related to each position; calculating the flow weight representing the situation that each position is accessed according to the position basic data of each position; creating an index file by generating index data of each position, wherein the index data comprises a position ID of each position, a company ID corresponding to the position and a flow weight of the position; searching at least one candidate position matched with the position of the request query from the index file in response to the search query request of the user; calculating the comprehensive score of each candidate position according to the similarity of each candidate position and the position requested to be inquired, the flow weight of each candidate position and the position basic data; and sequencing and displaying the at least one candidate position according to the sequence of the comprehensive score from high to low.
Description
Technical Field
The invention relates to the field of data processing, in particular to a job searching and sorting method and computing equipment.
Background
With the development of internet technology, network application/recruitment becomes a main approach for job seekers to apply work and recruit employees by employing units. The personnel units and job seekers register accounts on the talent recruitment website of the third party and search and communicate with each other to search for the most satisfactory objects.
The talent recruitment website has a large number of positions, and after an applicant logs in the website, the positions can be browsed from the position list, and the positions which can be suitable for the applicant can be searched by means of keywords. Generally, the number of job listings is huge, and the applicant cannot find the job in which the applicant is interested in a short time; and the keyword search is used, the number and the accuracy of the keywords used by the applicant are limited, the number of searched positions is often very large, and the position suitable for the applicant is difficult to find quickly. In actual business, positions displayed more ahead are more easily concerned by job seekers and receive more resumes, but sometimes too many resumes (human resource departments) cannot be processed in time; and other positions displayed later may receive few resumes, and the demands of the positions on the resumes cannot be met at all. The two polarization conditions can greatly reduce the effective delivery rate of the resume, thereby reducing the probability of acquiring interviews by job seekers and increasing the difficulty of recruiting companies.
Therefore, a method for balancing the ranking of positions during position search is needed to solve the above drawbacks.
Disclosure of Invention
To this end, the present invention provides a job search ranking method and computing device in an effort to solve, or at least alleviate, at least one of the problems identified above.
According to one aspect of the invention, a job search ranking method is provided, which is suitable for being executed in a computing device and comprises the following steps: acquiring basic position data related to each position; calculating the flow weight representing the situation that each position is accessed according to the position basic data of each position; creating an index file by generating index data of each position, wherein the index data comprises a position ID of each position, a company ID corresponding to the position and a flow weight of the position; searching at least one candidate position matched with the position of the request query from the index file in response to the search query request of the user; calculating the comprehensive score of each candidate position according to the similarity of each candidate position and the position requested to be inquired, the flow weight of each candidate position and the position basic data; and sequencing and displaying the at least one candidate position according to the sequence of the comprehensive score from high to low.
Optionally, in the job searching and sorting method according to the present invention, the job basic data includes a promotion state of the job, release time and refresh time of the job, exposure amount and delivery amount of the job, processing condition of resume corresponding to the job, and a company financing state and an authentication state corresponding to the job.
Optionally, in the job search ranking method according to the present invention, the step of calculating a traffic weight representing an accessed situation of each job based on the job basic data of the job includes: generating a candidate position set of a search engine according to the number of times of position inquiry; calculating a traffic score for each position in the set of candidate positions based on position profile data; calculating the mean and variance of flow scores in the candidate position set according to the flow score of each position; and carrying out normalization processing on the position score of each position according to the mean value and the variance to obtain the flow weight of each position.
Alternatively, in the job search ranking method according to the present invention, the step of calculating a traffic score for each job in the candidate job set based on the job basic data includes: calculating the human resource score of the position according to the processing condition of the resume corresponding to the position; calculating the commercial promotion score of the position according to the promotion state of the position; calculating the time score of the position according to the first release time and the latest refreshing time of the position; calculating the company financing score of the position according to the company financing state corresponding to the position; calculating the company authentication score of the position according to the company authentication state corresponding to the position; calculating the browsing volume of the position according to the exposure and the delivery volume of the position in the first preset time; and calculating the flow score of the corresponding position based on the human resource score, the commercial promotion score, the aging score, the company financing score, the company authentication score and the browsing amount.
Optionally, in the job search ranking method according to the present invention, the traffic weight Score of the ith jobflow_iThe calculation is performed as follows:
where mean and var represent the mean and variance, x, of the job scores in the candidate job set, respectivelyiRepresenting the flow score for the ith position.
Optionally, in the ranking method for job search according to the present invention, the flow score x of the ith jobiThe calculation is performed as follows:
wherein PViRepresents the ithBrowsing volume of job, Scorehr_iScore, human resources Score, representing the ith positionpromot_iScore, a commercial promotion Score representing the ith positiontime_iRepresents the age Score, of the ith positionfinance_iScore, a company financing Score representing the ith positionapprove_iA company certification score representing the ith position.
Optionally, in the method for searching and ranking positions according to the present invention, the step of creating an index file by generating index data for each position further includes: reacquiring the job basic data related to each job at intervals of second preset time and calculating the traffic weight; and updating the index file according to the newly acquired job basic data and the traffic weight.
Optionally, in the job search ranking method according to the present invention, the step of calculating a composite score of each candidate job based on the similarity of each candidate job to the job requested to be queried, the traffic weight of each candidate job, and the job basic data includes: calculating the similarity between each candidate position and the position requested to be inquired to obtain a similarity value; calculating a non-flow score of each candidate position according to the position basic data of each candidate position; and carrying out weighted calculation on the similarity value, the traffic weight and the non-traffic score of each candidate position to obtain a comprehensive score of each candidate position.
Optionally, in the position search ranking method according to the present invention, TotalScore is a total score of jth candidate positionjThe calculation is performed as follows:
TotalScorej＝wsim×Scoresim_j+wrank×(wflow×Scoreflow_j+(1-wflow)×Scorenonflow_j)，
wherein, wsim、wflowRespectively representing weighting factors, w, corresponding to the similarity value and the traffic weightrankRepresenting weighting factors characterizing the access of the user, Scoresim_jRepresenting the similarity value of the jth candidate position, Scoreflow_jTraffic weight, Score, representing the jth candidate positionnonflow_jRepresenting the non-flow score for the jth candidate position.
Optionally, in the ranking method for job search according to the present invention, the non-traffic score of the job includes: the human resource classification of the positions, the sorting state classification of the positions and the manual intervention classification.
Optionally, in the job search ranking method according to the present invention, values of the weighting factors are respectively: w is asim＝1000，wflow＝0.5，wrank＝600。
Alternatively, in the job search ranking method according to the present invention, the first predetermined time is 7 days.
Alternatively, in the job search ranking method according to the present invention, the second predetermined time is 5 minutes.
According to another aspect of the present invention, there is provided a computing device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described above.
According to a further aspect of the invention, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
According to the job position searching and sorting scheme, the flow weight representing the accessed situation of the job position is calculated according to the basic data of the job position, namely, the flow weight is calculated according to the resume processing situation of HR of the issued job position and other factors of the job position (such as popularization state of the job position, exposure amount and delivery amount of the job position, scale of a company corresponding to the job position, financing state of the company, authentication state and the like). Then, the flow weight is added into the returned result of the search query request for sorting, so that the positions which are viewed less frequently (namely, the exposure is lower) in the past obtain higher ranking, and the corresponding positions which are viewed more frequently (namely, the exposure is higher) obtain a corresponding lower ranking, thereby achieving the effect of dynamic balance of the flow and avoiding the occurrence of the two-pole differentiation condition.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 illustrates a functional block diagram of a search engine 100 according to one embodiment of the present invention;
FIG. 2 shows a schematic diagram of a computing device 200 according to an embodiment of the invention; and
fig. 3 illustrates a flow diagram of a job search ranking method 300 according to one embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 is a block diagram illustrating an operation of a search engine 100 for searching information according to an embodiment of the present invention. Referring to fig. 1, a search engine 100 is connected to a push center 20 and a client 30 through a network, which may include a Local Area Network (LAN), a Wide Area Network (WAN), and a telephone network. The push center 20 actively submits the structured data to the search engine 100 in a predetermined form so that the search engine 100 can provide a service for job search in response to a job search request of the browser 32 of the client 30. Here, the push center 20 that pushes the index message may be a database, a push job, which provides structured data to the search engine 100; of course, a crawler system may also be included that provides web page data to the search engine 100. According to one implementation of the present invention, the push center 20 includes at least a distributed file storage system HSDF (for obtaining a search presentation log of the client 30), a Redis (for obtaining frequently accessed data on a job) and a general DataBase (for obtaining company details, job details data, job delivery volume, and the like data related to a job).
According to one implementation, the search engine 100 may include one or more web server entities for storing and managing data and responding to job search requests. Client 30 may include one or more user terminal devices such as a personal computer, laptop, wireless telephone, Personal Digital Assistant (PDA), or other computer device and communication device. Both the server and the terminal devices are architecturally comprised of basic components such as a bus, processing means, storage means, one or more input/output means, and a communications interface. The detailed structural description is described below with reference to fig. 2.
As shown in fig. 1, the search engine 100 receives a job search request from the client 30, and searches results from the data storage 110 through a search module in the search component 120, for example, the search module performs union operation of document indexes according to the word segmentation results and the filtering conditions to obtain and summarize a matched document set; the document set is then subjected to a customized ranking by a ranking module (the ranking method for the search positions in the embodiment of the present invention will be described below with reference to fig. 3), and then presented by the user interface 130.
According to one implementation, the search engine 100 collects a batch of data in advance and stores the data in the data store 110 in some manner. According to an embodiment of the present invention, the data repository 110 includes an index file 112, which is constructed by using an index module in the open source Lucene project. In this embodiment, the data storage repository 110 further includes a basic data caching module 114, configured to store the job basic data obtained from the push center 20, and construct an index file according to the basic data. Optionally, the job basic data includes a promotion state of the job, release time and refresh time of the job, daily exposure and delivery amount of the job, processing conditions of a resume corresponding to the job, and a company financing state and an authentication state corresponding to the job. In addition, in the present embodiment, the basic data caching module 114 initializes to the latest data each time the index file needs to be updated, and then provides the latest data to the index file 112.
FIG. 2 shows a schematic diagram of a computing device 200, according to one embodiment of the invention. In a basic configuration 202, computing device 200 typically includes system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between the processor 204 and the system memory 206.
Depending on the desired configuration, the processor 204 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a Digital Signal Processor (DSP), or any combination thereof. The processor 204 may include one or more levels of cache, such as a level one cache 210 and a level two cache 212, a processor core 214, and registers 216. Example processor cores 214 may include Arithmetic Logic Units (ALUs), Floating Point Units (FPUs), digital signal processing cores (DSP cores), or any combination thereof. The example memory controller 218 may be used with the processor 204, or in some implementations the memory controller 218 may be an internal part of the processor 204.
Depending on the desired configuration, system memory 206 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include an operating system 220, one or more applications 222, and program data 224. The application 222 is actually a plurality of program instructions that direct the processor 204 to perform corresponding operations. In some embodiments, application 222 may be arranged to cause processor 204 to operate with program data 224 on an operating system.
Computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus/interface controller 230. The example output device 242 includes a graphics processing unit 248 and an audio processing unit 250. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 252. Example peripheral interfaces 244 can include a serial interface controller 254 and a parallel interface controller 256, which can be configured to facilitate communications with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 258. An example communication device 246 may include a network controller 260, which may be arranged to facilitate communications with one or more other computing devices 262 over a network communication link via one or more communication ports 264.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
Fig. 3 illustrates a flow diagram of a job search ranking method 300 according to one embodiment of the invention. As shown in fig. 3, the method 300 begins at step S310.
In step S310, job basic data related to each job is acquired. Optionally, the job basic data includes a promotion state of the job, release time and refresh time of the job, exposure and delivery amount of the job, processing condition of resume corresponding to the job, and company financing state and authentication state corresponding to the job.
As previously described, in the examples according to the present invention, data collection was mainly performed by HSDF, Redis, and DataBase. Taking HSDF as an example, reading a search display log of the client 30 (including the APP and PC) in the last 7 days from HSDF, counting the display times of different positions according to the day, and summing up to calculate the display times of each position in the last 7 days. Taking Redis as an example, since Redis is a Key-Value storage system, the read-write performance is excellent, all operations are atomic, and multi-thread access of searching can be supported, the daily exposure of the frequently accessed data similar to positions is stored in Redis. And the DataBase databank mainly stores description data related to the positions in the form of Map, such as release time and refresh time of the positions, delivery amount of the positions, company financing state and authentication state corresponding to the positions, and the like. In addition, the popularization state of the position, the processing condition of the resume corresponding to the position, manual intervention score adding on the position and the like can be obtained through the basic terminal service interface. It should be noted that the above example is only an exemplary description of the acquired job basic data, and in practical applications, the job basic data may be limited according to an application scenario, which is not limited by the present invention.
Subsequently, in step S320, a traffic weight representing the situation in which the position is accessed is calculated from the acquired position basic data of each position. According to an implementation manner of the present invention, the step S320 includes the following steps 1) to 4).
1) And generating a candidate position set of the search engine according to the number of times the position is queried. The number of times of job inquiry is counted by searching the display log, and optionally 5000 jobs with the largest number of times of job display in the past month are counted to generate a candidate job set.
2) A traffic score for each position in the set of candidate positions is calculated based on the position profile. The following describes parameters for calculating the flow rate score.
a. Human resources Scorehr
Calculating the human resource Score of the position according to the processing condition of the resume corresponding to the position acquired through the basic terminal service interfacehr. Optionally, the resume processing score DR of the HR is predicted by collecting the resume information received by each position, the resume deliverer information, and the resume processing record of the HR for each position (for example, establishing an HR resume processing prediction model), since the time prediction of the HR processing resume is not the main content of the embodiment, it is not described here, in short, any executable manner can be adopted to obtain the resume processing score DR of the HR, then,
the following table lists some values of DR and the corresponding Scorehr。
| HR processing resume score DR | DR2 | Scorehr |
| 0 | 0 | 1.00 |
| 0.1 | 0.01 | 1.02 |
| 0.2 | 0.04 | 1.07 |
| 0.3 | 0.09 | 1.17 |
b. Commercial promotional Scorepromot
Calculating the commercial promotion Score of the position according to the promotion state of the positionpromot. According to one embodiment, each promotion state for a position corresponds to a weight WP, then Scorepromot＝3WP. The following table exhales the Score corresponding to each promotion statepromot。
| Promotion status | WP | Scorepromot |
| General purpose | 0 | 1.00 |
| Non-emphasis points | 0.6 | 1.93 |
| High quality | 0.8 | 2.41 |
| Emphasis is given to | 1 | 3.00 |
c. Age division Scoretime
And calculating the time score of the position according to the first release time and the latest refreshing time of the position. Setting day as first release time and current time intervalcreateDay is the last refresh time separated from the current time by daysrefreshThen the release time weight is
The refresh time is weighted by
The comprehensive aging weight is wt ═ wc + wr)/2, then the aging Score is Scoretime＝5wt. The following table lists the current daycreate＝dayrefreshScore when day corresponds totime。
| day | wt | Scoretime |
| 0 | 1.00 | 5.00 |
| 1 | 0.93 | 4.47 |
| 2 | 0.87 | 4.03 |
| 3 | 0.81 | 3.67 |
| 4 | 0.75 | 3.36 |
d. Company financing Scorefinance
Calculating the company financing Score of the position according to the company financing state corresponding to the positionfinance. Optionally, there is a weight WF for each financing stage of the company, and then Scorefinance＝1.2WF. The following table exhales the Score corresponding to each financing stagefinance。
| Financing phase | WF | Scorefinance |
| Non-financing | 0 | 1.00 |
| Angel wheel | 0.3 | 1.06 |
| A wheel | 0.5 | 1.10 |
| Without the need for financing | 0.7 | 1.14 |
| Listed Co Ltd | 0.7 | 1.14 |
| B wheel | 0.8 | 1.16 |
| C wheel | 0.8 | 1.16 |
| D wheel and above | 1 | 1.20 |
e. Company authentication Scoreapprove
Calculating the company authentication Score of the position according to the corresponding company authentication state of the positionapprove. OptionallyIn the ground, each authentication status of the company corresponds to a weight WA, and then, Scoreapprove＝1.2WA. The following table exhales the Score corresponding to each authentication stateapprove。
| Authentication status | WA | Scoreapprove |
| Authenticated | 1 | 1.2 |
| Is not authenticated | 0 | 1 |
f. Volume of view PV
The browsing volume PV of a position is calculated based on the exposure and delivery volume of the position for a first predetermined time (e.g., 7 days). Assuming that the exposure is P and the delivery is D, the final PV value is determined by the following equation:
PV＝wp×P+wd×Dm，
in the above formula, the weighting factors of the exposure amount P and the delivery amount D are set as follows: w is ap＝wdThe variable m takes 2.
Finally, a flow score of the corresponding position, for example, the flow score x of the ith position is calculated based on the parameters a to f calculated aboveiThe calculation is performed as follows:
wherein PViRepresents the browsing volume of the ith position, Scorehr_iScore, human resources Score, representing the ith positionpromot_iScore, a commercial promotion Score representing the ith positiontime_iRepresents the age Score, of the ith positionfinance_iScore, a company financing Score representing the ith positionapprove_iA company certification score representing the ith position.
It should be noted that the present embodiment only exemplifies the above parameters, and in practical applications, based on the parameter statistical method provided by the present embodiment, other relevant parameters of each position can be calculated statistically, such as calculating the effective delivery rate of the position by counting the number of interview opportunities obtained after the delivery resume, etc.
3) And calculating the mean and variance of the flow scores in the candidate position set according to the flow score of each position. The calculation of the mean and variance is subject to basic statistical knowledge and is not described herein.
4) And normalizing the position score of each position according to the mean value and the variance to obtain the flow weight of each position. Due to different ranges of the calculated parameters, the job scores are distributed in [0, 1 ] through normalization processing]Within the interval (border values are taken directly for values less than 0 or greater than 1). For example, the traffic weight Score for the ith positionflow_iThe calculation is performed as follows:
where mean and var represent the mean and variance, x, of the job scores in the candidate job set, respectivelyiRepresenting the flow score for the ith position.
Subsequently, in step S330, an index file is created by generating index data for each position, wherein the index data includes a position ID for each position, a company ID corresponding to the position, and a traffic weight for the position. Optionally, the index file may further include job names, job descriptions for each job, and the like.
According to the implementation of the present invention, as described above, the index file is updated periodically: reacquiring the job basic data associated with each job at every second predetermined time (e.g., 5 minutes) and calculating a traffic weight; and updating the index file according to the newly acquired job basic data and the traffic weight. So as to ensure the timeliness of the index file.
Subsequently, in step S340, in response to the search query request of the user, at least one candidate position matching the position of the request query is searched from the index file.
According to one implementation, based on the job of the requested query, the user enters one or more corresponding search terms or phrases or descriptions, and at least one candidate job is obtained by calculating the textual similarity of the search terms entered by the user to the job names (or job descriptions) in the index file. The embodiment of the present invention does not limit the specific implementation manner of calculating the candidate positions.
Then, in step S350, a composite score of each candidate position is calculated based on the similarity of each candidate position to the position requested to be queried, the traffic weight of each candidate position, and the position profile.
According to one embodiment, the step of calculating a composite score for each candidate position includes:
(1) the similarity between each candidate position and the position requested to be queried is first calculated to obtain a similarity value (e.g., the similarity value is calculated according to step S340).
(2) And calculating the non-flow score of the position according to the position basic data of each candidate position. According to one embodiment of the invention, the non-flow score for the position comprises: the human resource classification of the positions, the sorting state classification of the positions and the manual intervention classification. Wherein the human resources are as described above; the manual intervention pre-distribution is a score manually set for positions, such as punishment scores set for malicious positions; the ranking state score can be obtained by linearly adding several items of the business promotion score, the company financing score, the company authentication score and the time-efficiency score of the position.
(3) And carrying out weighted calculation on the similarity value, the traffic weight and the non-traffic score of each candidate position to obtain the comprehensive score of each candidate position.
For example, TotalScore for the composite score of the jth candidate positionjThe calculation is performed as follows:
TotalScorej＝wsim×Scoresim_j+wrank×(wflow×Scoreflow_j+(1-wflow)×Scorenonflow_j)，
wherein, wsim、wflowRespectively representing weighting factors, w, corresponding to the similarity value and the traffic weightrankRepresenting weighting factors characterizing the access of the user, Scoresim_jRepresenting the similarity value of the jth candidate position, Scoreflow_jTraffic weight, Score, representing the jth candidate positionnonflow_jRepresenting the non-flow score for the jth candidate position. Optionally, the weighting factors take the following values: w is asim＝1000，wflow＝0.5，wrank＝600。
Finally, in step S360, at least one candidate position is ranked and displayed in the order of the composite score from high to low.
According to the job position searching and sorting scheme, the flow weight representing the accessed situation of the job position is calculated according to the basic data of the job position, namely, the flow weight is calculated according to the resume processing situation of HR of the issued job position and other factors of the job position (such as popularization state of the job position, exposure amount and delivery amount of the job position, scale of a company corresponding to the job position, financing state of the company, authentication state and the like). Then, the flow weight is added into the returned result of the search query request for sorting, so that the positions which are viewed less frequently (namely, the exposure is lower) in the past obtain higher ranking, and the corresponding positions which are viewed more frequently (namely, the exposure is higher) obtain a corresponding lower ranking, thereby achieving the effect of dynamic balance of the flow and avoiding the occurrence of the two-pole differentiation condition.
Through the job position searching and sorting scheme, the exposure of the job positions (namely the number of times of checking the job positions) can be increased, the resume processing speed is accelerated, and the satisfaction degrees of both the employing party and the recruiting party are improved.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in a device as described in this embodiment or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
The invention also discloses:
a9 method as in A8, wherein the total score TotalScore for the jth candidate positionjThe calculation is performed as follows:
TotalScorej＝wsim×Scoresim_j+wrank×(wflow×Scoreflow_j+(1-wflow)×Scorenonflow_j)，
wherein, wsim、wflowRespectively representing weighting factors, w, corresponding to the similarity value and the traffic weightrankRepresenting weighting factors characterizing the access of the user, Scoresim_jRepresenting the similarity value of the jth candidate position, Scoreflow_jTraffic weight, Score, representing the jth candidate positionnonflow_jRepresenting the non-flow score for the jth candidate position.
A10, the method of a9, wherein the non-traffic score for the position comprises: the human resource classification of the positions, the sorting state classification of the positions and the manual intervention classification.
A11, the method as in a9, wherein the weighting factors take the following values: w is asim＝1000，wflow＝0.5，wrank＝600。
A12, the method of any one of A4-11, wherein the first predetermined time is 7 days.
A13, the method of any one of A7-12, wherein the second predetermined time is 5 minutes.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the mobile terminal generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to perform the method of the invention according to instructions in said program code stored in the memory.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.
Claims (12)
1. A method of job search ranking, the method being adapted for execution in a computing device, the method comprising the steps of:
acquiring basic position data related to each position;
calculating the flow weight representing the situation that each position is accessed according to the position basic data of each position;
creating an index file by generating index data of each position, wherein the index data comprises a position ID of each position, a company ID corresponding to the position and a flow weight of the position;
searching at least one candidate position matched with the position of the request query from the index file in response to the search query request of the user;
calculating the comprehensive score of each candidate position according to the similarity of each candidate position and the position requested to be inquired, the flow weight of each candidate position and the position basic data; and
at least one candidate position is displayed in a ranking mode according to the sequence of the comprehensive scores from high to low,
wherein, the step of calculating the traffic weight representing the situation that the position is accessed according to the position basic data of each position comprises the following steps:
generating a candidate position set of a search engine according to the number of times of position inquiry;
calculating a traffic score for each of the set of candidate positions based on position profile data, comprising the steps of:
calculating the human resource score of the position according to the processing condition of the resume corresponding to the position,
calculating the commercial promotion score of the position according to the promotion state of the position,
calculating the time score of the position according to the first release time and the latest refresh time of the position,
calculating the company financing score of the position according to the company financing state corresponding to the position,
calculating the company authentication score of the position according to the corresponding company authentication state of the position,
calculating the browsing volume of the position according to the exposure and the delivery volume of the position in the first preset time,
calculating the flow score of the corresponding position based on the human resource score, the commercial promotion score, the aging score, the company financing score, the company authentication score and the browsing amount; calculating the mean and variance of flow scores in the candidate position set according to the flow score of each position; and
and normalizing the position score of each position according to the mean value and the variance to obtain the flow weight of each position.
2. The method as claimed in claim 1, wherein the job basic data includes a promotion status of the job, a release time and a refresh time of the job, an exposure amount and a delivery amount of the job, a processing status of a resume corresponding to the job, and a company financing status and an authentication status corresponding to the job.
3. The method as claimed in claim 2, wherein the traffic weight Score for the ith positionflow_iThe calculation is performed as follows:
where mean and var represent the mean and variance, x, of the job scores in the candidate job set, respectivelyiThe traffic score representing the ith position.
4. The method of claim 3, wherein the flow score x for the ith positioniThe calculation is performed as follows:
wherein PViRepresents the browsing volume of the ith position, Scorehr_iThe human resources Score, representing the ith positionpromot_iThe commercial offer Score, representing the ith positiontime_iRepresents the age Score, of the ith positionfinance_iScore, the company financing Score representing the ith positionapprove_iThe company certification score representing the ith position.
5. The method of claim 4, wherein the creating of the index file by generating the index data for each position further comprises:
reacquiring the job basic data related to each job at intervals of second preset time and calculating the traffic weight; and
and updating the index file according to the newly acquired job basic data and the traffic weight.
6. The method of claim 5, wherein the calculating of the composite score for each candidate position based on the similarity of each candidate position to the position requested to be queried, the traffic weight for each candidate position, and the position profile comprises:
calculating the similarity between each candidate position and the position requested to be inquired to obtain a similarity value;
calculating a non-flow score for the position according to the position profile data for each candidate position, wherein the non-flow score for the position comprises: the human resource score of the position, the ranking state score of the position and the manual dry pre-score, wherein the ranking state score is obtained by adding the commercial promotion score, the company financing score, the company authentication score and the time efficiency score of the position, and the manual dry pre-score is a manually set score; and
and carrying out weighted calculation on the similarity value, the traffic weight and the non-traffic score of each candidate position to obtain the comprehensive score of each candidate position.
7. The method of claim 6, wherein the total score TotalScore for the jth candidate positionjThe calculation is performed as follows:
TotalScorej＝wsim×Scoresim_j+wrank×(wflow×Scoreflow_j+(1-wflow)×Scorenonflow_j)，
wherein, wsim、wflowRespectively representing weighting factors, w, corresponding to the similarity value and the traffic weightrankRepresenting weighting factors characterizing the access of the user, Scoresim_jRepresenting the similarity value of the jth candidate position, Scoreflow_jTraffic weight, Score, representing the jth candidate positionnonflow_jThe non-traffic score representing a jth candidate position.
8. The method of claim 7, wherein the weighting factors take on values of: w is asim＝1000，wflow＝0.5，wrank＝600。
9. The method of claim 8, wherein the first predetermined time is 7 days.
10. The method of any one of claims 5-9, wherein the second predetermined time is 5 minutes.
11. A computing device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-10.
12. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-10.
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| CN112365148A (en) * | 2020-11-06 | 2021-02-12 | 中国平安人寿保险股份有限公司 | Staff sorting method, device, equipment and storage medium based on multiple dimensions |
| CN114153963A (en) * | 2021-11-30 | 2022-03-08 | 北京达佳互联信息技术有限公司 | Document recommendation method and device, computer equipment and medium |
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