CN111597441B - Information processing method and device and electronic equipment - Google Patents
Information processing method and device and electronic equipment Download PDFInfo
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- CN111597441B CN111597441B CN202010374977.5A CN202010374977A CN111597441B CN 111597441 B CN111597441 B CN 111597441B CN 202010374977 A CN202010374977 A CN 202010374977A CN 111597441 B CN111597441 B CN 111597441B
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
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Abstract
According to the information processing method, the information processing device and the electronic equipment, after the personal introduction information of the user is obtained, at least one keyword in the personal introduction information is extracted, and whether the personal introduction information accords with the target position or not is determined by analyzing the at least one keyword and the target position condition. After the personal introduction information is acquired, whether the personal introduction information accords with the target position is not required to be analyzed manually, and the relation between the keywords in the personal introduction information and the target position condition is compared through the server, so that whether the personal introduction information accords with the target position is determined. And the matching degree of the personal introduction information and the target position is automatically analyzed through the machine, so that the labor cost can be saved, the matching accuracy is improved, meanwhile, the efficiency of analyzing the personal introduction information is improved, and the time for seeking talents is saved.
Description
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to an information processing method and apparatus, and an electronic device.
Background
At present, when a company recruits people, a recruitment brief is usually issued on a recruitment platform, and the types of talents needing to be recruited are usually marked on the recruitment brief. Thus, the resume can be conveniently delivered by job hunting personnel.
Before delivering the resume, the job seeker usually notes the basic situation or the work expectation of the job seeker according to the resume, so that the company can quickly know the job seeker, and the HR (Human Resource) can perform preliminary screening on the job seeker.
However, in the prior art, the HR needs to acquire the resume from the recruitment platform and then observe whether the resume meets the needs of the company, and this process can be understood as the evaluation of the resume. Because there are many resumes on the recruitment platform, the HR can only view a small portion of the resumes from which the engaging person is then determined, but this approach can make the last engaging person a bit different from the one the company desires to recruit.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides an information processing method, an information processing device and electronic equipment, which can quickly determine whether personal introduction information conforms to a target position.
In a first aspect, an embodiment of the present disclosure provides an information processing method, including: acquiring personal introduction information of a user; extracting at least one keyword from the personal introduction information, wherein the at least one keyword is used for representing personal basic information of the user from at least one dimension; determining the matching degree of the personal introduction information and the target position according to the at least one keyword and the target position condition of the target position; and determining whether the personal introduction information conforms to the target position or not according to the matching degree.
In a second aspect, an embodiment of the present disclosure provides an information processing apparatus, including: an acquisition unit configured to acquire personal introduction information of a user; an extracting unit, configured to extract at least one keyword from the personal introduction information, where the at least one keyword is used to represent personal basic information of a user from at least one dimension; a first determining unit, configured to determine a matching degree between the personal introduction information and a target position according to the at least one keyword and a target position condition of the target position; and the second determining unit is used for determining whether the personal introduction information conforms to the target position according to the matching degree.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device, configured to store one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the information processing method according to the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the steps of the information processing method as described above in the first aspect.
According to the information processing method and device and the electronic equipment, after the personal introduction information of the user is obtained, at least one keyword in the personal introduction information is extracted, and whether the personal introduction information meets the target position or not is determined by analyzing the at least one keyword and the target position condition. After the personal introduction information is acquired, whether the personal introduction information accords with the target position is not required to be analyzed manually, and the relation between the keywords in the personal introduction information and the target position condition is compared through the server, so that whether the personal introduction information accords with the target position is determined. And the matching degree of the personal introduction information and the target position is automatically analyzed through the machine, so that the labor cost can be saved, the matching accuracy is improved, meanwhile, the efficiency of analyzing the personal introduction information is improved, and the time for seeking talents is saved.
Drawings
The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a flow diagram of one embodiment of an information processing method according to the present disclosure;
FIG. 2 is a flow diagram of another embodiment of an information processing method according to the present disclosure;
FIG. 3 is a flow diagram of yet another embodiment of an information processing method according to the present disclosure;
FIG. 4 is a flow diagram of yet another embodiment of an information processing method according to the present disclosure;
FIG. 5 is a flow diagram of yet another embodiment of an information processing method according to the present disclosure;
FIG. 6 is a flow diagram of yet another embodiment of an information processing method according to the present disclosure;
FIG. 7 is a schematic block diagram of one embodiment of an information processing apparatus according to the present disclosure;
FIG. 8 is an exemplary system architecture to which the information processing method of one embodiment of the present disclosure may be applied;
fig. 9 is a schematic diagram of a basic structure of an electronic device provided according to an 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 are shown in the 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 rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, 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 "include" and variations thereof as used herein are 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". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Referring to fig. 1, a flow of one embodiment of an information processing method according to the present disclosure is shown. The information processing method can be applied to a server. The information processing method as shown in fig. 1 includes the steps of:
In some embodiments, the server may obtain the personal referral information issued by the user on the recruitment platform, or obtain the personal referral information from a talent mailbox of the company (that is, the user may deliver the personal referral information to a favorite company by himself, and the personal referral information may be stored in the talent mailbox of the company); and the personal introduction information may be understood as information for introducing the user. For example: the work unit, the learning experience, the height, the age and the like of the user can be obtained through the human introduction information. That is, the basic situation of the user can be known through the personal information of the user.
In some embodiments, the personal introduction information may be text information (e.g., resume), voice information, video information, or the like. The carrier of the personal introduction is not limited herein.
Here, at least one keyword is used to characterize the personal basic information of the user from at least one dimension.
In some embodiments, the content included in the personal introduction information may be more, and it may also be understood that the personal introduction information includes information of multiple dimensions of the user. Here, the plurality of dimensions may be divided into: competency, occupation, age, hobbies, home address, etc.; it can also be divided into: post application, work skills, work industry, expected salaries, academic records, expected work and the like. That is, information of multiple dimensions of a user can be understood as information of multiple aspects of the user.
According to the divided dimensionality, natural semantic processing can be carried out on the personal introduction information to determine at least one keyword. For example, the determined keywords may include: ' age of user ', ' school of graduation ', ' taste of user ', ' name of company the user has been to have, etc. That is, according to the determined at least one keyword, the general situation of the user in various aspects can be known.
And 103, determining the matching degree of the personal introduction information and the target position according to the at least one keyword and the target position condition of the target position.
In some embodiments, each position has a corresponding position condition, and the position condition can be understood as a requirement of the position. Such as: a job position is 'big data product manager', and the job position conditions of the job position may include: the study calendar of the department is more than 5 years of work experience of related products such as artificial intelligence and the like, has leadership, and is familiar with the artificial intelligence industry, the artificial intelligence product design method and the like. That is, the target job condition defines the ability that the user who is to be assigned to the target job should have, and the at least one keyword introduces the ability that the user has.
Furthermore, the matching degree of the user and the target position can be determined according to the at least one keyword. Such as: the at least one keyword includes: 'doctor', 'AI' 10 years '(by these keywords, it is possible or the user's academic story is doctor, with 10 years of AI work experience); and the target job conditions may be: the system has more than a master's academic calendar, and has more than 5 years of working experience in the field of artificial intelligence'; at this time, it can be seen that the ability of the user satisfies the ability defined in the target position condition, and thus a high degree of matching between the at least one keyword and the target position condition can be represented.
And step 104, determining whether the personal introduction information conforms to the target position according to the matching degree.
In some embodiments, when the matching degree is higher, the personal introduction information may be characterized to conform to the target position, that is, the user may be characterized to be competent for the target position; and when the matching degree is lower, the personal introduction information which does not conform to the target position can be represented, namely, the user which cannot be qualified for the target position can be represented.
Therefore, in some embodiments, a threshold may be preset, and when the matching degree of the personal introduction information and the target position is greater than the threshold, the personal introduction information is characterized to be in accordance with the target position. And when the matching degree of the personal introduction information and the target position is not more than the threshold value, the personal introduction information can be represented to be not in accordance with the target position.
It can be seen that, in the embodiment of the present disclosure, after the personal introduction information of the user is obtained, at least one keyword in the personal introduction information is extracted, and whether the personal introduction information meets the target position is determined by analyzing the at least one keyword and the target position condition. After the personal introduction information is acquired, whether the personal introduction information accords with the target position is not required to be analyzed manually, and the relation between the keywords in the personal introduction information and the target position condition is compared through the server, so that whether the personal introduction information accords with the target position is determined. And the matching degree of the personal introduction information and the target position is automatically analyzed through the machine, so that the labor cost can be saved, the matching accuracy is improved, meanwhile, the efficiency of analyzing the personal introduction information is improved, and the time for seeking talents is saved.
Referring to fig. 2, in some embodiments, the target job condition is generated by:
Here, the job description information is used to describe skills that should be possessed by a practitioner corresponding to the target job.
In some embodiments, when a company lacks a certain type of talent, the characteristics of the type of talent may be described, and the described information may be referred to as job description information.
The first keyword set can be understood as a set of keywords for describing the user ability or characteristics in the job description information. Such as: when a company lacks a driver, the job description may be: above the C1-grade driving license, 5 years of driving experience exists, and no major traffic accidents occur'; and the keywords in the first keyword set may include: 'C1 driving license', '5 years' and 'no accident'.
In some embodiments, a position identifier for querying the first keyword set and/or for querying the position description may also be generated, i.e., the first keyword set and/or the position description may be obtained by the position identifier.
The role of job identification is illustrated below, for example: when a driver of a certain company leaves a position, the server can acquire the position description of the company last time when the driver is recruited according to the position identification corresponding to the position of the driver, and can also acquire a first keyword set corresponding to the position of the driver by the company.
Here, the job table is used to store the correspondence between the job identifier and the keyword.
In some embodiments, the correspondence between the position identifier and the keyword may be recorded through a position table, such as: the keywords corresponding to the position A are as follows: keyword 1, keyword 2, keyword 3, and the keywords corresponding to position B are: the keywords 1, 4, and 5, that is, the position a and the position B, may include the same keyword, and thus, the position a and the position B may be characterized in a certain dimension, and the requirements for the user may be the same. That is, the position a and the position B may be two positions close to each other. Such as: position a is 'big data product manager' and position B is 'voice product manager', and position a and position B may both include the keyword 'manual position'. Therefore, the related positions of the target position can be determined through the position table, and the position identification set of the related positions is obtained.
In some embodiments, since the job description may only describe a part of the ability (or some skill) that the practitioner should have to engage in the job, if the target job condition is generated only according to the first keyword set extracted from the job description, the number of personal introduction information meeting the target job condition may be larger. Thus, the second set of keywords may be determined by obtaining relevant positions and then by analyzing commonalities present in the relevant positions. And the keywords in the second set of keywords may be keywords that are mostly included in these related positions. Such as: the relevant position of A position is B position, C position and D position, and B position, C position and D position all include the keyword has: keywords X and Y, while keywords included in both position C and position D are: the keyword M, in this case, the second keyword set may include: keyword X, key Y, and keyword M.
For ease of understanding, to further illustrate, the a position does not define marital status, computer capabilities, and english capabilities; however, the B, C, and D positions all define computer capabilities and English capabilities, and the C and D positions all define marital status (it may also be understood that keywords X and Y are keywords relating to computer capabilities and English capabilities, respectively, e.g., keyword X is 'computer third level', keyword Y is 'English sixth level', and keyword M may be 'not married'). It may be that the a-position also needs to define the computer competency, the english competency, and the marital status, and it is only the person of the human resources forgets to define the computer competency, the english competency, and the marital status during the process of inputting the position description corresponding to the a-position. Thus, the first keyword set corresponding to position a is caused to have no 'three computer stages', 'six english stages', and 'no marriage'.
And step 204, generating target position conditions according to the first keyword set and the second keyword set.
In some embodiments, according to the target position conditions generated by the first keyword set and the second keyword set, the limiting conditions in the target position conditions can be increased, so that the personal introduction information obtained by the final screening can better accord with the target position.
Referring to fig. 3, in some embodiments, the information processing method may further include steps 301 to 306, wherein details of the steps 301 to 304 are described in detail in steps 101 to 104, and are not described herein again for brevity of the description.
In some embodiments, after the personal introduction information does not conform to the target position, it may be further determined whether the personal introduction information conforms to a position associated with the target position. Such as: the target position is highly desirable in english, but some related position (first position) of the target position is similar in other aspects to the target position, but is less desirable in english. Therefore, it is possible to calculate the degree of matching between the first position and the personal introduction information, and then determine whether the personal introduction information corresponds to the first position.
Here, the first position identifier corresponding to the first position is an element in the position identifier set.
In some embodiments, each position has a corresponding stored list for storing personal introduction information corresponding to the position. If the first position is stored in correspondence with the personal introduction information, it can be understood that the personal information is stored in the storage list corresponding to the first position. That is, each personal introduction information to be stored has a corresponding position, and when the personal introduction information is viewed by the subsequent human resources, the position corresponding to the resume can be clearly known.
In some embodiments, the step 103 (determining the matching degree of the personal introduction information with the target position according to the at least one keyword and the target position condition of the target position) may specifically include: constructing a first portrait based on at least one key field; constructing a second portrait according to the first keyword set and the second keyword set; and performing overlapping comparison on the first person portrait and the second person portrait, and determining the matching degree of the personal introduction information and the target position as a first matching degree value.
In some embodiments, the larger the overlapping area between the first person representation and the second person representation, the greater the first matching value may be characterized, i.e., the greater the probability that the personal introduction information may be characterized as meeting the target position. When the overlapping area of the first portrait and the second portrait is smaller, the smaller the first matching degree value can be represented, that is, the smaller the probability that the personal introduction information can be represented to meet the target position.
In some embodiments, the step 103 (determining the matching degree of the personal introduction information with the target position according to the at least one keyword and the target position condition of the target position) may further include:
screening a third keyword set which meets the target position condition from the at least one keyword; and performing weighting processing on the keywords in the third keyword set, and determining the matching degree of the personal introduction information and the target position as a first matching degree value.
In some embodiments, the target position condition may not define some abilities (first ability) of the user, and some keywords of the at least one keyword are used for introducing the first ability of the user, so that keywords introducing the first ability (these keywords cannot represent the matching degree of the personal introduction information and the target position) need to be eliminated from the at least one keyword, and the rest keywords constitute elements of the third keyword set. That is, the keywords in the third keyword set may be used to characterize how well the personal introduction information matches the target position. While some keywords in the third set of keywords characterize the learning ability of the user, some keywords characterize the leadership ability of the user, and some keywords also characterize some other ability of the user. The weights of the target positions for the abilities may be different, that is, the target positions may first require that the user has a high leadership capability, and then require that the user has a certain learning capability and a certain academic calendar, at this time, different weights may be respectively configured for keywords representing the leadership capability, the learning capability and the academic calendar, so that the finally obtained first matching degree value is more accurate and reasonable. Of course, the weight of each keyword is not limited herein, and may be set according to actual conditions.
Referring to fig. 4, in some embodiments, after determining that the matching degree of the personal introduction information and the target position is the first matching degree value, the following steps may be performed.
Here, the second preset matching degree threshold is greater than the first preset matching degree threshold.
In some embodiments, the first preset matching degree threshold and the second preset matching degree threshold may be set according to actual situations, and specific values of the first preset matching degree threshold and the second preset matching degree threshold are not limited herein.
Referring to fig. 5, in some embodiments, after determining that the matching degree of the personal introduction information and the target position is the first matching degree value, step 501 to step 506 may be further performed, and details of implementation of step 501 and step 502 are already described in step 401 and step 402, and are not described herein again for brevity of the description.
In step 503, intention inquiry information for inquiring the user's intention to enter the job is generated.
In some embodiments, after the first matching degree value is greater than the second preset threshold, it may be characterized that the personal introduction information meets the target position, that is, it may be characterized that the user meets the requirement of the target position, and at this time, it is required to ask whether the user desires to join a certain company and perform a first role in the target position. The intention inquiry information may be understood as information for inquiring whether the user desires to enter the target position.
And step 504, responding to the acquired first information fed back by the user according to the intention inquiry information, and determining the job entry keywords in the first information for representing the user's job entry intention.
And step 505, determining a processing mode of the personal introduction information according to the entry keywords.
In some embodiments, when determining the entry keywords in the first information fed back by the user comprises: when the words with similar meanings such as 'don't consider ',' don't agree', the personal introduction information is not saved, and when the entry keywords in the first information include: if the words "may", "desire to enter the job", "consider next", etc., then step 506 may continue.
In some embodiments, when the first matching degree value is between a first preset matching degree threshold and a second preset matching degree threshold (the first matching degree value is greater than or equal to the first preset threshold, and the first matching degree value is less than or equal to the second preset threshold), the first keyword set and the second keyword set may be merged to obtain a fourth keyword set (i.e., a keyword set corresponding to the target position condition). Then, an intersection between the fourth set of keywords and the at least one keyword may be determined. And determining a fifth keyword set by taking the keywords except the intersection in the fourth keyword set as elements. And generating inquiry information according to the fifth keyword set. Here, the query information may be understood as a query for prompting the user to supplement the target keyword corresponding to the fifth keyword set. And determining whether the personal introduction information meets the target position according to the target keyword and the keyword in the second information.
In some embodiments, since the personal introduction information is acquired by going to the recruitment platform, the acquired information may not be complete information, and may only be partial information. Alternatively, the user may miss some information when writing personal introductory information that is exactly what the target job conditions require. Therefore, it may cause the finally obtained first matching degree value to be lower. At this time, it may be determined whether the personal introduction information is complete (that is, it is determined whether the fifth keyword set is an empty set), and if the personal introduction information is not complete, query information may be sent to the user so that the user supplements missing information (target keywords), and then it is determined whether the personal introduction information conforms to the target position according to the first information supplemented by the user. If the matching degree of the obtained personal introduction information and the target position is still smaller than the second preset threshold after the user is supplemented, the fact that the personal introduction information does not accord with the target position can be represented.
Referring to fig. 6, in some embodiments, before storing the personal introduction, it may be checked whether the personal introduction is previously stored, and if the personal introduction is previously stored, the personal introduction may not be stored. The personal introduction information acquired on the recruitment platform may not be complete personal introduction information, and the user may write multiple pieces of personal introduction information. Thus, there may be some differences in the content of the personal introduction information, but all for the same user, such as: in the first person introduction information, it is mentioned that '2013-2017 voice technical department in a factory is responsible for speaker products with screens and team management work', and in the stored person information, one person introduction information (second person introduction information) is mentioned that '2013-2017 company A is responsible for speaker products and team management work'. At this time, the first person introduction information and the second person introduction information may be for the same user, and thus, to avoid this as much as possible, steps 601 to 603 may be performed.
Here, the degree of matching between the person image composed of the sixth keyword set and the person image composed of at least one keyword is greater than a preset degree of matching.
In some embodiments, when the personal introduction information matches the target position, at least one keyword corresponding to the personal introduction information may be determined, and a plurality of keywords (a sixth keyword set) similar to the keywords in the at least one keyword may also be determined. Such as: the sound box product with the screen is similar to the sound box of company 'A'.
In some embodiments, the keyword corresponding to each stored personal introduction information may be compared with the sixth keyword set and at least one keyword, such as: if the keyword corresponding to a certain stored personal introduction information is found to be a subset of the combination of the sixth keyword set and at least one keyword, it may be characterized that the personal introduction information is not stored for the first time. Accordingly, if the keyword corresponding to any of the stored personal introduction information is not a subset of the combination of the sixth keyword set and the at least one keyword, step 602 may be executed.
With further reference to fig. 7, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of an information processing apparatus, which corresponds to the embodiment of the information processing method shown in fig. 1, and which is particularly applicable to various electronic devices.
As shown in fig. 7, the information processing apparatus of the present embodiment includes: an acquisition unit 701 configured to acquire personal introduction information of a user; an extracting unit 702, configured to extract at least one keyword from the personal introduction information, where the at least one keyword is used to characterize personal basic information of a user from at least one dimension; a first determining unit 703, configured to determine a matching degree between the personal introduction information and the target position according to the at least one keyword and a target position condition of the target position; a second determining unit 704, configured to determine whether the personal introduction information matches the target position according to the matching degree.
In some embodiments, the target job condition is generated by: determining a first keyword set for representing a user image and generating a position identifier from position description information, wherein the position description information is used for describing skills which should be possessed by a practitioner corresponding to the target position; determining a position identification set comprising keywords in the first keyword set from a pre-established position form, wherein the position form is used for storing the corresponding relation between the position identification and the keywords; determining a second keyword set according to the keywords corresponding to each position identifier in the position identifier set; and generating the target position condition according to the first keyword set and the second keyword set.
In some embodiments, the above apparatus further comprises: the second determining unit 704 is further specifically configured to: in response to determining that the personal introduction information does not conform to the target position, determining whether the personal introduction information conforms to a position corresponding to a position identifier in the position identifier set; and in response to determining that the personal introduction information conforms to the first position, correspondingly storing the first position and the personal introduction information, wherein the first position identifier corresponding to the first position is an element in the position identifier set.
In some embodiments, the first determining unit 703 is further specifically configured to: constructing a first portrait based on the at least one key field; constructing a second portrait according to the first keyword set and the second keyword set; and performing overlapping comparison on the first person portrait and the second person portrait, and determining the matching degree of the personal introduction information and the target position as a first matching degree value.
In some embodiments, the first determining unit 703 is further specifically configured to: screening out a third keyword set which meets the target position condition from the at least one keyword;
and performing weighting processing on the keywords in the third keyword set, and determining the matching degree of the personal introduction information and the target position as a first matching degree value.
In some embodiments, the second determining unit 704 is further specifically configured to: determining that the personal introduction information does not conform to the target position in response to determining that the first matching degree value is less than a first preset threshold; and in response to determining that the first matching degree value is greater than a second preset threshold, determining that the personal introduction information meets the target position, wherein the second preset threshold is greater than the first preset threshold.
In some embodiments, after determining that the personal introduction information matches the target position in response to determining that the first matching degree value is greater than a second preset threshold, the second determining unit 704 is further specifically configured to: generating intention inquiry information for inquiring the user's intention to enter a job; responding to first information fed back by the user according to the intention inquiry information, and determining an enrollment keyword used for representing the user's enrollment intention in the first information; and determining a processing mode of the personal introduction information according to the enrollment keywords.
In some embodiments, the second determining unit 704 is further specifically configured to: and correspondingly storing the personal introduction information and the target position in response to determining that the enrollment keywords represent the expected enrollment of the user.
In some embodiments, the above apparatus further comprises: a third determining unit 705, where the third determining unit 705 is specifically configured to: in response to determining that the first matching degree value is greater than or equal to the first preset threshold value and the first matching degree value is less than or equal to the second preset threshold value, combining the first keyword set and the second keyword set to obtain a fourth keyword set; determining an intersection between said fourth set of keywords and said at least one keyword; determining a fifth keyword set by taking the keywords except the intersection in the fourth keyword set as elements; generating inquiry information according to the fifth keyword set, wherein the inquiry information is used for prompting a user to supplement a target keyword corresponding to the fifth keyword set; responding to second information fed back by a user based on the fifth keyword set, and determining keywords in the second information; and determining whether the personal introduction information meets the target position according to the target keywords and the keywords in the second information.
In some embodiments, the above apparatus further comprises: a fourth determining unit 706, where the fourth determining unit 706 is specifically configured to: in response to determining that the personal introduction information conforms to the target position, determining a sixth keyword set according to a knowledge graph and a natural language understanding frame, wherein the matching degree of the character image formed by the sixth keyword set and the character image formed by the at least one keyword is greater than a preset matching degree; determining whether the personal introduction information is stored for the first time according to the sixth keyword set and the at least one keyword; and correspondingly storing the personal introduction information and the target position in response to the fact that the personal introduction information is determined to be stored for the first time.
Referring to fig. 8, fig. 8 illustrates an exemplary system architecture to which the information processing method of one embodiment of the present disclosure may be applied.
As shown in fig. 8, the system architecture may include terminal devices 801, 802, 803, a network 804, and a server 805. The network 804 may be the medium used to provide communications links between terminal devices 801, 802, 803 and the server 805. Network 804 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 801, 802, 803 may interact with a server 805 over a network 804 to receive or send messages or the like. The terminal devices 801, 802, 803 may have various client applications installed thereon, such as a web browser application, a search-type application, and a news-information-type application. The client application in the terminal devices 801, 802, 803 may receive the instruction of the user, and complete the corresponding function according to the instruction of the user, for example, add the corresponding information to the information according to the instruction of the user.
The terminal devices 801, 802, 803 may be hardware or software. When the terminal devices 801, 802, 803 are hardware, they may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like. When the terminal devices 801, 802, 803 are software, they can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
The server 805 may be a server providing various services, for example, receiving an information acquisition request sent by the terminal devices 801, 802, and 803, and acquiring presentation information corresponding to the information acquisition request in various ways according to the information acquisition request. And the relevant data of the presentation information is sent to the terminal devices 801, 802, 803.
It should be noted that the information processing method provided by the embodiment of the present disclosure may be executed by a terminal device, and accordingly, the information pushing apparatus may be disposed in the terminal devices 801, 802, and 803. Furthermore, the information processing method provided by the embodiment of the present disclosure may also be executed by the server 805, and accordingly, an information processing apparatus may be provided in the server 805.
It should be understood that the number of terminal devices, networks, and servers in fig. 8 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to fig. 9, shown is a schematic diagram of an electronic device (e.g., a terminal device or a server of fig. 8) suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal 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 vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 9 is only an example, and should not bring any limitation to the functions and the use range of the embodiment of the present disclosure.
As shown in fig. 9, the electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 901 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 902 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 603. In the RAM 903, various programs and data necessary for the operation of the electronic apparatus 900 are also stored. The processing apparatus 901, ROM902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
Generally, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 907 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 908 including, for example, magnetic tape, hard disk, etc.; and a communication device 909. The communication means 909 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 9 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 909, or installed from the storage device 908, or installed from the ROM 902. The computer program, when executed by the processing device 901, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination 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 present 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 contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications 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 network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled 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 personal introduction information of a user; extracting at least one keyword from the personal introduction information, wherein the at least one keyword is used for representing personal basic information of a user from at least one dimension; determining the matching degree of the personal introduction information and the target position according to the at least one keyword and the target position condition of the target position; and determining whether the personal introduction information meets the target position or not according to the matching degree.
Computer program code for carrying out operations for the present disclosure may be written in any combination of 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart 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 described in the embodiments of the present disclosure may be implemented by software or hardware. Here, the name of the unit does not constitute a limitation to the unit itself in some cases, and for example, the acquisition unit 701 may also be described as a "unit that acquires personal introduction information of the user".
The functions described herein above 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: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), 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. A 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 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 exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while 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. Under 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 limitations on the scope of the 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 disclosed as example forms of implementing the claims.
Claims (10)
1. An information processing method characterized by comprising:
acquiring personal introduction information of a user;
extracting at least one keyword from the personal introduction information, wherein the at least one keyword is used for representing personal basic information of a user from at least one dimension;
determining the matching degree of the personal introduction information and the target position according to the at least one keyword and the target position condition of the target position; wherein the target position condition is generated by: determining a first keyword set for representing a user image from position description information, and generating a position identifier, wherein the position description information is used for describing skills which a practitioner corresponding to the target position should have; determining a position identification set comprising keywords in the first keyword set from a pre-established position table, wherein the position table is used for storing the corresponding relation between position identifications and keywords; determining a second keyword set according to keywords corresponding to each position identifier in the position identifier set; generating the target position condition according to the first keyword set and the second keyword set;
determining whether the personal introduction information conforms to the target position or not according to the matching degree;
the determining the matching degree of the personal introduction information and the target position according to the at least one keyword and the target position condition of the target position comprises the following steps:
constructing a first person image according to the at least one keyword; constructing a second portrait according to the first keyword set and the second keyword set; performing overlapping comparison on the first person portrait and the second person portrait, and determining that the matching degree of the personal introduction information and the target position is a first matching degree value;
wherein, according to the matching degree, determining whether the personal introduction information conforms to the target position comprises:
in response to the fact that the first matching degree value is larger than or equal to a first preset threshold value and the first matching degree value is smaller than or equal to a second preset threshold value, combining the first keyword set and the second keyword set to obtain a fourth keyword set; determining an intersection between the fourth set of keywords and the at least one keyword; determining a fifth keyword set by taking the keywords except the intersection in the fourth keyword set as elements; generating inquiry information according to the fifth keyword set, wherein the inquiry information is used for prompting a user to supplement target keywords corresponding to the fifth keyword set; responding to second information fed back by a user based on the fifth keyword set, and determining keywords in the second information; and determining whether the personal introduction information conforms to the target position according to the target keywords and keywords in the second information.
2. The method of claim 1, further comprising:
in response to determining that the personal introduction information does not conform to the target position, determining whether the personal introduction information conforms to a position corresponding to a position identifier in the position identifier set;
in response to determining that the personal introduction information conforms to a first position, the first position and the personal introduction information are correspondingly stored, wherein a first position identifier corresponding to the first position is an element in the position identifier set.
3. The method of claim 1, wherein determining a matching degree of the personal introduction information with the target position according to the at least one key field and a target position condition of the target position further comprises:
screening out a third keyword set which meets the target position condition from the at least one keyword;
and performing weighting processing on the keywords in the third keyword set, and determining that the matching degree of the personal introduction information and the target position is a first matching degree value.
4. The method according to claim 1 or 3, wherein determining whether the personal introduction information corresponds to the target position according to the matching degree comprises:
in response to determining that the first matching degree value is less than a first preset threshold, determining that the personal introduction information does not conform to the target position;
in response to determining that the first matching degree value is greater than a second preset threshold, determining that the personal introduction information conforms to the target position, wherein the second preset threshold is greater than the first preset threshold.
5. The method of claim 4, wherein after determining that the personal introductory information corresponds to the target position in response to determining that the first match score is greater than a second preset threshold, the method further comprises:
generating intention inquiry information for inquiring the user's intention to enter a job;
responding to first information fed back by the user according to the intention inquiry information, and determining an enrollment keyword used for representing the intention of the user in the first information;
and determining a processing mode of the personal introduction information according to the enrollment keywords.
6. The method of claim 5, wherein determining the processing manner of the personal introduction information according to the entry keywords comprises:
in response to determining that the enrollment keywords characterize a desired enrollment of the user, storing the personal introduction information in correspondence with the target position.
7. The method of claim 1, further comprising:
in response to determining that the personal introduction information conforms to the target position, determining a sixth keyword set according to a knowledge graph and a natural language understanding frame, wherein the matching degree of the character image formed by the sixth keyword set and the character image formed by the at least one keyword is greater than a preset matching degree;
determining whether the personal introduction information is stored for the first time according to the sixth keyword set and the at least one keyword;
and responding to the fact that the personal introduction information is determined to be stored for the first time, and correspondingly storing the personal introduction information and the target position.
8. An information processing apparatus characterized by comprising:
an acquisition unit configured to acquire personal introduction information of a user;
the extraction unit is used for extracting at least one keyword from the personal introduction information, wherein the at least one keyword is used for representing personal basic information of the user from at least one dimension;
the first determining unit is used for determining the matching degree of the personal introduction information and the target position according to the at least one keyword and the target position condition of the target position; wherein the target position condition is generated by: determining a first keyword set for representing a user image from position description information, and generating position identification, wherein the position description information is used for describing skills which should be possessed by a practitioner corresponding to the target position; determining a position identification set comprising keywords in the first keyword set from a pre-established position table, wherein the position table is used for storing the corresponding relation between position identifications and keywords; determining a second keyword set according to keywords corresponding to each position identifier in the position identifier set; generating the target position condition according to the first keyword set and the second keyword set;
the second determining unit is used for determining whether the personal introduction information conforms to the target position according to the matching degree;
the first determining unit is further used for constructing a first person picture according to the at least one keyword; constructing a second portrait according to the first keyword set and the second keyword set; performing overlapping comparison on the first person portrait and the second person portrait, and determining that the matching degree of the personal introduction information and the target position is a first matching degree value;
the second determining unit is further configured to combine the first keyword set and the second keyword set to obtain a fourth keyword set in response to determining that the first matching degree value is greater than or equal to a first preset threshold and the first matching degree value is less than or equal to a second preset threshold; determining an intersection between the fourth set of keywords and the at least one keyword; determining a fifth keyword set by taking the keywords except the intersection in the fourth keyword set as elements; generating inquiry information according to the fifth keyword set, wherein the inquiry information is used for prompting a user to supplement target keywords corresponding to the fifth keyword set; responding to second information fed back by a user based on the fifth keyword set, and determining keywords in the second information; and determining whether the personal introduction information conforms to the target position according to the target keywords and keywords in the second information.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6662194B1 (en) * | 1999-07-31 | 2003-12-09 | Raymond Anthony Joao | Apparatus and method for providing recruitment information |
| CN103634420A (en) * | 2013-11-22 | 2014-03-12 | 北京极客优才科技有限公司 | Resume e-mail screening system and method |
| CN110019689A (en) * | 2019-04-17 | 2019-07-16 | 北京网聘咨询有限公司 | Position matching process and position matching system |
| CN110069603A (en) * | 2019-04-17 | 2019-07-30 | 北京军融英杰信息技术服务有限公司 | Resume immediate processing method, device and electronic equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2961157A1 (en) * | 2016-03-18 | 2017-09-18 | Mark Meier | Job posting, resume creation/management and applicant tracking system and method |
-
2020
- 2020-05-06 CN CN202010374977.5A patent/CN111597441B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6662194B1 (en) * | 1999-07-31 | 2003-12-09 | Raymond Anthony Joao | Apparatus and method for providing recruitment information |
| CN103634420A (en) * | 2013-11-22 | 2014-03-12 | 北京极客优才科技有限公司 | Resume e-mail screening system and method |
| CN110019689A (en) * | 2019-04-17 | 2019-07-16 | 北京网聘咨询有限公司 | Position matching process and position matching system |
| CN110069603A (en) * | 2019-04-17 | 2019-07-30 | 北京军融英杰信息技术服务有限公司 | Resume immediate processing method, device and electronic equipment |
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