CN112287215A - Intelligent employment recommendation method and device - Google Patents
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Abstract
The invention belongs to the field of recommendation systems, and provides an intelligent employment recommendation method and device. The intelligent employment recommendation method comprises the steps of obtaining personal information and post information; generating a candidate post recommendation list according to employment willingness in the personal information; according to the score relation between the posts and set factors, carrying out comprehensive scoring on the posts in the candidate post recommendation list, and filtering the posts lower than a set score threshold value from the candidate post recommendation list; and carrying out semantic association on the personal information and the post information in the candidate post recommendation list to generate a personal semantic vector characteristic and a post semantic vector characteristic, and reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic to generate the post recommendation list.
Description
Technical Field
The invention belongs to the field of recommendation systems, and particularly relates to an intelligent employment recommendation method and device.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid development of internet technology, a large amount of employment data is emerging continuously. The advent of employment recommendation systems can help people to obtain desired content more quickly and accurately. As one of the main methods in the recommendation field, a recommendation algorithm based on contents is widely applied to employment personalized recommendation, and the recommendation system generally recommends a post with higher similarity to a user by comparing the similarity of a post to be recommended and a post browsed by a job seeker.
The inventor finds that in the employment recommendation system, both the post and the individual contain short text descriptions containing more key information, and the existing content-based recommendation algorithm just converts the short text descriptions into vectors to simply calculate the similarity of the short text descriptions, so that the semantic association between the individual and the post is omitted. Meanwhile, for the people who just register, the content-based recommendation algorithm needs to traverse all the posts to be scored (or similar) and then recommend the posts with high scores (or high similarities), which wastes a large amount of computing resources and increases the expense cost.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the invention provides an intelligent employment recommendation method and device, which adopt a strategy from coarse to fine for recommendation, gradually narrow the recommendation range and improve the matching degree between an individual and a post through semantic association.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an intelligent employment recommendation method.
An intelligent employment recommendation method, comprising:
acquiring personal information and post information;
generating a candidate post recommendation list according to employment willingness in the personal information;
according to the score relation between the posts and set factors, carrying out comprehensive scoring on the posts in the candidate post recommendation list, and filtering the posts lower than a set score threshold value from the candidate post recommendation list;
and carrying out semantic association on the personal information and the post information in the candidate post recommendation list to generate a personal semantic vector characteristic and a post semantic vector characteristic, and reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic to generate the post recommendation list.
A second aspect of the invention provides an intelligent employment recommendation apparatus.
An intelligent employment recommendation device, comprising:
the data loading module is used for acquiring personal information and post information;
the work type module is used for generating a candidate post recommendation list according to employment willingness in the personal information;
the rule filtering module is used for comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and the set factors and filtering the posts lower than the set score threshold value from the candidate post recommendation list;
and the skill matching module is used for performing semantic association on the personal information and the post information in the candidate post recommendation list, generating a personal semantic vector characteristic and a post semantic vector characteristic, reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic, and generating the post recommendation list.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the intelligent employment recommendation method as set forth above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the intelligent employment recommendation method as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the employment willingness in personal information, a candidate post recommendation list is generated, according to the score relation between the posts and set factors, the posts in the candidate post recommendation list are comprehensively graded, and the posts lower than a set score threshold value are filtered from the candidate post recommendation list; the range of the recommended post is defined as much as possible, the expenditure of computing resources is reduced, a coarse recommendation strategy is adopted, the recommendation range is gradually reduced, and the matching degree of the post and the individual is improved.
(2) According to the method, semantic association is carried out on personal information and post information in a candidate post recommendation list to generate a personal semantic vector characteristic and a post semantic vector characteristic, the filtered candidate post recommendation list is reordered according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic to generate a post recommendation list, semantic association between an individual and a post is generated through a semantic association network, a potential interest model of the individual is mined, and accurate matching of the post and the individual is achieved.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of an intelligent employment recommendation concept in accordance with an embodiment of the present invention;
FIG. 2 is a semantic relationship network architecture of an embodiment of the invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Referring to fig. 1, the intelligent employment recommendation method of the present embodiment includes:
s101, data loading step: and acquiring personal information and position information.
In the specific implementation:
s1011: personal information is obtained from the resume. And screening the information from the position information.
S1012: the method comprises the steps of obtaining personal information, wherein the personal information comprises nine attributes of name, identification card number, age, sex, salary, academic calendar, working age, related skills and will position, preprocessing texts such as self evaluation and interests and hobbies, and obtaining personal keywords.
S1013: the method comprises the steps of obtaining post information, wherein the post information comprises eight attributes of a post number, a post name, a work seed name, a sex, salary, a study calendar, a working age and an age, and preprocessing text descriptions such as post description and skill requirements and obtaining post keywords.
S102, a work type recommendation step: and generating a candidate position recommendation list according to the employment willingness in the personal information.
In a specific implementation, the process of generating the candidate post recommendation list according to the employment willingness in the personal information comprises the following steps:
s1021: associating to a corresponding work type according to the personal wish post, and generating a wish work type recommendation list;
s1022: by matching the personal keywords with the post keywords, if the same keywords exist, the corresponding work types are associated, whether the work types are recommended or not is judged, if not, the work types are recommended, and an extended work type recommendation list is generated;
s1023: and summarizing the willingness work kind recommendation list and the expanded work kind recommendation list to generate a candidate post recommendation list.
For example: the personal will post is an electromechanical engineering professional post, the associated work types comprise electromechanical research and development engineers, electromechanical testers, electromechanical sales and the like, and a will work type recommendation list is generated;
the personal keywords comprise double break and the like, and the teacher can recommend the work category in an organic and electronic way, and if the teacher is not in the wish work category recommendation list, the teacher is stored in the extended work category recommendation list.
And finally, summarizing the wished work type recommendation list and the expanded work type recommendation list to obtain a candidate post recommendation list.
S103, rule filtering step: and according to the score relation between the posts and the set factors, performing comprehensive scoring on the posts in the candidate post recommendation list, and filtering the posts lower than the set score threshold value from the candidate post recommendation list.
Specifically, in the process of comprehensively scoring the positions in the candidate position recommendation list, the position has known value relations with factors such as gender, age, academic history, working age and salary:
s1031: scoring the gender attribute of the post to obtain a gender score;
s1032: scoring the age attribute of the post to obtain an age score;
s1033: scoring the academic attribute of the post to obtain an academic score;
s1034: scoring the working age attribute of the post to obtain a working age score;
s1035: scoring the salary attributes of the posts to obtain salary scores;
s1036: and accumulating the sex, the age, the academic calendar, the working age and the salary score to obtain the post comprehensive score.
S104 skill matching step: and carrying out semantic association on the personal information and the post information in the candidate post recommendation list to generate a personal semantic vector characteristic and a post semantic vector characteristic, and reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic to generate the post recommendation list.
In this embodiment, training and testing data is set, high-level semantic features of individuals and stations are generated, semantic similarity is calculated, and a recommendation list is generated.
Specifically, the method comprises the following steps:
s1041: converting the personal and post keywords into vectors, setting the willingness post and job hunting post as positive examples, setting the non-recommended, non-willingness and non-job hunting post as negative examples, and simultaneously dividing the training data and the testing data according to job hunter data;
s1042: the semantic association between the post and the person is enhanced, vector features with high semantic meaning are generated, and the semantic association is carried out on the personal information and the post information in the candidate post recommendation list through a semantic association network. Referring to fig. 2, the semantic association network includes a personal data network, a post data network and a shared network; the personal data network is used for projecting the personal vector characteristics to obtain low-semantic personal characteristics, the post data network is used for projecting the post vector characteristics to obtain low-semantic post characteristics, and the low-semantic personal and the post characteristics are projected to the shared network to generate corresponding high-semantic characteristics.
It should be noted that the personal data network, the post data network and the sharing network can be implemented by using the existing neural network, and will not be described in detail here.
S1043: and calculating the similarity by using the Euclidean distance, sequencing, and recommending the post with high similarity.
The method comprises the steps of generating a candidate post recommendation list according to employment willingness in personal information, comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and set factors, and filtering the posts lower than a set score threshold value from the candidate post recommendation list; the range of the recommended post is defined as much as possible, the expenditure of computing resources is reduced, a coarse recommendation strategy is adopted, the recommendation range is gradually reduced, and the matching degree of the post and the individual is improved.
According to the method and the device, semantic association is carried out on the personal information and the post information in the candidate post recommendation list, personal semantic vector features and post semantic vector features are generated, the filtered candidate post recommendation list is reordered according to the similarity between the personal semantic vector features and the post semantic vector features, the post recommendation list is generated, semantic association between individuals and posts is generated through a semantic association network, potential interest models of the individuals are mined, and accurate matching between the posts and the individuals is achieved.
Example two
This embodiment provides an intelligence employment recommendation device, it includes:
the data loading module is used for acquiring personal information and post information;
the work type module is used for generating a candidate post recommendation list according to employment willingness in the personal information;
the rule filtering module is used for comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and the set factors and filtering the posts lower than the set score threshold value from the candidate post recommendation list;
and the skill matching module is used for performing semantic association on the personal information and the post information in the candidate post recommendation list, generating a personal semantic vector characteristic and a post semantic vector characteristic, reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic, and generating the post recommendation list.
Each module in the intelligent employment recommendation device of this embodiment corresponds to each step in the intelligent employment recommendation method of the first embodiment one by one, and the specific implementation process thereof will not be described here again.
The method comprises the steps of generating a candidate post recommendation list according to employment willingness in personal information, comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and set factors, and filtering the posts lower than a set score threshold value from the candidate post recommendation list; the range of the recommended post is defined as much as possible, the expenditure of computing resources is reduced, a coarse recommendation strategy is adopted, the recommendation range is gradually reduced, and the matching degree of the post and the individual is improved.
According to the method and the device, semantic association is carried out on the personal information and the post information in the candidate post recommendation list, personal semantic vector features and post semantic vector features are generated, the filtered candidate post recommendation list is reordered according to the similarity between the personal semantic vector features and the post semantic vector features, the post recommendation list is generated, semantic association between individuals and posts is generated through a semantic association network, potential interest models of the individuals are mined, and accurate matching between the posts and the individuals is achieved.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps in the intelligent employment recommendation method as described in the first embodiment above.
The method comprises the steps of generating a candidate post recommendation list according to employment willingness in personal information, comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and set factors, and filtering the posts lower than a set score threshold value from the candidate post recommendation list; the range of the recommended post is defined as much as possible, the expenditure of computing resources is reduced, a coarse recommendation strategy is adopted, the recommendation range is gradually reduced, and the matching degree of the post and the individual is improved.
According to the method and the device, semantic association is carried out on the personal information and the post information in the candidate post recommendation list, personal semantic vector features and post semantic vector features are generated, the filtered candidate post recommendation list is reordered according to the similarity between the personal semantic vector features and the post semantic vector features, the post recommendation list is generated, semantic association between individuals and posts is generated through a semantic association network, potential interest models of the individuals are mined, and accurate matching between the posts and the individuals is achieved.
Example four
The embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the intelligent employment recommendation method according to the first embodiment.
The method comprises the steps of generating a candidate post recommendation list according to employment willingness in personal information, comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and set factors, and filtering the posts lower than a set score threshold value from the candidate post recommendation list; the range of the recommended post is defined as much as possible, the expenditure of computing resources is reduced, a coarse recommendation strategy is adopted, the recommendation range is gradually reduced, and the matching degree of the post and the individual is improved.
According to the method and the device, semantic association is carried out on the personal information and the post information in the candidate post recommendation list, personal semantic vector features and post semantic vector features are generated, the filtered candidate post recommendation list is reordered according to the similarity between the personal semantic vector features and the post semantic vector features, the post recommendation list is generated, semantic association between individuals and posts is generated through a semantic association network, potential interest models of the individuals are mined, and accurate matching between the posts and the individuals is achieved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent employment recommendation method, comprising:
acquiring personal information and post information;
generating a candidate post recommendation list according to employment willingness in the personal information;
according to the score relation between the posts and set factors, carrying out comprehensive scoring on the posts in the candidate post recommendation list, and filtering the posts lower than a set score threshold value from the candidate post recommendation list;
and carrying out semantic association on the personal information and the post information in the candidate post recommendation list to generate a personal semantic vector characteristic and a post semantic vector characteristic, and reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic to generate the post recommendation list.
2. The intelligent employment recommendation method of claim 1, wherein the personal information includes name, identification number, age, gender, salary, academic calendar, working age, related skills and will position.
3. The intelligent employment recommendation method of claim 1, wherein the post information comprises a post number, a post name, a work seed name, a gender, a salary, a school calendar, a working age, and an age.
4. The intelligent employment recommendation method according to claim 1, characterized in that the process of generating the candidate position recommendation list according to the employment willingness in the personal information comprises:
associating to a corresponding work type according to the personal wish post, and generating a wish work type recommendation list;
by matching the personal keywords with the post keywords, if the same keywords exist, the corresponding work types are associated, whether the work types are recommended or not is judged, if not, the work types are recommended, and an extended work type recommendation list is generated;
and summarizing the willingness work kind recommendation list and the expanded work kind recommendation list to generate a candidate post recommendation list.
5. The intelligent employment recommendation method of claim 1, wherein in the process of comprehensively scoring the posts in the candidate post recommendation list, the respective score relationships between the posts and the factors of gender, age, academic history, working years and salary are known, so as to obtain corresponding gender, age, academic history, working years and salary scores, and the gender, age, academic history, working years and salary scores are accumulated to obtain the post comprehensive score.
6. The intelligent employment recommendation method of claim 1 wherein the personal information and the post information in the candidate post recommendation list are semantically correlated via a semantic association network.
7. The intelligent employment recommendation method of claim 6, wherein the semantic association network comprises a personal data network, a post data network and a shared network; the personal data network is used for projecting the personal vector characteristics to obtain low-semantic personal characteristics, the post data network is used for projecting the post vector characteristics to obtain low-semantic post characteristics, and the low-semantic personal and the post characteristics are projected to the shared network to generate corresponding high-semantic characteristics.
8. An intelligent employment recommendation device, comprising:
the data loading module is used for acquiring personal information and post information;
the work type module is used for generating a candidate post recommendation list according to employment willingness in the personal information;
the rule filtering module is used for comprehensively scoring the posts in the candidate post recommendation list according to the score relation between the posts and the set factors and filtering the posts lower than the set score threshold value from the candidate post recommendation list;
and the skill matching module is used for performing semantic association on the personal information and the post information in the candidate post recommendation list, generating a personal semantic vector characteristic and a post semantic vector characteristic, reordering the filtered candidate post recommendation list according to the similarity of the personal semantic vector characteristic and the post semantic vector characteristic, and generating the post recommendation list.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the intelligent employment recommendation method according to any one of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps in the intelligent employment recommendation method of any one of claims 1-7.
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Application publication date: 20210129 |