CN110727852A - Method, device and terminal for pushing recruitment recommendation service - Google Patents
Method, device and terminal for pushing recruitment recommendation service Download PDFInfo
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Abstract
The invention is applicable to the technical field of data processing, and provides a method, a device and a terminal for pushing recruitment recommendation service, which construct a knowledge map according to enterprise information and application member information, calculate the similarity between enterprises and the similarity between recruitment members according to attribute information of enterprise entities and recruitment member entities contained in the knowledge map so as to classify the enterprises and the recruitment members, score the same type of enterprises or the same type of application members, generate corresponding recruitment recommendation service after weighting and sorting scoring results and push the corresponding recruitment recommendation service to the same type of enterprises or the same type of application members, realize bidirectional recommendation and improve the recommendation accuracy by recommending the same type of application members to the same type of enterprises and pushing the same type of application members to the same type of enterprises, so that the enterprises or the application members can quickly and accurately find the application members or enterprises meeting requirements, the recruitment efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a method, a device and a terminal for pushing recruitment recommendation service.
Background
With the continuous development of big data and artificial intelligence technology, the application of artificial intelligence technology has begun to spread to various fields. The traditional recruitment system uses manual screening, consumes a large amount of manpower and material resources, has low recruitment efficiency and can not accurately match enterprises or applicants meeting the recruitment requirement.
The job position recommendation is performed on enterprises and application personnel by using a recommendation algorithm in the existing part of recruitment websites, the recommendation is mainly performed according to key information such as vocational skills, work experience and the like of the application personnel, and the accurate recommendation is difficult to be performed on the enterprises with diversified requirements, or the enterprise recommendation which meets the job hunting requirements of the application personnel is provided for the application personnel quickly and accurately.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, and a terminal for pushing a recruitment recommendation service, so as to solve the problems that a large amount of screening and establishment are required in a current recruitment system, the recruitment efficiency is low, the recommendation manner is single, and the recommendation accuracy is low.
The method comprises the steps of constructing a knowledge map according to enterprise information and application member information, calculating the similarity between enterprises and the similarity between application members according to attribute information of enterprise entities and application member entities contained in the knowledge map to classify the enterprises and the application members, scoring the same type of enterprises or the same type of application members, generating corresponding recruitment recommendation service after weighting and sorting scoring results, pushing the corresponding recruitment recommendation service to the same type of enterprises or the same type of application members, recommending the same type of application members to the same type of enterprises and pushing the same type of application members to the same type of enterprises, realizing bidirectional recommendation and improving recommendation accuracy, enabling the enterprises or the application members to quickly and accurately find the application members or the enterprises meeting the requirements of the enterprises, and improving recruitment efficiency.
A first aspect of an embodiment of the present invention provides a method for pushing a recruitment recommendation service, including:
constructing a knowledge graph according to the enterprise information and the information of the applicant, wherein the knowledge graph comprises a plurality of nodes of enterprise entities and the entities of the applicant and attribute information of the entities;
according to the attribute information, calculating the similarity between enterprises and the similarity between engaging members so as to classify the enterprises and the engaging members in the knowledge graph;
scoring is carried out on the same type of enterprises or the same type of application personnel according to preset indexes, the scoring results are weighted and sorted, and corresponding recruitment recommendation service is generated;
and correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
A second aspect of the embodiments of the present invention provides a device for pushing a recruitment recommendation service, including:
the system comprises a knowledge map construction unit, a data processing unit and a data processing unit, wherein the knowledge map construction unit is used for constructing a knowledge map according to enterprise information and engaging person information, and the knowledge map comprises a plurality of nodes of enterprise entities and engaging person entities and attribute information of the entities;
the classification calculating unit is used for calculating the similarity between enterprises and the similarity between engaging members according to the attribute information so as to classify the enterprises and the engaging members in the knowledge graph;
the recruitment recommendation service generation unit is used for scoring the same type of enterprises or the same type of application personnel according to preset indexes, performing weighted sequencing on scoring results and generating corresponding recruitment recommendation service;
and the recruitment recommendation service pushing unit is used for correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
A third aspect of an embodiment of the present invention provides a terminal, including:
the recruitment recommendation system comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for pushing a recruitment recommendation service provided by the first aspect of the embodiment of the invention when executing the computer program.
Wherein the computer program comprises:
the system comprises a knowledge map construction unit, a data processing unit and a data processing unit, wherein the knowledge map construction unit is used for constructing a knowledge map according to enterprise information and engaging person information, and the knowledge map comprises a plurality of nodes of enterprise entities and engaging person entities and attribute information of the entities;
the classification calculating unit is used for calculating the similarity between enterprises and the similarity between engaging members according to the attribute information so as to classify the enterprises and the engaging members in the knowledge graph;
the recruitment recommendation service generation unit is used for scoring the same type of enterprises or the same type of application personnel according to preset indexes, performing weighted sequencing on scoring results and generating corresponding recruitment recommendation service;
and the recruitment recommendation service pushing unit is used for correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program, where the computer program, when executed by a processor, implements the steps of the method of pushing a recruitment recommendation service provided by the first aspect of the embodiments of the present invention.
Wherein the computer program comprises:
the system comprises a knowledge map construction unit, a data processing unit and a data processing unit, wherein the knowledge map construction unit is used for constructing a knowledge map according to enterprise information and engaging person information, and the knowledge map comprises a plurality of nodes of enterprise entities and engaging person entities and attribute information of the entities;
the classification calculating unit is used for calculating the similarity between enterprises and the similarity between engaging members according to the attribute information so as to classify the enterprises and the engaging members in the knowledge graph;
the recruitment recommendation service generation unit is used for scoring the same type of enterprises or the same type of application personnel according to preset indexes, performing weighted sequencing on scoring results and generating corresponding recruitment recommendation service;
and the recruitment recommendation service pushing unit is used for correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the method comprises the steps of constructing a knowledge map according to enterprise information and application member information, calculating the similarity between enterprises and the similarity between application members according to attribute information of enterprise entities and application member entities contained in the knowledge map to classify the enterprises and the application members, scoring the same type of enterprises or the same type of application members, generating corresponding recruitment recommendation service after weighting and sorting scoring results, pushing the corresponding recruitment recommendation service to the same type of enterprises or the same type of application members, recommending the same type of application members to the same type of enterprises and pushing the same type of application members to the same type of enterprises, realizing bidirectional recommendation and improving recommendation accuracy, enabling the enterprises or the application members to quickly and accurately find the application members or the enterprises meeting the requirements of the enterprises, and improving recruitment efficiency.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of an implementation of a method for pushing a recruitment recommendation service according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a constructed knowledge graph according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a specific implementation of a method for constructing a knowledge graph according to enterprise information and engaging member information according to an embodiment of the present invention;
fig. 4 is a flowchart of an implementation of a method for calculating similarity between enterprises and similarity between engaging members according to an embodiment of the present invention;
fig. 5 is a flowchart of a specific implementation of a method for scoring the same type of enterprise or the same type of applicant according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an apparatus for pushing a recruitment recommendation service according to an embodiment of the invention;
fig. 7 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples. Referring to fig. 1, fig. 1 shows an implementation flow of a method for pushing a recruitment recommendation service according to an embodiment of the present invention, which is detailed as follows:
in step S101, a knowledge graph is constructed according to the enterprise information and the hiring person information, where the knowledge graph includes a plurality of nodes of the enterprise entities and the hiring person entities and attribute information of the entities.
In the embodiment of the invention, the enterprise information includes, but is not limited to, basic enterprise information, department information and position information. The basic information of the enterprise includes but is not limited to the information of enterprise profile, the industry of the enterprise, the department set by the enterprise, the enterprise relationship such as enterprise cooperation or subordinate unit, etc. Department information includes, but is not limited to, department profiles, department posts, etc. The post information includes, but is not limited to, a post description, salary treatment, etc. The applicant information includes, but is not limited to, information such as resume of applicant, basic information, skill information, post information, and attribute information between persons.
Here, the enterprise and the hiring personnel are used as entity nodes in the knowledge graph, that is, the name of the enterprise and the name of the hiring personnel are used as entity node data in the knowledge graph. It is to be understood that department names and post names are also entity node data in the knowledge-graph as entity nodes in the knowledge-graph. The enterprise name, the applicant name, the department name and the post name are all entities in the knowledge graph, and the constructed knowledge graph further comprises attribute information of each entity. That is, the knowledge-graph includes nodes of a plurality of business entities and engaging person entities and attribute information of the entities.
Here, the attribute information of the entity is specifically characteristic data of the entity; each entity can contain a plurality of characteristic data, and each characteristic data contains descriptive information which has an association relationship with the entity, such as department information, position information, salary treatment information of related positions and the like contained in the enterprise entity, identity information, skill information and relationship information of personnel among persons and the like of the applicant entity.
In the embodiment of the invention, an enterprise information database, a resume information database, enterprise data of entity names, relationship names and entity attribute information in enterprise Description information captured from the internet and data of engaging personnel are extracted in the form of Resource Description Framework (RDF) to form knowledge data of enterprises and engaging personnel, and a knowledge map is constructed together. According to the structure of the RDF triple, the entity names, the relationship names and the attribute information of the entities in the enterprise description information, the personnel resume information database, the enterprise description information and the application personnel information captured from the Internet are extracted, so that a knowledge map is constructed according to the structured RDF triple data. Specifically, the constructed knowledge graph is composed of a plurality of metadata. The metadata is organized in terms of the structure of RDF triples. Specifically, RDF describes metadata of data by triples of subjects, predicates, and objects. For example, "king" belongs to "enterprise a," king "is the subject, while" enterprise a "is an object, and" belonging "is a predicate. Both objects and predicates are attributes of the subject. By filling the attributes of the body with corresponding values, data about the body, i.e. metadata, is described. Where one such triple describes a property with respect to a subject, multiple such triples may describe multiple attributes.
Here, a triplet may be represented by a graph: a node representing the subject, a node representing the object, and an arc of representation predicates directed by the subject to the object. It will be appreciated that a knowledge graph is made up of a graph represented by a plurality of triplets. For example, embodiments of the present invention provide a constructed knowledge-graph as shown in FIG. 2.
Preferably, the embodiment of the present invention provides specific implementation steps of the method for constructing the knowledge graph according to the enterprise information and the hire information, as shown in fig. 3, and the detailed steps are as follows:
in step S301, according to the structure of the RDF triple, the entity name, the relationship name, and the entity attribute information in the enterprise information database, the resume information database, and the enterprise description information captured from the internet are extracted.
In the embodiment of the invention, the extraction of the entities, the relations and the entity attributes mainly depends on the collection of data composed of mixed information of massive enterprise information databases, resume information data of staff, enterprise descriptions earned from the Internet and the like. And arranging the collected data into RDF triples so as to form a knowledge graph. Here, the mixed information includes enterprise information and engaging person information.
In step S302, the extracted entity name, relationship name and entity attribute information are subjected to knowledge fusion to obtain structured RDF triple data.
In the embodiment of the invention, the purpose of acquiring information such as entities, relationships, entity attributes and the like from unstructured and semi-structured data is realized through knowledge extraction. However, due to the problems of duplication, hierarchy loss and the like of information extracted from massive enterprise information databases, staff resume information databases and enterprise description information captured from the internet, further knowledge fusion is required to obtain structured RDF triple data.
Specifically, knowledge fusion includes entity alignment and attribute correction. Entity alignment is mainly to screen entities with the same name to determine the uniqueness of the entities. The attribute correction is mainly to correct the relationship, attribute and attribute value of the entity to obtain the complete and correct entity relationship and entity attribute. Here, the method used for knowledge fusion is a probability-based method.
In step S303, a knowledge graph is constructed based on the structured RDF triple data.
In the embodiment of the invention, after the structured RDF triple data are obtained, the entity names, the relationship names, the attribute information and the like in the RDF triple data are represented by a graph based on the entity nodes, so as to construct a knowledge graph containing enterprise information and engaging person information.
In the embodiment of the invention, the attribute information of different entities is characterized by the RDF triple data in the knowledge graph, for example, the attribute information mainly characterized by the applicant comprises the following components:
1) basic information of the recruiter, describing name, gender, age, academic calendar, place of household registration, marriage and so on;
2) the resume mainly comprises a resume storage address;
3) a skill label, which mainly describes personnel skill information;
4) other information describing work experience, project experience, hobbies, and the like;
5) relationships among people, describing classmates, relatives, co-workers, etc.;
6) and the post information belongs to which post.
The attribute information mainly described for the business entity is as follows:
1) department of department
2) Enterprise information
3) And the enterprise relation describes the relationship among enterprises such as subordinate enterprises, cooperative enterprises and the like.
The attribute information mainly described for the department entity is as follows:
1) a post;
2) department information.
The attribute information mainly characterized by the post entity is as follows:
1) describing the positions;
2) and (5) dealing with salaries.
In step S102, according to the attribute information, calculating a similarity between enterprises and a similarity between engaging members so as to classify the enterprises and the engaging members in the knowledge graph.
In the embodiment of the invention, the similarity between enterprises and the similarity between engaging persons are calculated through the Pearson correlation coefficient according to the attribute information of the entities in the knowledge graph. After calculating the similarity between enterprises, dividing the enterprises of which the similarity reaches a first preset threshold into the same type of enterprises; and after the similarity between the application personnel is calculated, the application personnel with the similarity reaching a second preset threshold value are classified into the same application personnel.
In an application scenario, the enterprises X, Y, Z belong to the same industry and are all provided with a post of an AI department and an AI engineer, but the enterprise scale, salary treatment, the number of the departments and the evaluation of the personnel who apply are different, when the similarity between the enterprises is calculated by using the Pearson correlation coefficient, the similarity of the enterprise X, Y, Z reaches a preset threshold value, and the enterprises X, Y, Z are divided into the same type of enterprises.
In another application scenario, the enterprises X, Y, Z are all provided with a position of an AI department and an AI engineer, the scale and salary treatment of the enterprises are almost different, but the enterprises belong to different industries, and when the similarity between the enterprises is calculated by using the pearson correlation coefficient, the similarity of the enterprise X, Y, Z fails to reach a preset threshold, and the enterprise X, Y, Z does not belong to the same class of enterprises.
In the embodiment of the present invention, specific implementation steps of the method for calculating the similarity between enterprises and the similarity between engaging members are provided as shown in fig. 4, which are detailed as follows:
in step S401, according to the attribute information, calculating similarity between the application members based on the first scoring results of different application members for different enterprises, and classifying the application members with the similarity reaching a preset threshold as the same application member.
In the embodiment of the invention, the first scoring result comprises attitudes and preference degrees of different employing personnel on the same enterprise, and specifically, different attributes of the employing personnel on the enterprise, such as the selection degrees of post conditions, salary treatment, unit information and the like, are searched through historical behavior data of the employing personnel, and a final scoring result is obtained after comprehensive scoring is performed according to the attributes, and the similarity between the employing personnel is calculated through a Pearson correlation coefficient according to the final scoring result.
For example, all of the applicants A, B had been engaged in business X, Y, Z in the same industry, and all of the X, Y, Z business pairs were better rated by A, B, and business X was engaged in and business X was better rated by C, then A, B, C could be considered to belong to the same category of applicants. An enterprise Z that was already engaged in position can also be recommended to the applicant C at A, B, a schematic of which is shown in fig. 3.
In step S402, according to the attribute information, calculating similarity between enterprises based on second scoring results of corresponding engaging members of different enterprises, and classifying the enterprises whose similarity reaches a preset threshold value as the same type of enterprise.
In step S103, scoring is performed on the same type of enterprises or the same type of employees according to preset indexes, and the scoring results are weighted and sorted to generate corresponding recruitment recommendation services.
In the embodiment of the invention, the preset index is an index set according to attribute information of an enterprise or a suitable personnel, and the attribute information comprises but is not limited to industry information, department information, post information, salary treatment, skill information, time of employment, personnel and other attribute information. The enterprise recruitment recommendation service is pushed to the same type of application personnel, the problem that the application personnel need to select lots of company posts is solved, the selection efficiency and selection accuracy of the application personnel are improved, and better experience is provided for users.
In an application scenario, the enterprises X, Y, Z are the same type of enterprises and are all provided with AI parts, the personnel applying A, B all work in the AI part of the enterprise X, Y, Z, the personnel applying A, B has better evaluation on the enterprise X, Y, Z, the enterprises X, Y, Z are scored after other indexes such as salary treatment, migration space and the like are integrated, and the enterprises are ranked according to the scores after weighted ranking, wherein Y is the highest, and X is the lowest. And the applicant C has been in position in the AI department of the enterprise X, then enterprise Y and Z can be recommended to the applicant C, and the priority of the recommended enterprise Y is higher than that of the enterprise Z.
The preset indexes for scoring the application members classified into the same type comprise indexes for evaluating the application members by enterprises, such as talent evaluation, talent skill conformity degree, application condition conformity degree and the like, according to which different application members classified into the same type are comprehensively scored, and the recruitment recommendation service containing the recommendation information of the application members of the same type is obtained after weighted sorting, and is pushed to the same type of enterprises, so that the application members meeting the recruitment requirement are accurately provided for the enterprises, the problem of recruitment of the enterprises is solved, and the enterprise recruitment efficiency is improved.
In one application scenario, the applicants A, B, C all engaged in the same industry, took the same job and had the same skills, but the evaluations of the corresponding applicants A, B, C by the enterprise X, Y were not the same, and the work experience and project experience of the applicants A, B, C were also not the same, and the scoring of the corresponding applicants A, B, C was performed by integrating other indicators, such as the essential information of the applicants (name, gender, age, academic calendar, etc.), for an enterprise Z that is the same category as the enterprise X, Y and has the same talent demand, the applicants A, B, C can be recommended to the enterprise Z, and the corresponding applicants A, B, C are weighted and ranked according to the other indicators of the talent demand of the enterprise Z, so that the applicants with the highest degree of compliance are preferentially pushed to the enterprise Z.
Preferably, the step of scoring the same type of enterprises or the same type of applicants according to the preset indexes specifically includes:
and scoring the same type of enterprises or the same type of application personnel according to a preset scoring function and a preset index.
Preferably, the embodiment of the present invention provides specific implementation steps of the method for scoring the same type of enterprise or the same type of applicant as shown in fig. 5, which are detailed as follows:
in step S501, the preset index is set as a function variable, and a value of the function variable is normalized.
In the embodiment of the invention, preset indexes corresponding to enterprises, such as enterprise information, industry information, department information, salary treatment information, personnel information, evaluation information of personnel on the enterprises and the like, or preset indexes corresponding to applicants, such as essential information of applicants, time-of-employment information, personnel relationship information, evaluation information of the enterprises to the applicants and the like, are set as the function variable x1,x2,x3,...,xn。
Here, each numerical function variable is normalized, and for non-numerical function variables, specific variable values may be designed using a piecewise function, for example, a personal relationship term, where a family is 1 in the company, a classmate/friend is 0.8 in the company, a known person is 0.5 in the company, and an unknown person is 0.3 in the company may be set.
In step S502, a weight corresponding to the preset index is obtained.
In the embodiment of the invention, due to different requirements of enterprises and engaging personnel, the importance of indexes of the enterprises and the engaging personnel needs to be considered and is also different, so that a certain weight needs to be set to obtain the enterprises or the engaging personnel which meet the requirements, and each function variable corresponds to a certain weight w1,w2,w3,...,wn。
In step S503, according to a preset scoring function, scoring the normalized function variable value and the weight corresponding to the preset index to obtain a scoring result of the same type of enterprise or the same type of candidate.
In the embodiment of the present invention, the preset scoring function specifically includes:
M＝w1x1+w2x2+w3x3+...+wnxn；
wherein, M is a scoring result, w is a numerical value of the normalized function variable, and x is a weight corresponding to a preset index, that is, a weight corresponding to the function variable.
In step S104, the recruitment recommendation service is correspondingly pushed to the same type of enterprises or the same type of applicants.
In an embodiment of the invention, the recruitment recommendation service comprises a first recruitment recommendation service and a second recruitment recommendation service. The first recruitment recommendation service is specifically a recruitment recommendation service containing the recommendation information of the same type of application personnel; the second recruitment recommendation service is specifically a recruitment recommendation service containing recommendation information of the same type of enterprises. It can be understood that the recruitment recommendation service pushed to the same type of enterprises is a first recruitment recommendation service, and the recruitment recommendation service pushed to the same type of applicants is a second recruitment recommendation service.
In the embodiment of the invention, a knowledge map is constructed according to enterprise information and information of the application personnel, after the similarity between enterprises and the similarity between the application personnel are calculated according to attribute information of the enterprise entity and the application personnel entity contained in the knowledge map so as to classify the enterprises and the application personnel, the same type of enterprises or the same type of application personnel are scored, and after the scoring results are weighted and sorted, corresponding recruitment recommendation service is generated and pushed to the same type of enterprises or the same type of application personnel.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Corresponding to the method for pushing the recruitment recommendation service in the above embodiments, fig. 5 is a schematic diagram of a device for pushing the recruitment recommendation service according to an embodiment of the invention, and for convenience of explanation, only the parts related to the embodiment of the invention are shown.
Referring to fig. 6, the apparatus includes:
the knowledge map construction unit 61 is used for constructing a knowledge map according to the enterprise information and the recruitment member information, wherein the knowledge map comprises a plurality of nodes of enterprise entities and recruitment member entities and attribute information of the entities;
the classification calculating unit 62 is configured to calculate similarities between enterprises and between engaging members according to the attribute information, so as to classify the enterprises and the engaging members in the knowledge graph;
the recruitment recommendation service generation unit 63 is configured to score the same type of enterprises or the same type of employees according to preset indexes, perform weighted sorting on scoring results, and generate corresponding recruitment recommendation service;
and the recruitment recommendation service pushing unit 64 is used for correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
Specifically, the knowledge graph constructing unit 61 includes:
the knowledge extraction subunit is used for extracting entity names, relationship names and entity attribute information in an enterprise information database, a staff resume information database and enterprise description information captured from the Internet according to the structure of the RDF triple;
the knowledge fusion subunit is used for performing knowledge fusion on the extracted entity name, the relationship name and the entity attribute information to obtain structured RDF ternary group data;
and the knowledge graph constructing subunit is used for constructing a knowledge graph based on the structured RDF triple data.
Specifically, the classification calculating unit 62 is specifically configured to:
and according to the attribute information, calculating the similarity between enterprises and the similarity between engaging persons through a Pearson correlation coefficient so as to classify the enterprises and the engaging persons in the knowledge graph.
Specifically, the classification calculation unit 62 includes:
the first classification calculating subunit is used for calculating the similarity between the application personnel based on the first scoring results of different application personnel on different enterprises according to the attribute information and classifying the application personnel with the similarity reaching a preset threshold value as the same application personnel;
and the second classification calculating subunit is used for calculating the similarity between the enterprises based on the second grading results of the corresponding engaging persons of different enterprises according to the attribute information and classifying the enterprises with the similarity reaching a preset threshold value into the same enterprise.
Preferably, the preset index is an index set according to attribute information of an enterprise or an applicable member.
Preferably, the recruitment recommendation service generating unit 63 is further configured to:
and scoring the same type of enterprises or the same type of application personnel according to a preset scoring function and a preset index.
Preferably, the recruitment recommendation service generating unit 63 further includes:
the numerical value normalization processing subunit is used for setting the preset index as a function variable and normalizing the numerical value of the function variable;
the weight obtaining subunit is configured to obtain a weight corresponding to the preset index;
and the scoring calculation subunit is used for carrying out scoring calculation on the numerical value of the function variable subjected to the normalization processing and the weight corresponding to the preset index according to a preset scoring function so as to obtain a scoring result of the same type of enterprises or the same type of employees.
Specifically, the recruitment recommendation service comprises a first recruitment recommendation service and a second recruitment recommendation service; the first recruitment recommendation service is specifically a recruitment recommendation service containing recommendation information of the same type of application personnel; the second recruitment recommendation service is specifically a recruitment recommendation service containing the recommendation information of the same type of enterprises; the recruitment recommendation service pushing unit 64 includes:
the first recruitment recommendation service pushing subunit is used for pushing the first recruitment recommendation service to the same type of enterprises;
and the first recruitment recommendation service pushing subunit is used for pushing the second recruitment recommendation service to the same type of employing members.
In the embodiment of the invention, a knowledge map is constructed according to enterprise information and information of the application personnel, after the similarity between enterprises and the similarity between the application personnel are calculated according to attribute information of the enterprise entity and the application personnel entity contained in the knowledge map so as to classify the enterprises and the application personnel, the same type of enterprises or the same type of application personnel are scored, and after the scoring results are weighted and sorted, corresponding recruitment recommendation service is generated and pushed to the same type of enterprises or the same type of application personnel.
Fig. 7 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 7, the terminal 7 of this embodiment includes: a processor 70, a memory 71 and a computer program 72 stored in said memory 71 and executable on said processor 70. The processor 70, when executing the computer program 72, implements the steps in the various above-described method embodiments of the push recruitment recommendation service, such as the steps 101-104 shown in fig. 1. Alternatively, the processor 70, when executing the computer program 72, implements the functions of the units in the system embodiments, such as the functions of the modules 61 to 63 shown in fig. 6.
Illustratively, the computer program 72 may be divided into one or more units, which are stored in the memory 71 and executed by the processor 70 to accomplish the present invention. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 72 in the terminal 7. For example, the computer program 72 may be divided into a knowledge graph constructing unit 61, a classification calculating unit 62, a recruitment recommendation service generating unit 63, and a recruitment recommendation service pushing unit 64, and the specific functions of the units are as follows:
the knowledge map construction unit 61 is used for constructing a knowledge map according to the enterprise information and the recruitment member information, wherein the knowledge map comprises a plurality of nodes of enterprise entities and recruitment member entities and attribute information of the entities;
the classification calculating unit 62 is configured to calculate similarities between enterprises and between engaging members according to the attribute information, so as to classify the enterprises and the engaging members in the knowledge graph;
the recruitment recommendation service generation unit 63 is configured to score the same type of enterprises or the same type of employees according to preset indexes, perform weighted sorting on scoring results, and generate corresponding recruitment recommendation service;
and the recruitment recommendation service pushing unit 64 is used for correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
Specifically, the knowledge graph constructing unit 61 includes:
the knowledge extraction subunit is used for extracting entity names, relationship names and entity attribute information in an enterprise information database, a staff resume information database and enterprise description information captured from the Internet according to the structure of the RDF triple;
the knowledge fusion subunit is used for performing knowledge fusion on the extracted entity name, the relationship name and the entity attribute information to obtain structured RDF ternary group data;
and the knowledge graph constructing subunit is used for constructing a knowledge graph based on the structured RDF triple data.
Specifically, the classification calculating unit 62 is specifically configured to:
and according to the attribute information, calculating the similarity between enterprises and the similarity between engaging persons through a Pearson correlation coefficient so as to classify the enterprises and the engaging persons in the knowledge graph.
Specifically, the classification calculation unit 62 includes:
the first classification calculating subunit is used for calculating the similarity between the application personnel based on the first scoring results of different application personnel on different enterprises according to the attribute information and classifying the application personnel with the similarity reaching a preset threshold value as the same application personnel;
and the second classification calculating subunit is used for calculating the similarity between the enterprises based on the second grading results of the corresponding engaging persons of different enterprises according to the attribute information and classifying the enterprises with the similarity reaching a preset threshold value into the same enterprise.
Preferably, the preset index is an index set according to attribute information of an enterprise or an applicable member.
Preferably, the recruitment recommendation service generating unit 63 is further configured to:
and scoring the same type of enterprises or the same type of application personnel according to a preset scoring function and a preset index.
Preferably, the recruitment recommendation service generating unit 63 further includes:
the numerical value normalization processing subunit is used for setting the preset index as a function variable and normalizing the numerical value of the function variable;
the weight obtaining subunit is configured to obtain a weight corresponding to the preset index;
and the scoring calculation subunit is used for carrying out scoring calculation on the numerical value of the function variable subjected to the normalization processing and the weight corresponding to the preset index according to a preset scoring function so as to obtain a scoring result of the same type of enterprises or the same type of employees.
Specifically, the recruitment recommendation service comprises a first recruitment recommendation service and a second recruitment recommendation service; the first recruitment recommendation service is specifically a recruitment recommendation service containing recommendation information of the same type of application personnel; the second recruitment recommendation service is specifically a recruitment recommendation service containing the recommendation information of the same type of enterprises; the recruitment recommendation service pushing unit 64 includes:
the first recruitment recommendation service pushing subunit is used for pushing the first recruitment recommendation service to the same type of enterprises;
and the first recruitment recommendation service pushing subunit is used for pushing the second recruitment recommendation service to the same type of employing members.
The terminal 7 may be a desktop computer, a notebook, a palm computer, a smart phone, or other terminal equipment. The terminal 7 may include, but is not limited to, a processor 70, a memory 71. It will be appreciated by those skilled in the art that fig. 7 is only an example of a terminal 7 and does not constitute a limitation of the terminal 7, and that it may comprise more or less components than those shown, or some components may be combined, or different components, for example the terminal may further comprise input output devices, network access devices, buses, etc.
The Processor 70 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the terminal 7, such as a hard disk or a memory of the terminal 7. The memory 71 may also be an external storage device of the terminal 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the terminal 7. The memory 71 is used for storing the computer program and other programs and data required by the terminal. The memory 71 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the system is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system/terminal device and method can be implemented in other ways. For example, the above-described system/terminal device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or system capable of carrying said computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method of pushing a recruitment recommendation service, the method comprising:
constructing a knowledge graph according to the enterprise information and the information of the applicant, wherein the knowledge graph comprises a plurality of nodes of enterprise entities and the entities of the applicant and attribute information of the entities;
according to the attribute information, calculating the similarity between enterprises and the similarity between engaging members so as to classify the enterprises and the engaging members in the knowledge graph;
scoring is carried out on the same type of enterprises or the same type of application personnel according to preset indexes, the scoring results are weighted and sorted, and corresponding recruitment recommendation service is generated;
and correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel.
2. The method of claim 1, wherein the step of constructing the knowledge-graph based on the business information and the applicant information comprises:
extracting entity names, relationship names and attribute information of entities in enterprise description information and recruitment information captured from the Internet according to the structure of a resource description frame RDF triple;
performing knowledge fusion on the extracted entity name, the relationship name and the attribute information of the entity to obtain structured RDF ternary group data;
and constructing a knowledge graph based on the structured RDF triple data.
3. The method of claim 1, wherein the step of calculating the similarity between businesses and the similarity between engaging members to classify the businesses and engaging members in the knowledge graph based on the attribute information comprises:
and according to the attribute information, calculating the similarity between enterprises and the similarity between engaging persons through a Pearson correlation coefficient so as to classify the enterprises and the engaging persons in the knowledge graph.
4. The method of claim 1, wherein the scoring results comprise a first scoring result and a second scoring result; the step of calculating the similarity between the enterprises and the similarity between the engaging personnel to classify the enterprises and the engaging personnel in the knowledge graph comprises the following steps:
calculating the similarity between the application personnel based on the first scoring results of different enterprise by different application personnel according to the attribute information, and classifying the application personnel with the similarity reaching a preset threshold value as the same type of application personnel;
and calculating the similarity between the enterprises based on the second grading results of the corresponding engaging members of different enterprises according to the attribute information, and classifying the enterprises of which the similarity reaches a preset threshold value into the same type of enterprises.
5. The method of claim 1, wherein the preset indicator is an indicator set according to attribute information of an enterprise or an applicant.
6. The method of claim 5, wherein the step of scoring the same type of enterprise or the same type of applicant according to the preset indicator comprises:
and scoring the same type of enterprises or the same type of application personnel according to a preset scoring function and a preset index.
7. The method of claim 6, wherein the step of scoring the same type of business or the same type of applicant according to a preset score function and according to a preset indicator comprises:
setting the preset index as a function variable, and carrying out normalization processing on the numerical value of the function variable;
acquiring the weight corresponding to the preset index;
and carrying out scoring calculation on the numerical value of the function variable subjected to the normalization processing and the weight corresponding to the preset index according to a preset scoring function to obtain a scoring result of the same type of enterprises or the same type of employees.
8. The method of any of the claims 1-7, wherein the recruitment recommendation service comprises a first recruitment recommendation service and a second recruitment recommendation service; the first recruitment recommendation service is specifically a recruitment recommendation service containing recommendation information of the same type of application personnel; the second recruitment recommendation service is specifically a recruitment recommendation service containing the recommendation information of the same type of enterprises; the step of correspondingly pushing the recruitment recommendation service to the same type of enterprises or the same type of application personnel comprises the following steps:
pushing the first recruitment recommendation service to the same type of enterprises;
and pushing the second recruitment recommendation service to the same type of employing personnel.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method for push recruitment recommendation service according to any one of claims 1 to 8.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the method for pushing a recruitment recommendation service according to any one of claims 1-8.
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