CN116628340B - Position agent recommending method and system - Google Patents
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- CN116628340B CN116628340B CN202310709258.8A CN202310709258A CN116628340B CN 116628340 B CN116628340 B CN 116628340B CN 202310709258 A CN202310709258 A CN 202310709258A CN 116628340 B CN116628340 B CN 116628340B
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- 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
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- 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
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a job agent recommending method, which comprises the following steps: acquiring behavior data and characteristic information of a user; acquiring a candidate position agent list, and behavior data and characteristic information of each position agent in the list; calculating feature similarity and behavior similarity according to the feature information of the user, the feature information of the job agency, the behavior data of the user and the behavior data of the job agency; calculating the matching degree between the user and each position agent according to the feature similarity and the behavior similarity; according to the matching degree, sequencing each job position agent, taking the job position agents sequenced in the front N job position agents in the candidate job position agent list as target job position agents, and recommending the target job position agents to the user, wherein N is more than or equal to 1; and, job agent recommendation systems. The recommendation method and the recommendation system can effectively improve the recommendation precision and satisfaction of the position agent.
Description
Technical Field
The present invention relates to the field of artificial intelligence and big data. More particularly, the invention relates to a job agent recommending method and system.
Background
With the development and popularization of internet technology, more and more job seekers and recruiters issue and search professional information through a network platform. However, due to the reasons of huge amount of professional information, uneven quality, high updating speed, high matching difficulty and the like on the network platform, the problems of asymmetric information and low matching efficiency exist between job seekers and recruiters. In order to solve these problems, some network platforms provide job recommendation functions based on keyword searching, filtering condition screening, ordering rule ordering and other modes, so as to help job seekers and recruiters to quickly find suitable positions or candidates. However, the above-mentioned job recommendation function still has certain limitations and disadvantages, and cannot fully satisfy the needs and expectations of job seekers and recruiters.
In order to solve the above-mentioned problems, the traditional offline job hunting market and some network platforms still adopt a recommendation mode of matching job seekers with recruiters through job agents, such as hunting heads, talent intermediaries, etc. However, the existing job agent recommending function only usually considers the matching degree of a single dimension between a recruiter or job seeker and the job agent, such as professional skills, working experience, salary expectations and the like, and has the problems of inaccurate recommendation, poor satisfaction and the like.
Disclosure of Invention
It is an object of the present invention to provide a job agent recommendation method to solve the above-mentioned problems.
To achieve the objects and other advantages and in accordance with the purpose of the invention, there is provided a job agent recommendation method, comprising: acquiring behavior data and characteristic information of a user; acquiring a candidate position agent list, and behavior data and characteristic information of each position agent in the list; calculating the similarity between users or between job agents according to the characteristic information and the behavior data of the users and the characteristic information and the behavior data of the job agents; calculating the matching degree between the user and each position agent according to the similarity between the users or the position agents; and sequencing each job agent according to the matching degree, taking the job agents sequenced in the front N in the candidate job agent list as target job agents, and recommending the target job agents to the user, wherein N is more than or equal to 1.
Preferably, the job agent recommending method further includes: receiving selection information of the user on the target position agent, and establishing contact between the user and the target position agent selected by the user; the interactive data of the user and/or the target position agent selected by the user are obtained, including browsing, collecting, applying, publishing, registering and evaluating; and updating the behavior data of the user and the behavior data of the target position agent according to the interaction data, recalculating the similarity and the matching degree between the user and the target position agent, and then reordering and recommending each position agent according to the matching degree.
Preferably, the method for recommending the job agent includes: acquiring input information of a user, wherein the input information comprises a user type, a user identifier, a user profile and a user professional tendency; according to the user type and the user identification, crawling behavior data of the user, including publishing, browsing, collecting, applying, inviting and evaluating of the user; and extracting the characteristics of the user according to the input information and the behavior data of the user to obtain the characteristic information of the user, wherein the characteristic information comprises basic characteristics, professional characteristics and personality characteristics of the user.
Preferably, the job agent recommending method, the obtaining a candidate job agent list, and behavior data and feature information of each job agent in the list, includes: acquiring a candidate position agent list according to the user type, the user profile and/or the user occupation tendency, wherein the candidate position agent list records information of each position agent, including position agent identification, position agent profile and position agent occupation capacity; according to the mark of the staff member, the behavior data of the staff member is crawled, wherein the behavior data comprise a staff list, a user list and a user evaluation, which are proxied by the staff member; and extracting characteristics of the staff member according to the staff member introduction, the staff member occupation capability and the behavior data of the staff member to obtain characteristic information of the staff member, wherein the characteristic information comprises basic characteristics, professional characteristics and individual characteristics of the staff member.
The invention also provides a job agent recommendation system, which comprises: the first acquisition module is used for acquiring behavior data and characteristic information of a user; the second acquisition module is used for acquiring a candidate position agent list and behavior data and characteristic information of each position agent in the list; the similarity calculation module is used for calculating the similarity between users or between job agents according to the characteristic information and the behavior data of the users and the characteristic information and the behavior data of the job agents; the matching degree calculation module is used for calculating the matching degree between the user and each position agent according to the similarity between the users or the position agents; and the recommending module is used for sequencing each job position agent according to the matching degree, taking the job position agents sequenced in the front N job position agents in the candidate job position agent list as target job position agents, and recommending the target job position agents to the user, wherein N is more than or equal to 1.
Preferably, the job agent recommendation system further comprises: the association module is used for receiving the selection information of the user on the target position agent and establishing the connection between the user and the selected target position agent; the third acquisition module is used for acquiring interaction data of the user and/or the target position agent selected by the user, and comprises browsing, collecting, applying, publishing, registering and evaluating; and the updating module is used for updating the behavior data of the user and the behavior data of the target position agent according to the interaction data, recalculating the similarity and the matching degree between the user and the target position agent, and then reordering and recommending each position agent according to the matching degree.
Preferably, the job agent recommendation system, the obtaining the behavior data and the feature information of the user includes: acquiring input information of a user, wherein the input information comprises a user type, a user identifier, a user profile and a user professional tendency; according to the user type and the user identification, crawling behavior data of the user, including publishing, browsing, collecting, applying, inviting and evaluating of the user; and extracting the characteristics of the user according to the input information and the behavior data of the user to obtain the characteristic information of the user, wherein the characteristic information comprises basic characteristics, professional characteristics and personality characteristics of the user.
Preferably, the job agent recommendation system, the obtaining a candidate job agent list, and behavior data and feature information of each job agent in the list, includes: acquiring a candidate position agent list according to the user type, the user profile and/or the user occupation tendency, wherein the candidate position agent list records information of each position agent, including position agent identification, position agent profile and position agent occupation capacity; according to the mark of the staff member, the behavior data of the staff member is crawled, wherein the behavior data comprise a staff list, a user list and a user evaluation, which are proxied by the staff member; and extracting characteristics of the staff member according to the staff member introduction, the staff member occupation capability and the behavior data of the staff member to obtain characteristic information of the staff member, wherein the characteristic information comprises basic characteristics, professional characteristics and individual characteristics of the staff member.
The invention also provides an electronic device, comprising: the system comprises at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to cause the at least one processor to perform the method described above.
The present invention also provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described method.
The invention at least comprises the following beneficial effects:
the job agent recommending method and system can comprehensively consider the matching degree of multiple aspects, multiple layers and multiple dimensions between the user (recruiter or job seeker) and the job agent, and recommend the most possibly satisfactory job agent to the user, thereby improving recommending effect and satisfaction.
According to the job agent recommending method and system, the matching degree between the user and the target job agent can be dynamically updated according to the interactive data between the user and/or the selected target job agent, and recommending precision and adaptability are improved.
Thirdly, the job agent recommending method and system of the invention are used for calculating the matching degree based on the characteristic information and the behavior data of the user and the job agent, and the characteristic information can be dynamically adjusted based on the behavior data of the user and the job agent, thereby avoiding the defects of exaggeration, deceiving and the like of the user when the off-line job agent recruits, truly reflecting the matching degree between the user and the job agent and improving the recommending efficiency.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
FIG. 1 is a flow chart of a job agent recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a job agent recommendation system according to an embodiment of the present invention.
Detailed Description
The present invention is described in further detail below with reference to examples and drawings to enable those skilled in the art to practice the same and to refer to the description.
It will be understood that terms, such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.
The experimental methods described in the following embodiments are conventional methods unless otherwise indicated, and the reagents and materials are commercially available.
As shown in fig. 1, the present invention provides a job agent recommending method, which includes the following steps:
s1, acquiring behavior data and characteristic information of a user.
The invention provides a method for acquiring behavior data and characteristic information of a user, wherein the method comprises the following steps of:
s101, acquiring input information of a user, wherein the input information comprises a user type, a user identification, a user profile, a user professional tendency and the like. The user type may be a recruiter or job seeker; the user identification can be at least one of an account number, a nickname and a mobile phone number of the user; the user profile may include the user's name, age, gender, academic, profession, work experience, project experience, skill certificate, etc.; the professional trends of the user may include industries, posts, places, salaries, benefits, etc. desired by the user.
S102, according to the user type and the user identification of the user, crawling behavior data of the user, including historical records of release, browsing, collection, application, invitation, evaluation and the like of the user. The behavior data of the user can also be crawled and stored in a database in advance, the database can be a local database or a cloud database, various types of data including text, pictures, audio, video and the like can be stored in the database, and the behavior data of the user is obtained by inquiring from the database. The user release record may include positions, time, etc. released by the user; the browsing record of the user can comprise the mark, time, duration and the like of the position or the position agent browsed by the user; the collection records of the user can comprise the marks, time and the like of the positions collected by the user or the position agent; the application record of the user can comprise the identity, time, state and the like of the post applied by the user or the post agent invited by the user; the user's invitation record may include the user's invited job or the invited job agent's identity, time, status, etc.; the user's rating record may include the user's rating of the position (e.g., whether the position is liked) or the user's rating of the position agent (e.g., whether the agent is liked, degree of liked: scoring), etc.
S103, extracting characteristics of the user according to the information input by the user and the behavior data of the user, wherein the characteristics comprise basic characteristics, professional characteristics, personality characteristics and the like of the user. The feature extraction may employ various machine learning or deep learning algorithms, such as Support Vector Machines (SVMs), decision Trees (DTs), random Forests (RFs), logistic Regression (LR), neural Networks (NN), convolutional Neural Networks (CNN), recurrent Neural Networks (RNN), long-short term memory networks (LSTM), attention mechanisms (Attention), etc. Wherein, the basic characteristics of the user can comprise age bracket, gender, academic history, professional tendency of the user and the like; the professional characteristics of the user may include the user's professional field, skill level, work experience, project experience, etc.; the personality characteristics of the user may include the user's personality type, communication style, value concept, hobbies, and so forth.
S2, acquiring a candidate position agent list, and behavior data and characteristic information of each position agent in the list. The method specifically comprises the following steps:
s201, acquiring a candidate position agent list according to the user type, the user profile and/or the user professional tendency, wherein the candidate position agent list records information of each position agent, including position agent identification, position agent profile and position agent professional capability. Before the candidate position agent list is obtained, information and behavior data of each position agent are crawled in advance and stored in a database, and then available position agents are inquired from the database according to the user types, the user profiles and/or the user professional trends to obtain the candidate position agent list. The mark of the position agent can be at least one of an account number, an identity card number and a mobile phone number of the position agent; the profile of the job agent may include the job duration, age, gender, academic, professional domain, work experience, project experience, skill certificate, etc. of the job agent; the professional capabilities of the job agent may include the number of entries obtained by the job agent, the job type of the agent, the users that have been serviced, whether being evaluated, etc.
S202, according to the mark of the job agent, the behavior data of the job agent is crawled from a database, wherein the behavior data comprise the job issued by the job agent (comprising type, time, job label and the like), a job list after agent, a user list after service, evaluation on the user (such as whether the user is liked or not), liveness and the like. The job list selects the job of the agent for the job agent, and the list of users served by the job agent may include an identification, time, status, etc. of each user.
S203, extracting characteristics of the job agent according to the job agent profile, job agent occupational capacity and behavior data of the job agent, and obtaining characteristic information of the job agent, wherein the characteristic information comprises basic characteristics, professional characteristics and individual characteristics of the job agent. The feature extraction may be performed using a machine learning or deep learning algorithm as listed in S103. The basic characteristics of each job agent may include age group, gender, academic calendar, etc. of each job agent; the professional characteristics of each job agent may include the professional field, skill level, work experience, project experience, etc. of each job agent; the personality characteristics of each job agent may include personality type, communication style, value concept, hobbies, etc. of each job agent.
And S3, calculating the similarity between the users or between the job agents according to the characteristic information and the behavior data of the users and the characteristic information and the behavior data of the job agents. The method specifically comprises the following steps:
s301, constructing a user-article scoring matrix according to the characteristic information and the behavior data of the user and the characteristic information and the behavior data of the job agent, wherein the user can be a job seeker or a recruiter, and the article is the job agent.
S302, calculating the similarity between users or between job agents by adopting cosine similarity, pearson correlation coefficient, jaccard coefficient and other methods according to the user-object scoring matrix.
And S4, calculating the matching degree between the user and each position agent according to the similarity between the users or the position agents.
Specifically, according to the similarity between users or between job agents and the grading of the users to the job agents, a collaborative filtering algorithm based on the users or a collaborative filtering algorithm based on articles is adopted to predict the grading of the users to the ungraded job agents, and the matching degree=the grading/grading score full of the users to the job agents, if the grading of the users to a certain job agent is 4 points, the grading full is 5 points, and the matching degree is 0.8. The scoring of the user to the position agent is obtained by calculating according to the evaluation of the user to the position or the position agent by adopting a graph analysis algorithm such as pagerank.
S5, sorting each job agent according to the matching degree, taking the job agents sorted in the front N in the candidate job agent list as target job agents, and recommending the target job agents to the user, wherein N is more than or equal to 1.
Specifically, the matching degree of the user and all the position agents is ranked from high to low, a recommendation list is generated, the position agents ranked in N positions are used as target position agents to be recommended to the user, information of each position agent is displayed, such as identification, brief introduction, professional capability, matching degree and the like of the position agent, and N is more than or equal to 1. The specific recommended number may be determined according to the needs of the user or the settings of the system, such as the first three, the first five, the first ten, etc. of the recommendations. The information displayed may be determined according to the preference of the user or the setting of the system, such as displaying all information, displaying part information, displaying summary information, and the like.
S6, receiving selection information of the user on the target position agent, and establishing contact between the user and the target position agent selected by the user.
Specifically, a user selection of one or more target job agents is received and a connection is established between the user and the selected job agents. The user may select one or more job agents, such as selecting the best matching one, selecting the ones of most interest, selecting the ones of different styles, etc., according to his own needs or preferences.
S7, acquiring interaction data of the user and/or the target position agent selected by the user, wherein the interaction data comprise browsing, collecting, applying, publishing, registering, evaluating and the like.
Specifically, the interactive data includes time and duration when the user browses the target position agent or the position of the target position agent, whether the user collects or applies for the position of the target position agent, the user evaluates the target position agent, the user evaluates the position of the target position agent, the target position agent evaluates the user, the target position agent issues the position at the current time point, and obtains the position of the registering (the position after the agent).
And S8, updating the behavior data of the user and the behavior data of the target position agent according to the interaction data, recalculating the similarity and the matching degree between the user and the target position agent, and carrying out reordering and recommendation on each position agent according to the matching degree.
Specifically, according to the actual interaction condition of the user and the target position agent, the matching degree of the user and the target position agent is dynamically adjusted, so that the recommendation effect and satisfaction degree can be improved. For example, the initial match between the job seeker a and the job agent B is high, B is recommended to a, and a selects B, but case 1: a browses the relevant positions of the agent of the position B, but does not apply for (the registration is not generated by the position B), or the A dislike evaluates the position B, so that the matching degree of the position A and the position B is reduced when the interaction data of the position A and the position B are recalculated; case 2: b introduces self-recruitment financial staff (A is financial staff) in the brief introduction, and the position issued by B and the position for obtaining the registration are programmers at the current time point, so that the matching degree of A and B is reduced during recalculation, and meanwhile B is matched with the programmers more; after the matching degree of A and B is adjusted, the position agent recommendation list is automatically reordered, and more proper position agents are recommended to the user A.
As shown in fig. 2, the present invention further provides a job agent recommendation system, including:
the first acquisition module is used for acquiring behavior data and characteristic information of a user, and specifically comprises the following steps: acquiring input information of a user, wherein the input information comprises a user type, a user identifier, a user profile and a user professional tendency; according to the user type and the user identification, crawling behavior data of the user, including publishing, browsing, collecting, applying, inviting and evaluating of the user; and extracting the characteristics of the user according to the input information and the behavior data of the user to obtain the characteristic information of the user, wherein the characteristic information comprises basic characteristics, professional characteristics and personality characteristics of the user.
The second acquisition module is used for acquiring a candidate position agent list and behavior data and characteristic information of each position agent in the list; the method specifically comprises the following steps: acquiring a candidate position agent list according to the user type, the user profile and/or the user occupation tendency, wherein the candidate position agent list records information of each position agent, including position agent identification, position agent profile and position agent occupation capacity; according to the mark of the staff member, the behavior data of the staff member is crawled, wherein the behavior data comprise a staff list, a user list and a user evaluation, which are proxied by the staff member; and extracting characteristics of the staff member according to the staff member introduction, the staff member occupation capability and the behavior data of the staff member to obtain characteristic information of the staff member, wherein the characteristic information comprises basic characteristics, professional characteristics and individual characteristics of the staff member.
And the similarity calculation module is used for calculating the similarity between the users or between the job agents according to the characteristic information and the behavior data of the users and the characteristic information and the behavior data of the job agents.
And the matching degree calculation module is used for calculating the matching degree between the user and each position agent according to the similarity between the users or the position agents.
And the recommending module is used for sequencing each job position agent according to the matching degree, taking the job position agents sequenced in the front N job position agents in the candidate job position agent list as target job position agents, and recommending the target job position agents to the user, wherein N is more than or equal to 1.
And the association module is used for receiving the selection information of the user on the target position agent and establishing the connection between the user and the selected target position agent.
And the third acquisition module is used for acquiring the interaction data of the user and/or the target position agent selected by the user, and comprises browsing, collecting, applying, publishing, registering and evaluating.
And the updating module is used for updating the behavior data of the user and the behavior data of the target position agent according to the interaction data, recalculating the similarity and the matching degree between the user and the target position agent, and then reordering and recommending each position agent according to the matching degree.
The technical scheme is based on the same inventive concept as the recommending method of the job agent, and the description of the recommending method part can be referred to for understanding the technical scheme.
The present invention also provides a storage medium including: various media, such as ROM, RAM, magnetic or optical disks, may store program code that, when loaded into and executed by a processor, performs all or part of the steps of the job agent recommendation method described above.
The invention also provides an electronic device, which is a device comprising a processor (CPU/MCU/SOC), a memory (ROM/RAM), such as: desktop computers, portable computers, smart phones, etc. In particular, the memory stores a computer program, which, when loaded and executed, implements all or part of the steps of the job agent recommendation method described above.
The number of equipment and the scale of processing described herein are intended to simplify the description of the present invention. Applications, modifications and variations of the present job agent recommendation method and system will be apparent to those skilled in the art.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (4)
1. The job agent recommending method is characterized by comprising the following steps:
acquiring behavior data and characteristic information of a user;
acquiring a candidate position agent list, and behavior data and characteristic information of each position agent in the list;
calculating the similarity between users or between job agents according to the characteristic information and the behavior data of the users and the characteristic information and the behavior data of the job agents;
calculating the matching degree between the user and each position agent according to the similarity between the users or the position agents;
according to the matching degree, sequencing each job position agent, taking the job position agents sequenced in the front N job position agents in the candidate job position agent list as target job position agents, and recommending the target job position agents to the user, wherein N is more than or equal to 1;
further comprises:
receiving selection information of the user on the target position agent, and establishing contact between the user and the target position agent selected by the user;
the interactive data of the user and/or the target position agent selected by the user are obtained, including browsing, collecting, applying, publishing, registering and evaluating;
updating the behavior data of the user and the behavior data of the target position agent according to the interaction data, recalculating the similarity and the matching degree between the user and the target position agent, and then reordering and recommending each position agent according to the matching degree;
the obtaining the behavior data and the characteristic information of the user comprises the following steps:
acquiring input information of a user, wherein the input information comprises a user type, a user identifier, a user profile and a user professional tendency;
according to the user type and the user identification, crawling behavior data of the user, including publishing, browsing, collecting, applying, inviting and evaluating of the user;
extracting characteristics of the user according to the input information and behavior data of the user to obtain characteristic information of the user, wherein the characteristic information comprises basic characteristics, professional characteristics and personality characteristics of the user;
the obtaining the candidate position agent list, and the behavior data and the characteristic information of each position agent in the list includes:
acquiring a candidate position agent list according to the user type, the user profile and/or the user occupation tendency, wherein the candidate position agent list records information of each position agent, including position agent identification, position agent profile and position agent occupation capacity;
according to the mark of the staff member, the behavior data of the staff member is crawled, wherein the behavior data comprise a staff list, a user list and a user evaluation, which are proxied by the staff member;
and extracting characteristics of the staff member according to the staff member introduction, the staff member occupation capability and the behavior data of the staff member to obtain characteristic information of the staff member, wherein the characteristic information comprises basic characteristics, professional characteristics and individual characteristics of the staff member.
2. The job agent recommendation system is characterized by comprising:
the first acquisition module is used for acquiring behavior data and characteristic information of a user;
the second acquisition module is used for acquiring a candidate position agent list and behavior data and characteristic information of each position agent in the list;
the similarity calculation module is used for calculating the similarity between users or between job agents according to the characteristic information and the behavior data of the users and the characteristic information and the behavior data of the job agents;
the matching degree calculation module is used for calculating the matching degree between the user and each position agent according to the similarity between the users or the position agents;
the recommending module is used for sequencing each job position agent according to the matching degree, taking the job position agents sequenced in the front N job position agents in the candidate job position agent list as target job position agents, and recommending the target job position agents to the user, wherein N is more than or equal to 1;
further comprises:
the association module is used for receiving the selection information of the user on the target position agent and establishing the connection between the user and the selected target position agent;
the third acquisition module is used for acquiring interaction data of the user and/or the target position agent selected by the user, and comprises browsing, collecting, applying, publishing, registering and evaluating;
the updating module is used for updating the behavior data of the user and the behavior data of the target position agent according to the interaction data, recalculating the similarity and the matching degree between the user and the target position agent, and then reordering and recommending each position agent according to the matching degree;
the obtaining the behavior data and the characteristic information of the user comprises the following steps:
acquiring input information of a user, wherein the input information comprises a user type, a user identifier, a user profile and a user professional tendency;
according to the user type and the user identification, crawling behavior data of the user, including publishing, browsing, collecting, applying, inviting and evaluating of the user;
extracting characteristics of the user according to the input information and behavior data of the user to obtain characteristic information of the user, wherein the characteristic information comprises basic characteristics, professional characteristics and personality characteristics of the user;
the obtaining the candidate position agent list, and the behavior data and the characteristic information of each position agent in the list includes:
acquiring a candidate position agent list according to the user type, the user profile and/or the user occupation tendency, wherein the candidate position agent list records information of each position agent, including position agent identification, position agent profile and position agent occupation capacity;
according to the mark of the staff member, the behavior data of the staff member is crawled, wherein the behavior data comprise a staff list, a user list and a user evaluation, which are proxied by the staff member;
and extracting characteristics of the staff member according to the staff member introduction, the staff member occupation capability and the behavior data of the staff member to obtain characteristic information of the staff member, wherein the characteristic information comprises basic characteristics, professional characteristics and individual characteristics of the staff member.
3. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of claim 1.
4. A storage medium having stored thereon a computer program, which when executed by a processor, implements the method of claim 1.
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