WO2020173140A1

WO2020173140A1 - Part-time job matching method and system, and storage medium

Info

Publication number: WO2020173140A1
Application number: PCT/CN2019/118379
Authority: WO
Inventors: 金波
Original assignee: 北京多点在线科技有限公司
Priority date: 2019-02-26
Filing date: 2019-11-14
Publication date: 2020-09-03
Also published as: CN109934468A

Abstract

Disclosed are a part-time job matching method and system as well as a storage medium, relating to the field of artificial intelligence. The method comprises: acquiring user data input by part-timers, task data issued by task publishers and user behavior data of the part-timers (S101); performing big data analysis on the basis of the user data, the task data and the user behavior data, and matching a task with a part-timer by means of an artificial intelligence algorithm model (S102); and pushing the matching result to a client terminal of the matched part-timer for display (S103). The method shortens the match time between part-timers and suitable part-time jobs, is more efficient than manual matching, avoids missed reading due to manual browsing so as to prevent the phenomenon that matched part-time jobs cannot be seen by the part-timers, enhances the acquisition rate of part-time job data, and increases the amount of part-time job information displayed to target users.

Description

Part-time job matching method, system and storage medium

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on February 26, 2019, the application number is 201910143174.6, and the invention title is "a part-time job matching method and system, and storage medium", the entire content of which is incorporated by reference In this application.

Technical field

The present invention relates to the field of artificial intelligence technology, in particular to a part-time job matching method, system and storage medium.

Background technique

With the popularization and development of Internet applications, the Internet has more and more impacts on people’s lives and work, and people’s working methods have gradually changed. Many Internet users with time and skills hope to get paid by completing Internet work so that people can More freedom in time and space. But the acquisition and selection of work tasks has become a new inevitable problem. The existing technical solutions for part-time websites mainly provide services to users through classification or search. It is difficult for users to quickly obtain high-precision matching tasks at low cost. There are also too much junk data, unclear content understanding, and poor matching. Precision and other issues. The main problems in the prior art are as follows:

The user clicks through a large number of categories (or search keywords) to obtain the task list level by level, narrowing the browsing range, long operation steps, browsing too many invalid messages, resulting in a waste of time;

The task list is complex and diverse, causing difficulties and even misunderstandings.

Summary of the invention

The embodiment of the present invention provides a part-time job matching method, system, and storage medium. In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not a general review, nor is it intended to identify key/important elements or describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

According to the first aspect of the embodiments of the present invention, there is provided a part-time job matching method, including:

Obtain user data entered by part-time users, task data released by task publishers, and user behavior data of part-time users;

Perform big data analysis based on the user data, the task data, and the user behavior data, and match tasks with part-time users through artificial intelligence algorithm models;

Push the matching result to the client of the matched part-time user for display.

In an embodiment, the user data includes:

User’s basic personal information and user’s skill information.

In an embodiment, the basic personal information includes:

Real name, age, education experience, work experience;

The skill information includes:

Service content, price, response time.

In an embodiment, the user behavior data includes:

User historical transaction data, user evaluation data.

In one embodiment, the artificial intelligence algorithm model is a machine learning algorithm model.

In an embodiment, it further includes:

Based on the user behavior data, the machine learning algorithm model is optimized through machine learning.

In one embodiment, the method is implemented based on a client/server (Client/Server, C/S) architecture.

Another aspect of the present invention provides a part-time job matching system, including:

The client terminal is used to obtain the user data input by the part-time user, the task data issued by the task publisher, and the user behavior data of the part-time user, and send the user data, the task data and the user behavior data to the data server, and Push the received matching results to the matched part-time users;

A data server, configured to perform big data analysis based on the user data, the task data, and the user behavior data;

The intelligent computing server is used for matching tasks with part-time users through artificial intelligence algorithm models based on the results of big data analysis, and sending the matching results to the client.

In one embodiment, the artificial intelligence algorithm model is a machine learning algorithm model, and the intelligent computing server is further configured to optimize the machine learning algorithm model through machine learning based on the user behavior data.

In another aspect of the present invention, a storage medium is provided with a computer program stored thereon, and when the computer program is executed by a processor, the part-time job matching method provided by the embodiment of the present invention is implemented.

The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

The method provided by the present invention realizes big data collection and analysis and artificial intelligence algorithms of machine learning through cloud services to realize the user's demand for accurately obtaining part-time tasks, and the user does not need to perform the operation of screening part-time tasks, and does not need to worry about wrong operations;

For users, the threshold for experience and learning is lowered. In view of the current large amount of part-time information, the efficiency of selecting part-time information is greatly improved, and a lot of time and bandwidth are saved;

For task publishers, it is possible to obtain more high-quality part-time personnel more quickly to intercept tasks and create value for the publisher;

The embodiment of the present invention shortens the matching time between part-time personnel and suitable part-time jobs, is relatively more efficient than manual matching, and avoids the omission of manual browsing, resulting in matching part-time jobs that cannot be seen by part-time personnel, which affects part-time data The acquisition rate has increased the amount of part-time information displayed to target users.

It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention.

Description of the drawings

The drawings herein are incorporated into the specification and constitute a part of the specification, show embodiments in accordance with the present invention, and together with the specification are used to explain the principle of the present invention.

Fig. 1 is a schematic flowchart showing a method for part-time job matching according to an exemplary embodiment;

Fig. 2 is a block diagram showing a part-time job matching system according to an exemplary embodiment.

detailed description

The following description and the drawings sufficiently illustrate specific embodiments of the present invention to enable those skilled in the art to practice them. Other implementations may include structural, logical, electrical, process, and other changes. The embodiments only represent possible changes. Unless explicitly required, individual components and functions are optional, and the order of operations can be changed. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The scope of the embodiments of the present invention includes the entire scope of the claims, and all available equivalents of the claims. In this document, each embodiment may be individually or collectively denoted by the term "invention", which is only for convenience, and if more than one invention is actually disclosed, it is not intended to automatically limit the scope of the application to any A single invention or inventive concept. In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not require or imply any actual relationship or relationship between these entities or operations. order. Moreover, the terms "including", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, or device including a series of elements includes not only those elements, but also other elements not explicitly listed. Elements, or also include elements inherent to such processes, methods, or equipment. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other same elements in the process, method, or device that includes the element. The various embodiments herein are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referred to each other. As for the methods, products, etc. disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method section.

As shown in Figure 1, an embodiment of the present invention provides a part-time job matching method, including:

S101. Obtain user data input by part-time users, task data released by task publishers, and user behavior data of part-time users;

S102: Perform big data analysis based on the user data, the task data, and the user behavior data, and realize the matching of tasks and part-time users through an artificial intelligence algorithm model;

S103: Push the matching result to the client of the matched part-time user for display.

In practical applications, this technical solution adopts a C/S structure to ensure user data security. Big data and intelligent algorithms are used to achieve precise matching of work tasks and functional distribution. The scheme is mainly realized by data server, intelligent computing server, client and so on.

When a user registers as a part-time employee, he fills in user data, the daily operation of the part-time user generates user behavior data, and the user who issues the task fills in the task data. In an embodiment, the hardware structure may include a data server, an intelligent computing server, and a client. The data server and the intelligent computing server are located in the cloud. The data server is based on big data technology to aggregate user data, task data, and user behavior data for management. For example, use Hadoop HDFS for big data storage, and store part-time personnel, number of people, user behavior data, etc., and then use MapReduce Offline calculation, collate and summarize the data results. In an implementation manner, the data server can also capture industry data from the network and manage it together with the above data. The intelligent computing server uses artificial intelligence algorithms, such as machine learning algorithms, to calculate user data, task data, etc., to determine the part-time tasks that match each part-time user. When the user requests in real time, the result of offline calculation is not enough. Perform a combination of lower latency, high throughput, unified stream computing and batch processing. Flink's FlinkML machine learning library is used to further strengthen the efficient matching of tasks and part-time personnel. And, according to user behavior data, machine learning techniques are used to continuously optimize machine learning algorithms. The client obtains user data, forwards it to the data server, receives the matching result from the intelligent computing server, and displays the matching result to the user. Through this process, the effect of intelligent matching from part-time personnel to work tasks is realized, and the results are presented to users. The client can be a software client on a mobile terminal or a page on a web browser.

In practical applications, the user data may include basic personal information of the user and skill information of the user.

Wherein, the basic personal information may include one or a combination of the following: real name, age, education experience, work experience;

The skill information may include one or a combination of the following: service content, price, and response time.

Among them, response time, the service provided for part-time personnel, and the time required to complete, generally refers to the time from the start of cooperation to the submission of part-time works.

Among them, the user behavior data includes one or a combination of the following: user historical transaction data and user evaluation data.

In the case of using a standard machine learning library, the standard algorithm still has a lot of room for optimization. In the process, this patent will re-model the user tags, task tags, user behaviors, etc. according to user behavior data. Optimize the matching algorithm to further improve the matching degree of the result.

As shown in Figure 2, another aspect of the present invention provides a part-time job matching system, including:

The client 201 is configured to obtain user data input by a part-time user, task data issued by a task publisher, and user behavior data of the part-time user, and send the user data, the task data, and the user behavior data to the data server, And push the received matching results to the matched part-time users;

The data server 202 is configured to perform big data analysis based on the user data, the task data, and the user behavior data;

The intelligent computing server 203 is configured to match tasks with part-time users through an artificial intelligence algorithm model based on the results of big data analysis, and send the matching results to the client.

When a user registers as a part-time employee, he fills in user data, the daily operation of the part-time user generates user behavior data, and the user who issues the task fills in the task data. In an embodiment, the hardware structure may include a data server, an intelligent computing server, and a client. The data server and the intelligent computing server are located in the cloud. The data server is based on big data technology to aggregate user data, task data, and user behavior data for management. In one implementation, the data server can also capture industry data from the network and manage it together with the above data. The intelligent computing server uses artificial intelligence algorithms, such as machine learning algorithms, to calculate user data, task data, etc., to determine part-time tasks that match each part-time user, and to continuously optimize machine learning algorithms through machine learning technology based on user behavior data. The client obtains user data, forwards it to the data server, receives the matching result from the intelligent computing server, and displays the matching result to the user. Through this process, the effect of intelligent matching from part-time personnel to work tasks is realized, and the results are presented to users. The client can be a software client on a mobile terminal or a page on a web browser.

Another aspect of the present invention provides a storage medium on which a computer program is stored, characterized in that, when the computer program is executed by a processor, the part-time job matching method provided by the embodiment of the present invention is implemented.

The invention provides an artificial intelligence algorithm that realizes big data collection and analysis and machine learning through cloud services to realize the user's demand for accurately obtaining part-time tasks. Users do not need to perform the operation of screening part-time tasks, and do not need to worry about wrong operations;

The embodiment of the present invention shortens the matching time between part-time personnel and suitable part-time jobs, and is relatively shorter than manual matching, and has higher efficiency. It avoids that the matched part-time job cannot be seen by the part-time personnel due to the omission of manual browsing. Affect the rate of obtaining part-time data and increase the amount of part-time information displayed to target users.

In an exemplary embodiment, there is also provided a non-transitory computer-readable storage medium including instructions, such as a memory including instructions, which can be executed by a processor to complete the aforementioned method. The aforementioned non-transitory computer-readable storage medium may be a read only memory (Read Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic tape, an optical storage device, etc.

Those skilled in the art may be aware that the units and algorithm steps of the examples described in the embodiments disclosed in this specification can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention. Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the embodiments disclosed herein, it should be understood that the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or It can be integrated into another system, or some features can be ignored or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the solutions of the embodiments. In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.

It should be understood that the flowcharts and block diagrams in the drawings show the possible implementation architecture, functions, and operations of systems, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, program segment, or part of the code, and the module, program segment, or part of the code contains one or more functions for realizing the specified logical function. Executable instructions. It should also be noted that, in some alternative implementations, the functions marked in the block may also occur in a different order from the order marked in the drawings. For example, two consecutive blocks can actually be executed in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions. The present invention is not limited to the processes and structures that have been described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

A part-time job matching method is characterized in that it includes:

Obtain user data entered by part-time users, task data released by task publishers, and user behavior data of part-time users;

Perform big data analysis based on the user data, the task data, and the user behavior data, and match tasks with part-time users through artificial intelligence algorithm models;

Push the matching result to the client of the matched part-time user for display.
The method of claim 1, wherein the user data includes:

User’s basic personal information and user’s skill information.
The method according to claim 2, wherein the basic personal information includes:

Real name, age, education experience, work experience;

The skill information includes:

Service content, price, response time.
The method of claim 1, wherein the user behavior data includes:

User historical transaction data, user evaluation data.
The method of claim 1, wherein the artificial intelligence algorithm model is a machine learning algorithm model.
The method of claim 5, further comprising:

Based on the user behavior data, the machine learning algorithm model is optimized through machine learning.
The method according to claim 1, wherein the method is implemented based on a client/server C/S architecture.
A part-time job matching system is characterized in that it includes:

The client terminal is used to obtain the user data input by the part-time user, the task data issued by the task publisher, and the user behavior data of the part-time user, and send the user data, the task data, and the user behavior data to the data server, and Push the received matching results to the matched part-time users;

A data server, configured to perform big data analysis based on the user data, the task data, and the user behavior data;

The intelligent computing server is used for matching tasks with part-time users through artificial intelligence algorithm models based on the results of big data analysis, and sending the matching results to the client.
The system of claim 8, wherein the artificial intelligence algorithm model is a machine learning algorithm model, and the intelligent computing server is further configured to optimize the machine learning through machine learning based on the user behavior data Algorithm model.
A storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the part-time job matching method according to claim 1 is implemented.