WO2019024496A1

WO2019024496A1 - Enterprise recommendation method and application server

Info

Publication number: WO2019024496A1
Application number: PCT/CN2018/077656
Authority: WO
Inventors: 汪伟; 徐冰; 王鸿滨; 朱伟峰; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-08-04
Filing date: 2018-02-28
Publication date: 2019-02-07
Also published as: CN108427695A

Abstract

Disclosed are an enterprise recommendation method and an application server (2), the application server (2) comprising a memory (11), a processor (12), and an enterprise recommendation program (200) stored in the memory (11) and capable of being run on the processor (12); the enterprise recommendation program (200) implements the following steps upon execution by the processor (12): establishing an enterprise feature table in advance; establishing an enterprise supply-to-demand relationship matching table individually according to the enterprise feature table; acquiring demand information of a target enterprise; and recommending a matched enterprise to the target enterprise according to the demand information and the enterprise supply-to-demand relationship matching table. The application server (2) and the enterprise recommendation method can be used to actively acquire demand information of an enterprise and recommend a matched enterprise to the enterprise according to the demand information of the enterprise.

Description

Enterprise recommendation method and application server

This application claims the priority of the Chinese Patent Application, filed on Aug. 4, 2017, the application Serial No.

Technical field

The present application relates to the field of communications technologies, and in particular, to an enterprise recommendation method and an application server.

Background technique

With the rise of the Internet + this new development, efficient cooperation between enterprises and enterprises is becoming more and more important. At present, cooperation between enterprises is a bit of a tradition in terms of efficiency and precision of cooperation. The company's basic information, business type, product service, competitiveness, credit status, industry dynamics, market information, potential customers and other information lack an efficient and accurate query matching method. The various implementations of the prior art, mostly traditional Internet technologies, rely on one-way active search queries by users.

Summary of the invention

In view of this, the present application proposes an enterprise recommendation method and an application server, which can actively acquire the demand information of the enterprise, and recommend a matching enterprise for the enterprise according to the demand information of the enterprise.

First, in order to achieve the above object, the present application provides an application server including a memory, a processor, and an enterprise recommendation program stored on the memory and executable on the processor, the enterprise recommendation program The following steps are implemented when executed by the processor:

Pre-establishing a corporate feature table;

Establishing a matching table of supply and demand relationships of each enterprise according to the enterprise characteristic table;

Obtain information on the needs of the target company; and

And matching the enterprise according to the demand information and the enterprise supply and demand relationship matching table for the target enterprise.

In addition, to achieve the above object, the present application further provides an enterprise recommendation method, which is applied to an application server, and the method includes:

Pre-establishing a corporate feature table;

Obtain information on the needs of the target company; and

Further, in order to achieve the above object, the present application further provides a computer readable storage medium storing an enterprise recommendation program, the enterprise recommendation program being executable by at least one processor to enable the At least one processor performs the steps of the enterprise recommendation method as described above.

Compared with the prior art, the application server, the enterprise recommendation method, and the computer readable storage medium proposed by the present application first establish an enterprise feature table in advance; and then, according to the enterprise feature table, establish a matching table of supply and demand relationships of each enterprise; And obtaining the demand information of the target enterprise; finally, recommending the matching enterprise for the target enterprise according to the demand information and the enterprise supply and demand relationship matching table. In this way, it is possible to avoid the disadvantages of the prior art enterprises spending a large amount of time and manpower actively searching for the cooperative enterprise, actively obtaining the demand information of the target enterprise, and recommending the matching enterprise for the target enterprise according to the demand information of the target enterprise. Achieving intelligent and efficient corporate recommendation services.

DRAWINGS

1 is a schematic diagram of an optional application environment of each embodiment of the present application;

2 is a schematic diagram of an optional hardware architecture of the application server of FIG. 1;

3 is a schematic diagram of program modules of the first, second, third and fourth embodiments of the enterprise recommendation procedure of the present application;

4 is a schematic diagram of an implementation process of a first embodiment of a method for recommending an enterprise according to the present application;

FIG. 5 is a schematic diagram of an implementation process of a second embodiment of a method for recommending an enterprise according to the present application;

6 is a schematic flowchart of an implementation process of a third embodiment of a method for recommending an enterprise according to the present application;

FIG. 7 is a schematic diagram of an implementation process of a fourth embodiment of a method for recommending an enterprise according to the present application.

Reference mark:

移动终端 Mobile terminal	11
应用服务器 application server	22
网络The internet	33
存储器Memory	1111
处理器processor	1212
网络接口Network Interface	1313
企业推荐程序 Corporate recommendation procedure	200200
第一建立模块First building module	201201
第二建立模块Second building module	202202
获取模块Acquisition module	203203
推荐模块Recommended module	204204

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

It should be noted that the descriptions of "first", "second" and the like in the present application are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. . Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly. In addition, the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.

Referring to FIG. 1 , it is a schematic diagram of an optional application environment of each embodiment of the present application.

In this embodiment, the present application is applicable to an application environment including, but not limited to, a mobile terminal 1, an application server 2, and a network 3. The mobile terminal 1 may be a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, an in-vehicle device, etc. Mobile devices such as, and fixed terminals such as digital TVs, desktop computers, notebooks, servers, and the like. The application server 2 may be a computing device such as a rack server, a blade server, a tower server, or a rack server. The application server 2 may be a stand-alone server or a server cluster composed of multiple servers. The network 3 may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, Wireless or wired networks such as 5G networks, Bluetooth, Wi-Fi, and call networks.

The application server 2 is respectively connected to one or more of the mobile terminals 1 (only one shown in the figure) through the network 3, and each of the mobile terminals 1 is installed and operated. The application client corresponding to the application server 2 (hereinafter referred to as "mobile terminal client"). The mobile terminal client is configured to create a long connection between the mobile terminal client and the application server 2 in response to an operation of the mobile terminal user, so that the mobile terminal client can pass the long connection and the The application server 2 performs data transmission and interaction.

In this embodiment, when the enterprise recommendation program 200 is installed and run in the application server 2, when the enterprise recommendation program 200 is running, the application server 2 pre-establishes an enterprise feature table, according to the enterprise feature table. A matching table of the enterprise supply and demand relationship is established. After the application server 2 obtains the demand information of the target enterprise, the matching enterprise is recommended to the target enterprise according to the demand information and the enterprise supply and demand relationship matching table, so that the application server 2 The company can actively obtain the demand information of the enterprise, and recommend matching companies to the enterprise according to the demand information of the enterprise. Achieving intelligent and efficient corporate recommendation services.

Referring to FIG. 2, it is a schematic diagram of an optional hardware architecture of the application server 2 in FIG. In this embodiment, the application server 2 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus. It is pointed out that FIG. 2 only shows the mobile terminal 1 with the components 11-13, but it should be understood that not all illustrated components are required to be implemented, and more or fewer components may be implemented instead.

The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), and a random access memory (RAM). , static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like. In some embodiments, the memory 11 may be an internal storage unit of the application server 2, such as a hard disk or memory of the application server 2. In other embodiments, the memory 11 may also be an external storage device of the application server 2, such as a plug-in hard disk equipped on the application server 2, a smart memory card (SMC), and a secure digital number. (Secure Digital, SD) card, flash card, etc. Of course, the memory 11 can also include both the internal storage unit of the application server 2 and its external storage device. In this embodiment, the memory 11 is generally used to store an operating system installed on the application server 2 and various types of application software, such as program codes of the enterprise recommendation program 200. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.

The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 12 is typically used to control the overall operation of the application server 2, such as performing control and processing related to data interaction or communication with the mobile terminal 1. In this embodiment, the processor 12 is configured to run program code or process data stored in the memory 11, such as running the enterprise recommendation program 200 and the like.

The network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the application server 2 and other electronic devices. In this embodiment, the network interface 13 is mainly used to connect the application server 2 to one or more mobile terminals 1 through the network 3, and the application server 2 and the one or more mobiles. A data transmission channel and a communication connection are established between the terminals 1.

So far, the application environment of the various embodiments of the present application and the hardware structure and functions of related devices have been described in detail. Hereinafter, various embodiments of the present application will be proposed based on the above-described application environment and related devices.

First, the present application proposes an enterprise recommendation program 200.

Referring to FIG. 3, it is a program module diagram of the first embodiment of the enterprise recommendation program 200 of the present application. In this embodiment, the enterprise recommendation program 200 may be divided into one or more modules, and the one or more modules are stored in the memory 11 and are composed of one or more processors (this embodiment) This is performed by the processor 12) to complete the application. For example, in FIG. 3, the industry recommendation program 200 can be divided into a first establishing module 201, a second establishing module 202, an obtaining module 203, and a recommending module 204. The program module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function, and is more suitable than the program to describe the execution process of the industry recommendation program 200 in the application server 2. The functions of each of the program modules 201-204 will be described in detail below.

The first establishing module 201 is configured to pre-establish an enterprise feature table in a database of the application server 2.

Generally, the manner of establishing the enterprise feature table in advance may be that the administrator imports through the database in the background of the application server 2.

The manner of establishing the enterprise feature table in advance may also be that the first establishing module 201 of the application server 2 establishes an enterprise feature table by using news information. The enterprise information information includes: a title of the information, an industry type, and an information content.

The second establishing module 202 is configured to establish, according to the enterprise feature table, each enterprise supply and demand relationship matching table.

Specifically, the enterprise supply and demand relationship matching table is established according to the information information of the enterprise feature table.

In this embodiment, in order to perform enterprise information matching more quickly, the enterprise may be classified according to the enterprise information, that is, the supply and demand relationship matching table further includes the category of the enterprise. For example: clothing industry, financial industry, manufacturing and so on. Each business category is not limited to only one category depending on the business situation. For example, company A can belong to both the financial industry and the electronics industry. For example, the enterprise supply and demand relationship matching table can be in the following form:

企业名称Company Name	企业类别Business category	供给supply	需求demand	............
AA	电子行业/金融行业Electronics Industry / Financial Industry	基金理财Fund management	IC元件IC component
BB	金融行业Financial sector	提供贷款Provide loans
CC	电子行业it	电子元器件供应商Electronic component supplier
DD	电子行业it	电子元件制造Electronic component manufacturing

The obtaining module 203 is configured to acquire demand information of the target enterprise. Specifically, the obtaining module 203 can obtain the demand information of the target enterprise through the mobile terminal 1. For example, the information about the target enterprise can be obtained through the operation of the client user of the mobile terminal 1 and the information of the client of the mobile terminal 1. The specific steps are as follows. Carry out the details.

The recommendation module 204 is configured to recommend a matching enterprise to the target enterprise according to the requirement information and the enterprise supply and demand relationship matching table.

Specifically, the recommendation module 204 generates a recommendation list to the target enterprise.

It can be seen from the above that the first establishing module 201 of the application server 2 pre-establishes an enterprise feature table, the second establishing module 202 establishes each enterprise supply and demand relationship matching table according to the enterprise feature table, and the obtaining module 203 acquires a target enterprise. Demand information. The recommendation module 204 recommends a matching enterprise for the target enterprise according to the demand information and the enterprise supply and demand relationship matching table. In this way, the application server 2 can actively acquire the demand information of the enterprise, and recommend the matching enterprise to the enterprise according to the demand information of the enterprise. Achieving intelligent and efficient corporate recommendation services.

Through the above-mentioned program modules 201-204, the enterprise recommendation program 200 proposed by the present application firstly establishes an enterprise feature table in advance, and secondly, establishes each enterprise supply and demand relationship matching table according to the enterprise feature table, and then, the application server 2 The mobile terminal 1 can obtain the demand information of the target enterprise, and finally, the matching enterprise is recommended for the target enterprise according to the demand information and the enterprise supply and demand relationship matching table. The disadvantages of the prior art enterprises spending a lot of time and manpower to actively search for the cooperative enterprise are avoided, the enterprise demand information is actively obtained, and the matching enterprise is recommended for the enterprise according to the demand information of the enterprise. Achieving intelligent and efficient corporate recommendation services.

Further, based on the above first embodiment of the enterprise recommendation procedure 200 of the present application, a second embodiment of the present application (shown in FIG. 3) is proposed. In this embodiment, the first establishing module 201 is further configured to:

Extracting the keyword of the enterprise; calculating the weight value of each keyword; sorting the weight value; selecting the sorted weight value according to the preset range, and using the keyword corresponding to the selected weight value as the enterprise label.

In an embodiment, the extracted enterprise keywords are composed of an enterprise industry, a supply and demand type, a city, a common time, and an information category. The extracted enterprise keywords can be obtained through the portal, news media, corporate information activities, etc. of the enterprise of the mobile terminal 1. In addition, enterprise introduction, enterprise supply and demand resource description, information article information, activity content description information, and news information can all be used as sources of information for extracting corporate keywords.

The first establishing module 201 assigns different weights to each keyword, and calculates a weight value of each keyword, and at the same time, sorts the weight values and selects the sorted weight values according to the preset range (for example, Select the keyword with the highest weight value, such as the top three keywords. Finally, the keyword corresponding to the selected weight value is used as the enterprise label. For example, the information of the A company is obtained through the information information of the mobile terminal 1, "electronic supplier", "requires financing of 10 million US dollars", "investment new factory", "IC chip supply", "employee 10000+", " The top 500 technology, "the number of patent applications is over one hundred", etc., the weight for the industry category is 4 points, the weight value of the supply and demand relationship is 3 points, and the others are 2 points. Therefore, the keyword rankings of A companies are: first place: "electronic supplier" 4 points; second place: "requires financing 10 million US dollars" 3 points; second place: "IC chip supply" 3 points; fourth Name: “Investment in new factory”, “Employees 10000+”, “Technology Top 500”, “Other patents and other keywords”. The first establishing module 201 of the application server 2 can set the top three keywords of the score to be the keywords of the A enterprise, so the keywords of the A enterprise are “electronic suppliers”, “need to raise 10 million US dollars” and “IC chip supply”. ". It should be noted that the extracted keywords are not limited to the top three keywords in the weight ranking, and can be set according to user needs.

Further, based on the above-described first embodiment of the enterprise recommendation procedure 200 of the present application, a third embodiment of the present application (shown in FIG. 3) is proposed. In this embodiment, the obtaining module 203 is further configured to:

Recording the operation behavior of the user of the target enterprise; recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and extracting the demand of the target enterprise according to the operation behavior and the label description information.

In an embodiment, the operation behavior of the user of the target enterprise on the client of the mobile terminal 1 is as follows:

(1) Which companies have been viewed by users of the target enterprise, which supply and demand have been viewed, and which information activities have been viewed;

(2) The user's collection/de-listed supply and demand list of the target company, the list of companies that are concerned/cancelled, the list of privately-subscribed users, and the published demand information.

For inputting keywords, selecting search conditions, browsing, collecting/uncollecting, paying attention/unfollowing, private messages, publishing supply and demand, etc., each operation behavior will have a frequency record, and the information content involved in each operation behavior will correspond. The corresponding label description. According to these operational behaviors and label descriptions, the demand information of the enterprise where the user is located can be extracted.

The obtaining module 203 is further configured to perform semantic analysis according to the information information to obtain a requirement change of the target enterprise. Further, the obtaining module 203 further changes the enterprise feature table and the enterprise supply and demand relationship matching table according to the information of the demand change.

For example, with the development of society, many companies are also undergoing transformation, and the direction of these enterprises may have changed. As a result, the needs of the enterprise will change. At present, news media has many resources and rich information. The news about enterprise transformation is generally disclosed through news. The application server 2 judges the change of the business direction and the development plan through the semantic analysis of the news sentences, and then provides the high-quality suppliers and suppliers in the industry.

Through the above embodiment, the obtaining module 203 records the operation behavior of the user of the target enterprise; records the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and according to the operation behavior and the The tag description extracts the demand information of the target enterprise. The obtaining module 203 further performs semantic analysis according to the information information, and acquires a requirement change of the target enterprise.

Through the above-mentioned program module 203, the enterprise recommendation program 200 proposed by the present application can timely know the change of the target enterprise according to the operation behavior of the user of the target enterprise and the semantic analysis of the information information, and then recommend a high-quality matching enterprise to the target enterprise. .

Further, based on the above-described first embodiment of the enterprise recommendation procedure 200 of the present application, a fourth embodiment (shown in FIG. 3) of the present application is proposed. In this embodiment, the recommendation module 204 is further configured to:

Selecting a plurality of preset parameters according to the entry of the enterprise supply and demand relationship matching table; assigning the plurality of preset parameters with different weights; calculating a matching degree of each enterprise with respect to the target enterprise; and according to the matching degree The calculated value is a list of recommendations generated by the target enterprise.

For example, the recommendation module 204 assigns different weight values to the industry, keywords, demand/supply, etc. of the enterprise. In order to save time and reduce the amount of calculation, the target enterprises that can limit the demand can only be selected from the enterprises that have the supply business, regardless of the enterprises with only the demand.

企业名称Company Name	企业类别Business category	供给supply	需求demand	专业度Professionalism
AA	电子行业/金融行业Electronics Industry / Financial Industry	基金理财Fund management	IC元件IC component
BB	金融行业Financial sector	提供贷款Provide loans
CC	电子行业it	电子元器件供应商Electronic component supplier		转销Resale
DD	电子行业it	电子元件制造Electronic component manufacturing		制造Manufacturing

For example, when the demand of the A company is an IC component, the B enterprise provides a loan, the C enterprise is an electronic component supplier, and the D enterprise is an electronic component manufacturing. According to the demand information of the A company, the preset parameters are set to “professionality” and “supply”, and the “professional” and “supply” weights are 0.6 and 0.4 respectively, and the “professional degree” is 3 points, and the manufacturing is 5 Points, the other is 1 point; the supplier in the "supply" is 2 points, the manufacturer is 3 points, and the others are 1 point. Among them, the score of A company can be calculated as 0.6*1+0.4*1=1, the score of B enterprise is 0.6*1+0.4*1=1, and the score of C enterprise is 0.6*3+0.4*2 =2.6, D company's score is 0.6 * 5 + 0.4 * 3 = 4.2.

Obviously, according to the demand information of the A company, it is possible to screen out two companies, C and D, and generate a recommendation form for the A company.

Through the above-mentioned program module 204, the enterprise recommendation program 200 proposed by the present application can also calculate the matching degree of each enterprise relative to the target enterprise according to the entries of the enterprise supply and demand relationship matching table, and sort according to the matching degree. Generate a list of recommendations for the target enterprise.

In addition, the present application also proposes a company recommendation method.

Referring to FIG. 4, it is a schematic diagram of an implementation process of the first embodiment of the enterprise recommendation method of the present application. In this embodiment, the order of execution of the steps in the flowchart shown in FIG. 4 may be changed according to different requirements, and some steps may be omitted.

In step S401, an enterprise feature table is pre-established in the database of the application server 2.

The manner of establishing the enterprise feature table in advance may also be that the application server 2 establishes an enterprise feature table through the news information. The enterprise information information includes: a title of the information, an industry type, and an information content. The specific steps will be described in detail in the second embodiment (see FIG. 5) of the company recommendation method of the present application.

Step S402, establishing, according to the enterprise feature table, a matching table of supply and demand relationships of each enterprise.

Specifically, the application server 2 establishes a supply and demand relationship matching table of the enterprise according to the information information of the enterprise feature table.

Step S403, obtaining demand information of the target enterprise.

Specifically, the application server 2 can obtain the demand information of the target enterprise through the mobile terminal 1. For example, the operation information of the client terminal of the mobile terminal 1 and the information information of the client of the mobile terminal 1 can be used to obtain the demand information of the target enterprise. The third embodiment (see FIG. 6) of the company recommendation method of the present application will be described in detail.

Step S404, recommend matching companies for the target enterprise according to the demand information and the enterprise supply and demand relationship matching table. Specifically, the application server 2 generates a recommendation list to the target enterprise.

It can be seen from the above that the application server 2 pre-establishes an enterprise feature table, and establishes a matching table of supply and demand relationships of the enterprises according to the enterprise feature table, and acquires the demand information of the target enterprise through the mobile terminal 1. The application server 2 recommends a matching enterprise for the target enterprise according to the demand information and the enterprise supply and demand relationship matching table. In this way, the application server 2 can actively acquire the demand information of the enterprise, and recommend the matching enterprise to the enterprise according to the demand information of the enterprise. Achieving intelligent and efficient corporate recommendation services.

Through the above steps S401-404, the enterprise recommendation method proposed by the present application firstly establishes an enterprise feature table in advance, and secondly, establishes each enterprise supply and demand relationship matching table according to the enterprise feature table, and then the application server 2 can The mobile terminal 1 acquires the demand information of the target enterprise, and finally, the matching enterprise is recommended for the target enterprise according to the demand information and the enterprise supply and demand relationship matching table. The disadvantages of the prior art enterprises spending a lot of time and manpower to actively search for the cooperative enterprise are avoided, the enterprise demand information is actively obtained, and the matching enterprise is recommended for the enterprise according to the demand information of the enterprise. Achieving intelligent and efficient corporate recommendation services.

As shown in FIG. 5, it is a schematic flowchart of the implementation of the second embodiment of the recommended method of the enterprise of the present application. In this embodiment, the step of pre-establishing the enterprise feature table specifically includes:

Step S501, extracting a business keyword.

Step S502, calculating a weight value of each keyword.

Step S503, sorting the weight values.

Step S504, selecting the sorted weight values according to the preset range.

In step S505, the keyword corresponding to the selected weight value is used as the enterprise label.

The application server 2 assigns different weights to each keyword, and calculates the weight value of each keyword. At the same time, the weight values are sorted, and the sorted weight values are selected according to the preset range (for example, Select the keyword with the highest weight value, such as the top three keywords. Finally, the keyword corresponding to the selected weight value is used as the enterprise label.

For example, the information of the A company is obtained through the information information of the mobile terminal 1, "electronic supplier", "requires financing of 10 million US dollars", "investment new factory", "IC chip supply", "employee 10000+", " The top 500 technology, "the number of patent applications is over one hundred", etc., the weight for the industry category is 4 points, the weight value of the supply and demand relationship is 3 points, and the others are 2 points. Therefore, the keyword rankings of A companies are: first place: "electronic supplier" 4 points; second place: "requires financing 10 million US dollars" 3 points; second place: "IC chip supply" 3 points; fourth Name: “Investment in new factory”, “Employees 10000+”, “Technology Top 500”, “Other patents and other keywords”. The first establishing module 201 of the application server 2 can set the top three keywords of the score to be the keywords of the A enterprise, so the keywords of the A enterprise are “electronic suppliers”, “need to raise 10 million US dollars” and “IC chip supply”. ". It should be noted that the extracted keywords are not limited to the top three keywords in the weight ranking, and can be set according to user needs.

Through the above steps S501-S505, the enterprise recommendation method proposed by the present application can acquire enterprise characteristic information by acquiring tags of each enterprise according to the information information.

As shown in FIG. 6, it is a schematic flowchart of the implementation of the third embodiment of the method for recommending the enterprise of the present application. In this embodiment, the step of acquiring the requirement information of the target enterprise includes:

Step S601, recording an operation behavior of a user of the target enterprise.

Step S602, recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior.

Step S603, extracting demand information of the target enterprise according to the operation behavior and the label description.

In this embodiment, the step of acquiring the requirement information of the target enterprise further includes:

In step S604, semantic analysis is performed according to the information information, and the demand change of the target enterprise is obtained. Further, the application server 2 further changes the enterprise feature table and the enterprise supply and demand relationship matching table according to the information of the demand change.

Through the above embodiment, the application server 2 records the operation behavior of the user of the target enterprise; records the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and according to the operation behavior and the The tag description extracts the demand information of the target enterprise. The application server 2 also performs semantic analysis based on the information information to obtain a change in the requirements of the target enterprise.

Through the above steps S601-S604, the enterprise recommendation method proposed by the present application can timely know the target enterprise's demand change according to the user's operation behavior and the semantic analysis of the information information of the target enterprise, and then recommend a high-quality matching enterprise to the target. enterprise.

As shown in FIG. 7, it is a schematic flowchart of the implementation of the fourth embodiment of the method for recommending the enterprise of the present application. In this embodiment, the step of recommending matching enterprises according to the requirement information and the enterprise supply and demand relationship matching table, specifically includes:

Step S701: Select a plurality of preset parameters according to the entry of the enterprise supply and demand relationship matching table.

Step S702, assigning the plurality of preset parameters with different weights.

In step S703, the matching degree of each enterprise with respect to the demand of the target enterprise is calculated.

Step S704, generating a recommendation list according to the calculated value of the matching degree.

For example, the application server 2 gives the enterprise's industry, keywords, demand/supply, and the like different weight values. In order to save time and reduce the amount of calculation, the target enterprises that can limit the demand can only be selected from the enterprises that have the supply business, regardless of the enterprises with only the demand.

Through the above steps S701-S704, the enterprise recommendation method proposed by the present application can also calculate the matching degree of each enterprise relative to the target enterprise according to the entry of the enterprise supply and demand relationship matching table, and according to the matching degree, The target company generates a list of recommendations.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

An enterprise recommendation method is applied to an application server, where the method includes:

Pre-establishing a corporate feature table;

Establishing a matching table of supply and demand relationships of each enterprise according to the enterprise characteristic table;

Obtain information on the needs of the target company; and

And matching the enterprise according to the demand information and the enterprise supply and demand relationship matching table for the target enterprise.
The enterprise recommendation method according to claim 1, wherein the step of establishing the enterprise feature table in advance comprises:

Extract corporate keywords;

And calculate the weight value of each keyword;

Sorting the weight values;

Selecting the sorted weight values according to the preset range, and

The keyword corresponding to the selected weight value is used as the enterprise label.
The enterprise recommendation method according to claim 1, wherein the step of acquiring the demand information of the target enterprise comprises:

Recording the operational behavior of the user of the target enterprise;

Recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and

Extracting the demand information of the target enterprise according to the operation behavior and the label description.
The enterprise recommendation method according to claim 2, wherein the step of acquiring the demand information of the target enterprise comprises:

Recording the operational behavior of the user of the target enterprise;

Recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and

Extracting the demand information of the target enterprise according to the operation behavior and the label description.
The method for recommending the enterprise according to claim 3, wherein the step of acquiring the demand information of the target enterprise further includes:

Semantic analysis based on the information information to obtain the demand change of the target enterprise.
The method for recommending the enterprise according to claim 4, wherein the step of acquiring the demand information of the target enterprise specifically includes:

Semantic analysis based on the information information to obtain the demand change of the target enterprise.
The enterprise recommendation method according to claim 3 or 4, wherein the step of recommending a matching enterprise for the target enterprise according to the demand information includes:

Selecting a plurality of preset parameters according to the entry of the enterprise supply and demand relationship matching table;

Assign multiple preset parameters with different weights;

Calculate the matching degree of each company's demand relative to the target company; and

And generating a recommendation list according to the calculated value of the matching degree.
An application server, comprising: a memory, a processor, and an enterprise recommendation program stored on the memory and executable on the processor, the enterprise recommendation program being executed by the processor The following steps are implemented:

Pre-establishing a corporate feature table;

Establishing a matching table of supply and demand relationships of each enterprise according to the enterprise characteristic table;

Obtain information on the needs of the target company; and

And matching the enterprise according to the demand information and the enterprise supply and demand relationship matching table for the target enterprise.
The application server according to claim 8, wherein the step of pre-establishing the enterprise feature table specifically includes:

Extract corporate keywords;

And calculate the weight value of each keyword;

Sorting the weight values;

Selecting the sorted weight values according to the preset range, and

The keyword corresponding to the selected weight value is used as the enterprise label.
The application server according to claim 8, wherein the step of acquiring the demand information of the target enterprise comprises:

Recording the operational behavior of the user of the target enterprise;

Recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and

Extracting the demand information of the target enterprise according to the operation behavior and the label description.
The application server according to claim 9, wherein the step of acquiring the demand information of the target enterprise comprises:

Recording the operational behavior of the user of the target enterprise;

Recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and

Extracting the demand information of the target enterprise according to the operation behavior and the label description.
The application server according to claim 10, wherein the step of acquiring the demand information of the target enterprise further includes:

Semantic analysis based on the information information to obtain the demand change of the target enterprise.
The application server according to claim 11, wherein the step of acquiring the demand information of the target enterprise further includes:

Semantic analysis based on the information information to obtain the demand change of the target enterprise.
The application server according to claim 10 or 11, wherein the step of recommending a matching enterprise for the target enterprise according to the requirement information comprises:

Selecting a plurality of preset parameters according to the entry of the enterprise supply and demand relationship matching table;

Assign multiple preset parameters with different weights;

Calculate the matching degree of each company's demand relative to the target company; and

And generating a recommendation list according to the calculated value of the matching degree.
A computer readable storage medium storing an enterprise recommendation program executable by at least one processor to cause the at least one processor to perform the following steps:

Pre-establishing a corporate feature table;

Establishing a matching table of supply and demand relationships of each enterprise according to the enterprise characteristic table;

Obtain information on the needs of the target company; and

And matching the enterprise according to the demand information and the enterprise supply and demand relationship matching table for the target enterprise.
The computer readable storage medium according to claim 15, wherein the step of pre-establishing the enterprise feature table comprises:

Extract corporate keywords;

And calculate the weight value of each keyword;

Sorting the weight values;

Selecting the sorted weight values according to the preset range, and

The keyword corresponding to the selected weight value is used as the enterprise label.
The computer readable storage medium according to claim 15, wherein the step of acquiring the demand information of the target enterprise comprises:

Recording the operational behavior of the user of the target enterprise;

Recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and

Extracting the demand information of the target enterprise according to the operation behavior and the label description.
The computer readable storage medium according to claim 16, wherein the step of acquiring the demand information of the target enterprise comprises:

Recording the operational behavior of the user of the target enterprise;

Recording the frequency of the operation behavior and the label description corresponding to the information content involved in the operation behavior; and

Extracting the demand information of the target enterprise according to the operation behavior and the label description.
The computer readable storage medium according to claim 17, wherein the step of acquiring the demand information of the target enterprise specifically includes:

Semantic analysis based on the information information to obtain the demand change of the target enterprise.
The computer readable storage medium according to claim 18, wherein the step of acquiring the demand information of the target enterprise further comprises:

Semantic analysis based on the information information to obtain the demand change of the target enterprise.
The computer readable storage medium according to claim 17 or claim 18, wherein the step of recommending a matching enterprise for the target enterprise according to the demand information comprises:

Selecting a plurality of preset parameters according to the entry of the enterprise supply and demand relationship matching table;

Assign multiple preset parameters with different weights;

Calculate the matching degree of each company's demand relative to the target company; and

And generating a recommendation list according to the calculated value of the matching degree.