CN113836379A - Intelligent recommendation method and system based on customer image - Google Patents
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Abstract
The application discloses an intelligent recommendation method and system based on customer images, and the method comprises the following steps: acquiring a plurality of client names input by a user; capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name; extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one; obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name; and acquiring other client names corresponding to the same keywords. Through the method and the device, the problems that in the prior art, the customer acquisition efficiency is low and the customer is possibly omitted due to the fact that the customer acquisition is carried out manually by means of sales personnel are solved, and therefore the accuracy and timeliness of the customer acquisition are improved.
Description
Technical Field
The application relates to the field of data processing, in particular to an intelligent recommendation method and system based on a client portrait.
Background
Suppliers of products or services typically rely on sales personnel to search for customers manually, which is inefficient and dependent on human experience.
The knowledge generally mastered by the salesperson is fixed, a large number of companies are newly registered every year along with the development of the society, or some old companies have new requirements, and the salesperson cannot master the new requirements in time, so that a lot of cooperation opportunities are lost.
Disclosure of Invention
The embodiment of the application provides an intelligent recommendation method and system based on customer images, and aims to at least solve the problems that in the prior art, customer obtaining efficiency is low and customers are likely to be missed due to the fact that customers are obtained manually by means of salesmen.
According to one aspect of the application, an intelligent recommendation method based on customer images is provided, and comprises the following steps: the method comprises the steps that a plurality of customer names input by a user are obtained, wherein the user provides goods and/or services for entities corresponding to the customer names; capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name; extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one; obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name; and acquiring other client names corresponding to the same keyword, wherein the other client names are client names except for the plurality of client names input by the user.
Further, the plurality of client names input by the user are client names in a predetermined service category, wherein the predetermined service category is a category of goods and/or services which can be provided by the user; the other client names are client names under the predetermined service category.
Further, the capturing the data corresponding to each client name from the network includes: acquiring a network address corresponding to each client name; and capturing the data corresponding to the client name from the network address corresponding to each client name.
Further, the network address includes at least one of: the website address corresponding to the customer name and the address for introducing and publicizing the entity corresponding to the customer name on the third-party platform.
Further, obtaining other client names corresponding to the same keyword includes: and retrieving other client names corresponding to the key system from the corresponding relation between the pre-stored key words and the client names, and displaying the retrieved other client names to the user.
According to another aspect of the present application, there is also provided a customer image-based intelligent recommendation system, including: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a plurality of customer names input by a user, and the user provides goods and/or services for entities corresponding to the customer names; the capturing module is used for capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name; the extraction module is used for extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one; the second obtaining module is used for obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name; and a third obtaining module, configured to obtain other client names corresponding to the same keyword, where the other client names are client names other than the multiple client names input by the user.
Further, the plurality of client names input by the user are client names in a predetermined service category, wherein the predetermined service category is a category of goods and/or services which can be provided by the user; the other client names are client names under the predetermined service category.
Further, the grasping module is configured to: acquiring a network address corresponding to each client name; and capturing the data corresponding to the client name from the network address corresponding to each client name.
Further, the network address includes at least one of: the website address corresponding to the customer name and the address for introducing and publicizing the entity corresponding to the customer name on the third-party platform.
Further, the third obtaining module is configured to: and retrieving other client names corresponding to the key system from the corresponding relation between the pre-stored key words and the client names, and displaying the retrieved other client names to the user.
In the embodiment of the application, a plurality of client names input by a user are acquired, wherein the user provides goods and/or services for entities corresponding to the client names; capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name; extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one; obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name; and acquiring other client names corresponding to the same keyword, wherein the other client names are client names except for the plurality of client names input by the user. Through the method and the device, the problems that in the prior art, the customer acquisition efficiency is low and the customer is possibly omitted due to the fact that the customer acquisition is carried out manually by means of sales personnel are solved, and therefore the accuracy and timeliness of the customer acquisition are improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a method for intelligent recommendation based on a customer image according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
In this embodiment, an intelligent recommendation method based on a client image is provided, and fig. 1 is a flowchart of the intelligent recommendation method based on the client image according to the embodiment of the present application, as shown in fig. 1, the flowchart includes the following steps:
step S102, a plurality of customer names input by a user are obtained, wherein the user provides goods and/or services for entities corresponding to the customer names;
step S104, capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name;
there are many ways to capture the data corresponding to each client name, for example, the network address corresponding to each client name can be obtained; and capturing the data corresponding to the client name from the network address corresponding to each client name. The network address may include at least one of: the website address corresponding to the customer name and the address for introducing and publicizing the entity corresponding to the customer name on the third-party platform.
Step S106, extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one;
in this step, the obtained data may be converted into a text, and the keywords extracted from the text may be used as keywords corresponding to the customer information corresponding to the customer name, where the customer information includes the customer name, a customer abbreviation, a company brand, a main product, and the like.
For example, in an alternative embodiment, the keywords may be extracted from the text in an artificial intelligence manner, and a machine learning model may be obtained by training multiple sets of training data, where each set of training data in the multiple sets of training data includes input data and output data, the input data is a section of text, and the output data is a keyword corresponding to the manually identified section of text. After training, the machine learning model can be used, the data in step S106 is converted into text and then input into the machine learning model, and the keywords output by the machine learning model are used as the keywords corresponding to the client name.
During training, a keyword group can be pre-selected and configured, each service type corresponds to one keyword group, the corresponding keyword group in the service type comprises a plurality of pre-configured keywords, and at least one keyword of the manual identification in the output data in the training data is selected from the keyword group. The training data obtained in this way is easier to converge when training.
Step S108, obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name;
a blacklist configured by the user may also be obtained, where a customer name in the blacklist is a customer for whom the user does not provide goods or services. And acquiring all blacklist client names in the blacklist, acquiring keywords corresponding to the blacklist client names, removing the keywords corresponding to the blacklist client names from the same keywords, and using the rest keywords after removing the keywords corresponding to the blacklist client names in the step S110 for retrieval.
Step S110, obtaining other client names corresponding to the same keyword, where the other client names are client names other than the plurality of client names input by the user.
In this step, another client name corresponding to the key system may be retrieved from a correspondence relationship between a pre-stored keyword and a client name, and the retrieved another client name may be displayed to the user.
And when a plurality of other client names are searched, sorting the plurality of other client names according to the priority, and displaying the sorted other client names to the user. The priority can be determined according to the number of the bids issued by the other client names to the predetermined service types in the predetermined time period, the more the number of the issued bids is, the higher the priority is, and the other client names with the largest number of the issued bids are preferentially displayed when the display is performed.
The priority may also be obtained by weighting according to a plurality of parameters, for example, calculating a weight corresponding to the bid inviting times according to the bid inviting times, obtaining a corresponding weight according to the scale of the entity enterprise corresponding to the other customer name, obtaining a corresponding weight according to the number of times of disputes occurring in the other customer name (the more dispute times in the weight, the lower the score of the weight), adding the obtained weights, and the highest priority with the higher score.
In another optional embodiment, the parameters and the weights corresponding to the parameters may be pre-configured by the user.
In the above step, the plurality of client names input by the user are client names in a predetermined service category, where the predetermined service category is a category of goods and/or services that can be provided by the user; the other client names are client names under the predetermined service category.
After the other customer names are displayed, the other customer names selected to be viewed by the user are recorded and marked. And after the user has actually transacted, if the other client names are other user names selected to be viewed by the user, recording keywords corresponding to the other user names viewed by the user, wherein the keywords are recommended to other users to use when the keywords are used for searching client names under the preset service category.
The method includes the steps that a machine learning model can be trained for each preset service type, the preset service types corresponding to a plurality of names input by a user are obtained firstly, then the machine learning model corresponding to the preset service type is found, a plurality of client names are input into the machine learning model corresponding to the preset service type, and keywords corresponding to the client names under the service type are output by the machine learning model. This is because some customers may have a need for multiple service categories, and through the alternative embodiment, keywords under the service category can be obtained, and a customer name corresponding to the service category can be found.
Through the steps, the problems that in the prior art, the customer obtaining efficiency is low and customers are possibly omitted due to the fact that the customers are obtained manually by salesmen are solved, and therefore the accuracy and timeliness of customer obtaining are improved.
In this embodiment, an electronic device is provided, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the method in the above embodiments.
The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
Such an apparatus or system is provided in this embodiment. The system is called an intelligent recommendation system based on customer images, and comprises: the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a plurality of customer names input by a user, and the user provides goods and/or services for entities corresponding to the customer names; the capturing module is used for capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name; the extraction module is used for extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the customer information comprises the customer name, a customer abbreviation, a company brand, a main product and the like, and the keywords are at least one; the second obtaining module is used for obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name; and a third obtaining module, configured to obtain other client names corresponding to the same keyword, where the other client names are client names other than the multiple client names input by the user.
The system or the apparatus is used for implementing the functions of the method in the foregoing embodiments, and each module in the system or the apparatus corresponds to each step in the method, which has been described in the method and is not described herein again.
For example, the plurality of client names input by the user are client names under a predetermined service category, wherein the predetermined service category is a category of goods and/or services that can be provided by the user; the other client names are client names under the predetermined service category.
For example, the grasping module is configured to: acquiring a network address corresponding to each client name; and capturing the data corresponding to the client name from the network address corresponding to each client name. Optionally, the third obtaining module is configured to: and retrieving other client names corresponding to the key system from the corresponding relation between the pre-stored key words and the client names, and displaying the retrieved other client names to the user.
The function provided in the present embodiment is referred to as a client expansion function, or as a portrait expansion function. The user enters a predetermined number of companies (e.g., five companies) and then matches the related companies in the background. And under the condition that the service types of the users are more, recommending related companies according to the service directions. And in the background learning process, learning is carried out according to the five companies, then a better company is recommended, and companies which the user wants to find and companies which the user dislikes can be added into the background in the learning process. This allows learning from bi-directional data.
The problem that in the prior art, the efficiency of obtaining the customers is low and the customers are possibly omitted due to the fact that the customers are obtained manually by the salespersons is solved through the embodiment, and therefore the accuracy and the timeliness of obtaining the customers are improved.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An intelligent recommendation method based on customer images is characterized by comprising the following steps:
the method comprises the steps that a plurality of customer names input by a user are obtained, wherein the user provides goods and/or services for entities corresponding to the customer names;
capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name;
extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one;
obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name;
and acquiring other client names corresponding to the same keyword, wherein the other client names are client names except for the plurality of client names input by the user.
2. The method of claim 1, wherein the plurality of client names input by the user are client names under a predetermined business category, wherein the predetermined business category is a category of goods and/or services that can be provided by the user; the other client names are client names under the predetermined service category.
3. The method of claim 1, wherein capturing the profile corresponding to each client name from the network comprises:
acquiring a network address corresponding to each client name;
and capturing the data corresponding to the client name from the network address corresponding to each client name.
4. The method of claim 3, wherein the network address comprises at least one of: the website address corresponding to the customer name and the address for introducing and publicizing the entity corresponding to the customer name on the third-party platform.
5. The method according to any one of claims 1 to 4, wherein obtaining other client names corresponding to the same keyword comprises:
and retrieving other client names corresponding to the key system from the corresponding relation between the pre-stored key words and the client names, and displaying the retrieved other client names to the user.
6. An intelligent recommendation system based on customer images, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a plurality of customer names input by a user, and the user provides goods and/or services for entities corresponding to the customer names;
the capturing module is used for capturing data corresponding to each client name in the plurality of client names from a network, wherein the data comprises news and/or introduction corresponding to the client name;
the extraction module is used for extracting keywords corresponding to the customer information corresponding to each customer name according to the data corresponding to each customer name, wherein the number of the keywords is at least one;
the second obtaining module is used for obtaining the same keywords corresponding to the plurality of client names, wherein the same keywords are keywords included in the keywords corresponding to each client name;
and a third obtaining module, configured to obtain other client names corresponding to the same keyword, where the other client names are client names other than the multiple client names input by the user.
7. The system according to claim 6, wherein the plurality of client names inputted by the user are client names under a predetermined service category, wherein the predetermined service category is a category of goods and/or services that can be provided by the user; the other client names are client names under the predetermined service category.
8. The system of claim 6, wherein the grasping module is to:
acquiring a network address corresponding to each client name;
and capturing the data corresponding to the client name from the network address corresponding to each client name.
9. The system of claim 8, wherein the network address comprises at least one of: the website address corresponding to the customer name and the address for introducing and publicizing the entity corresponding to the customer name on the third-party platform.
10. The system of any one of claims 6 to 9, wherein the third obtaining module is configured to:
and retrieving other client names corresponding to the key system from the corresponding relation between the pre-stored key words and the client names, and displaying the retrieved other client names to the user.
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| 黎伟健等: "大数据视角下的非结构化文本挖掘分析方法", 《新媒体研究》, pages 8 - 10 * |
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| CN113836379B (en) | 2023-08-25 |
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