CN109949090B - Client recommendation method and device, electronic equipment and medium - Google Patents
Client recommendation method and device, electronic equipment and medium Download PDFInfo
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- CN109949090B CN109949090B CN201910204714.7A CN201910204714A CN109949090B CN 109949090 B CN109949090 B CN 109949090B CN 201910204714 A CN201910204714 A CN 201910204714A CN 109949090 B CN109949090 B CN 109949090B
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Abstract
The invention provides a client recommendation method, a client recommendation device, electronic equipment and a medium. The client recommending method can extract a target label from a preconfigured label library, screen out clients containing the target label from the preconfigured client library to serve as clients to be recommended, calculate the association degree between the target label and each client to be recommended in the clients to be recommended to obtain each first association degree, further obtain events generated between existing clients and users, extract first characteristics of the existing clients, further determine the association degree between each client to be recommended and the users to obtain each second association degree, determine each target association degree according to the first association degree and the second association degree of each client to be recommended, sort each client to be recommended according to the target association degrees to obtain a queue, recommend clients according to the queue, and accordingly realize that under the condition of no manual operation, potential customers can be accurately acquired and intelligently recommended.
Description
Technical Field
The invention relates to the technical field of intelligent recommendation, in particular to a client recommendation method, a client recommendation device, electronic equipment and a medium.
Background
At present, a user acquires potential customers in a searching mode, and under the influence of current economic globalization, with the rapid increase of the number of enterprises, a manual searching mode cannot keep pace with the times, besides, workers with certain professional backgrounds or experiences need to manually search for enterprises related to own business, and therefore, the experience of the user is not facilitated.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, an electronic device, and a medium for recommending a customer, which can quickly and accurately acquire a potential customer and perform intelligent recommendation without human operation, and improve efficiency.
A method of customer recommendation, the method comprising:
extracting a target label from a pre-configured label library;
screening out the clients containing the target tags from a pre-configured client library to serve as clients to be recommended;
calculating the association degree between the target tag and each to-be-recommended client in the to-be-recommended clients to obtain a first association degree of each to-be-recommended client;
acquiring an event generated between an existing client and a user;
extracting a first feature of the existing customer;
determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended;
determining the target association degree of each customer to be recommended according to the first association degree and the second association degree of each customer to be recommended;
sequencing each client to be recommended according to the target relevance to obtain a queue;
and recommending the clients according to the queue.
According to a preferred embodiment of the present invention, before extracting the target tag from the preconfigured tag library, the method further comprises:
adopting a web crawler technology and/or acquiring customer information from an official website of a customer;
determining a label according to the customer information;
and generating a label library according to the label.
According to a preferred embodiment of the present invention, the calculating the association degree between the target tag and each of the clients to be recommended to obtain the first association degree of each of the clients to be recommended includes:
determining all labels of each customer to be recommended;
calculating the ratio of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended;
calculating the proportion of the to-be-recommended customers containing the target labels in the customer base to obtain a second ratio of each to-be-recommended customer;
multiplying the first ratio and the second ratio of each customer to be recommended to obtain a first numerical value of each customer to be recommended;
and squaring each first numerical value to obtain a first relevance of each client to be recommended.
According to the preferred embodiment of the present invention, the acquiring the event generated between the existing client and the user includes one or more of the following combinations:
acquiring events related to the existing clients from a mailbox; and/or
And acquiring events related to the existing clients from a client relationship management system.
According to a preferred embodiment of the present invention, the determining, according to the event and the first feature, a degree of association between each to-be-recommended client and the user, and obtaining a second degree of association of each to-be-recommended client includes:
calculating a second value for each event based on the sigmod function;
calculating the weight of each event;
multiplying the second numerical value and the weight of each event to obtain a third numerical value of each event;
adding all third numerical values in the existing clients to obtain a first compactness of the existing clients and the users;
calculating the closeness of the first characteristic and each client to be recommended by adopting a TF-IDF method to obtain a second closeness of the existing client and each client to be recommended;
and multiplying the first closeness by the second closeness of each client to be recommended, and determining the association degree between each client to be recommended and the user to obtain the second association degree of each client to be recommended.
According to the preferred embodiment of the present invention, before each to-be-recommended client is sorted according to the target relevance to obtain a queue, the method further includes:
acquiring an existing client;
and when detecting that the existing client is contained in the recommendation client, deleting the existing client from the clients to be recommended.
According to a preferred embodiment of the present invention, after making the customer recommendation according to the queue, the method further comprises:
acquiring feedback information every preset time;
and updating the queue according to the feedback information.
A customer recommendation device, the device comprising:
the extraction unit is used for extracting a target label from a pre-configured label library;
the screening unit is used for screening out the clients containing the target labels from a pre-configured client library to serve as clients to be recommended;
the calculation unit is used for calculating the association degree between the target tag and each to-be-recommended client in the to-be-recommended clients to obtain a first association degree of each to-be-recommended client;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring an event generated between an existing client and a user;
the extracting unit is further used for extracting a first feature of the existing client;
the determining unit is used for determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended;
the determining unit is further used for determining the target relevance of each customer to be recommended according to the first relevance and the second relevance of each customer to be recommended;
the sequencing unit is used for sequencing each client to be recommended according to the target relevance degree to obtain a queue;
and the recommending unit is used for recommending the client according to the queue.
According to the preferred embodiment of the present invention, the obtaining unit is further configured to obtain the client information by using a web crawler technology and/or from an official website of the client before extracting the target tag from the pre-configured tag library;
the determining unit is further used for determining a label according to the customer information;
the device further comprises:
and the generating unit is used for generating a label library according to the labels.
According to a preferred embodiment of the present invention, the computing unit is specifically configured to:
determining all labels of each customer to be recommended;
calculating the ratio of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended;
calculating the proportion of the to-be-recommended customers containing the target labels in the customer base to obtain a second ratio of each to-be-recommended customer;
multiplying the first ratio and the second ratio of each customer to be recommended to obtain a first numerical value of each customer to be recommended;
and squaring each first numerical value to obtain a first relevance of each client to be recommended.
According to a preferred embodiment of the present invention, the acquiring unit acquires an event generated between an existing client and a user, and the acquiring unit includes one or more of the following combinations:
acquiring events related to the existing clients from a mailbox; and/or
And acquiring events related to the existing clients from a client relationship management system.
According to a preferred embodiment of the present invention, the determining unit determines, according to the event and the first feature, a degree of association between each to-be-recommended client and the user, and obtaining the second degree of association of each to-be-recommended client includes:
calculating a second value for each event based on the sigmod function;
calculating the weight of each event;
multiplying the second numerical value and the weight of each event to obtain a third numerical value of each event;
adding all third numerical values in the existing clients to obtain a first compactness of the existing clients and the users;
calculating the closeness of the first characteristic and each client to be recommended by adopting a TF-IDF method to obtain a second closeness of the existing client and each client to be recommended;
and multiplying the first closeness by the second closeness of each client to be recommended, and determining the association degree between each client to be recommended and the user to obtain the second association degree of each client to be recommended.
According to the preferred embodiment of the present invention, the obtaining unit is further configured to obtain the existing clients before the clients to be recommended are sorted according to the target association degree to obtain the queue;
the device further comprises:
and the deleting unit is used for deleting the existing client from the clients to be recommended when the existing client is detected to be contained in the recommended clients.
According to a preferred embodiment of the present invention, the obtaining unit is further configured to obtain feedback information every preset time after performing the customer recommendation according to the queue;
the device further comprises:
and the updating unit is used for updating the queue according to the feedback information.
An electronic device, the electronic device comprising:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the customer recommendation method.
A computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executable by a processor in an electronic device to implement the customer recommendation method.
According to the technical scheme, the target label can be extracted from the preconfigured label library, the clients containing the target label are screened from the preconfigured client library to be the clients to be recommended, the association degree between the target label and each client to be recommended in the clients to be recommended is calculated to obtain the first association degree of each client to be recommended, the event generated between the existing client and the user is further obtained, the first characteristic of the existing client is extracted, the association degree between each client to be recommended and the user is determined according to the event and the first characteristic to obtain the second association degree of each client to be recommended, the target association degree of each client to be recommended is determined according to the first association degree and the second association degree of each client to be recommended, and each client to be recommended is sorted according to the target association degrees, and obtaining the queue, and recommending the client according to the queue, so that the potential client can be quickly and accurately obtained and intelligently recommended without manual operation, and the efficiency is improved.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the client recommendation method of the present invention.
FIG. 2 is a functional block diagram of a preferred embodiment of the client recommendation device of the present invention.
FIG. 3 is a schematic structural diagram of an electronic device implementing a preferred embodiment of the method for client recommendation according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a preferred embodiment of the client recommendation method of the present invention. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The customer recommendation method is applied to one or more electronic devices, which are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), an intelligent wearable device, and the like.
The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.
The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
And S10, extracting the target label from the pre-configured label library.
Wherein at least one tag is stored in the tag library.
In at least one embodiment of the invention, prior to extracting the target tag from the preconfigured tag library, the method further comprises:
the electronic equipment adopts a web crawler technology and/or acquires client information from an official website of a client, determines a label according to the client information, and further generates a label library according to the label.
Specifically, the web crawler technology (web crawler) is a technology for capturing programs or scripts on a network according to a certain configuration rule, and through the web crawler technology, the electronic device can collect all contents contained in all accessible pages, so as to obtain data of these websites, for example: the business, contact, turnover, etc. of the customer. Functionally, the web crawler technology is generally divided into three parts, namely data acquisition, data processing and data storage. Further, the web crawler technology can download web page data and provide a data source for a search engine system.
Through the implementation mode, the electronic equipment can acquire the customer information more comprehensively so as to establish a better customer information basis.
In at least one embodiment of the invention, the tag is determined based on the customer information. The tag is extracted from the customer information by the electronic device by applying a Natural Language Processing (NLP) technology.
For example: the tag may include: LED, automation equipment, chip.
Further, natural language processing is a field in which computer science, artificial intelligence, linguistics focus on the interaction between computers and human (natural) language. Current NLP algorithms are based on machine learning, in particular statistical machine learning.
Through the embodiment, the electronic equipment can quickly and accurately determine the label from the customer information.
In at least one embodiment of the invention, the electronic device extracts the target tag from a preconfigured tag library.
For example: the target tag may be an LED in the tag.
Wherein, the extraction mode is not limited as long as the extraction effect can be achieved.
And S11, screening out the clients containing the target labels from a pre-configured client library to serve as clients to be recommended.
Wherein at least one customer with a tag is stored in the customer base.
In at least one embodiment of the invention, before screening customers containing the target tag from a preconfigured customer base, the method further comprises:
and establishing a client portrait according to the client information, and further generating a client library by the electronic equipment according to the client portrait.
In at least one embodiment of the present invention, the screening out the clients containing the target tag from the pre-configured client library as the clients to be recommended includes:
when the fact that the client library comprises the client containing the target label is detected, the electronic equipment extracts the client to obtain the client to be recommended.
Through the implementation mode, the to-be-recommended clients can be quickly screened out from the client library without manual operation, and the efficiency is improved.
S12, calculating the association degree between the target label and each to-be-recommended client in the to-be-recommended clients to obtain a first association degree of each to-be-recommended client.
In at least one embodiment of the present invention, the calculating the association degree between the target tag and each of the clients to be recommended to obtain the first association degree of each of the clients to be recommended includes:
the electronic equipment determines all labels of each customer to be recommended, further calculates the ratio of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended, calculates the ratio of the customer to be recommended containing the target label in the customer base to obtain a second ratio of each customer to be recommended, multiplies the first ratio and the second ratio of each customer to be recommended to obtain a first value of each customer to be recommended, and squares each first value to obtain a first degree of association of each customer to be recommended.
For example: the known client library comprises 10000 enterprises, four enterprises with target labels of LEDs are respectively a client A to be recommended, a client B to be recommended, a client C to be recommended and a client D to be recommended, wherein the client A to be recommended comprises 1 label, the client B to be recommended comprises 4 labels, the client C to be recommended comprises 100 labels, and the client D to be recommended comprises 400 labels, so that the first ratio of the client A to be recommended is 1/10000, the first ratio of the client B to be recommended is 1/2500, the first ratio of the client C to be recommended is 1/100, the first ratio of the client D to be recommended is 1/25, the second ratios of the client A, the client B to be recommended, the client C to be recommended are 1/2500, the first value of the client A to be recommended is 1/25000000, the first value of the client B to be recommended is 1/6250000, The first value of the customer B to be recommended is 1/250000, and the first value of the customer D to be recommended is 1/62500, then the first degree of association of the customer A to be recommended is 1/5000, the first degree of association of the customer B to be recommended is 1/2500, the first degree of association of the customer C to be recommended is 1/500, and the first degree of association of the customer D to be recommended is 1/50.
Through the embodiment, the first association degree of each client to be recommended can be rapidly and accurately calculated.
And S13, acquiring the event generated between the existing client and the user.
In at least one embodiment of the invention, the event is generated between the existing customer and the user, by which the existing customer and the user can be associated. Specifically, the events include, but are not limited to: the customer to be recommended prepays money, the number of times of cooperation is mentioned in the mail, and the like.
In at least one embodiment of the present invention, the acquiring the event generated between the existing client and the user includes, but is not limited to, one or more of the following combinations:
(1) and acquiring the event related to the existing client from the mailbox.
The electronic equipment acquires a first mailbox address of the existing client, and when the mailbox address of a sender in the mail is detected to be the first mailbox address or the mailbox address of a receiver is detected to be the first mailbox address, the electronic equipment acquires the mail, and further, the electronic equipment acquires an event in the mail.
For example: the event may include an event in the mail that mentions collaboration.
(2) And acquiring events related to the existing clients from a client relationship management system.
The customer relationship management system stores customer information, such as: customer name, turnover.
When the existing customer is detected to exist in the customer relationship management system, the electronic equipment acquires the event of the existing customer in the customer relationship management system.
For example: the event comprises a percentage of the amount the existing customer has paid in advance.
Of course, in other embodiments, the electronic device may obtain the event in other manners as long as it is legal and reasonable, and the present invention is not limited thereto.
And S14, extracting the first characteristics of the existing clients.
Wherein the first characteristic comprises the size of the enterprise, the main product of the enterprise and the like. In at least one embodiment of the invention, the electronic device extracts the first feature from the existing customer's official website.
Through the embodiment, the first characteristic of the existing client can be accurately extracted.
S15, determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended.
In at least one embodiment of the present invention, the determining, according to the event and the first feature, a degree of association between each to-be-recommended client and the user, and obtaining a second degree of association of each to-be-recommended client includes:
based on a sigmod function, the electronic equipment calculates a second numerical value of each event, calculates the weight of each event, multiplies the second numerical value of each event with the weight to obtain a third numerical value of each event, adds all the third numerical values in the existing clients to obtain a first closeness between the existing clients and the users, further calculates the closeness between the first characteristic and each client to be recommended by adopting a TF-IDF method to obtain a second closeness between the existing clients and each client to be recommended, multiplies the first closeness with the second closeness of each client to be recommended, determines the degree of association between each client to be recommended and the users, and obtains a second degree of association of each client to be recommended.
Through the embodiment, the association degree between each client to be recommended and the user can be automatically determined, so that the trouble caused by calculating the second association degree is reduced, and the accuracy is high.
S16, determining the target relevance of each customer to be recommended according to the first relevance and the second relevance of each customer to be recommended.
In at least one embodiment of the present invention, the determining the target relevance degree of each to-be-recommended client according to the first relevance degree and the second relevance degree of each to-be-recommended client includes:
and the electronic equipment multiplies the first relevance degree and the second relevance degree of each customer to be recommended to obtain the target relevance degree of each customer to be recommended.
Through the embodiment, the electronic equipment can determine the target relevance of each client to be recommended, so that the clients to be recommended can be sequenced regularly and subsequently.
And S17, sequencing each client to be recommended according to the target relevance to obtain a queue.
In at least one embodiment of the present invention, before each to-be-recommended client is sorted according to the target relevance to obtain a queue, the method further includes:
and the electronic equipment acquires the existing client and deletes the existing client when detecting that the recommended client contains the existing client.
Through the implementation mode, the existing clients can be filtered, so that the occupation of the memory of the electronic equipment is reduced, and the time for eliminating the existing clients is saved.
In at least one embodiment of the present invention, the sorting each to-be-recommended client according to the target relevance includes:
the electronic equipment sorts each client to be recommended from high to low or from low to high according to each target relevance degree.
Through the embodiment, preferential recommendation is favorably carried out on the clients to be recommended with high target association degree.
And S18, recommending clients according to the queue.
In at least one embodiment of the invention, after making the customer recommendation based on the queue, the method further comprises:
and the electronic equipment acquires feedback information every preset time, and further updates the queue according to the feedback information.
The value of the preset time may be configured in a user-defined manner, for example, 24 hours, which is not limited in the present invention.
The feedback information includes, but is not limited to: uninteresting clients, interested clients, etc.
Specifically, when the feedback information is a customer who is not interested, and the number of times the electronic device detects that the customer who is not interested appears exceeds a preset number of times, the electronic device deletes the customer who is not interested in the queue.
When the feedback information is the interested customer, marking the interested customer, and further arranging the interested customer at the first position or the last position of the queue by the electronic equipment.
The value of the preset number of times can be configured in a user-defined manner, for example, 10 times, and the present invention is not limited.
Through the implementation mode, the sequence of the to-be-recommended clients in the queue can be adjusted according to the preference of the user, and the requirements of different users are met.
According to the technical scheme, the target label can be extracted from the preconfigured label library, the clients containing the target label are screened from the preconfigured client library to be the clients to be recommended, the association degree between the target label and each client to be recommended in the clients to be recommended is calculated to obtain the first association degree of each client to be recommended, the event generated between the existing client and the user is further obtained, the first characteristic of the existing client is extracted, the association degree between each client to be recommended and the user is determined according to the event and the first characteristic to obtain the second association degree of each client to be recommended, the target association degree of each client to be recommended is determined according to the first association degree and the second association degree of each client to be recommended, and each client to be recommended is sorted according to the target association degrees, and obtaining the queue, and recommending the client according to the queue, so that the potential client can be quickly and accurately obtained and intelligently recommended without manual operation, and the efficiency is improved.
Fig. 2 is a functional block diagram of a preferred embodiment of the client recommendation device according to the invention. The client recommendation device 11 includes an extraction unit 110, a filtering unit 111, a calculation unit 112, an acquisition unit 113, a determination unit 114, a sorting unit 115, a recommendation unit 116, a generation unit 117, a deletion unit 118, an update unit 119, a creation unit 120, and an annotation unit 121. The module/unit referred to in the present invention refers to a series of computer program segments that can be executed by the processor 13 and that can perform a fixed function, and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
The extracting unit 110 extracts a target tag from a pre-configured tag library.
Wherein at least one tag is stored in the tag library.
In at least one embodiment of the present invention, before the extracting unit 110 extracts the target tag from the pre-configured tag library, the method further includes:
the obtaining unit 113 obtains the client information from the client's official website by using web crawler technology, further, the determining unit 114 determines the label according to the client information, and further, the generating unit 117 generates the label library according to the label.
Specifically, the web crawler technology (web crawler) is a technology for capturing programs or scripts on the web according to a certain configuration rule, and through the web crawler technology, the obtaining unit 113 may collect all contents included in all accessible pages, so as to obtain data of these websites, for example: the business, contact, turnover, etc. of the customer. Functionally, the web crawler technology is generally divided into three parts, namely data acquisition, data processing and data storage. Further, the web crawler technology can download web page data and provide a data source for a search engine system.
With the above embodiment, the obtaining unit 113 can obtain the customer information more comprehensively so as to establish a better customer information basis.
In at least one embodiment of the invention, the determining unit 114 determines the tag based on the customer information. Wherein, the label is extracted from the customer information by applying Natural Language Processing (NLP) technology.
For example: the tag may include: LED, automation equipment, chip.
Further, natural language processing is a field in which computer science, artificial intelligence, linguistics focus on the interaction between computers and human (natural) language. Current NLP algorithms are based on machine learning, in particular statistical machine learning.
With the above embodiment, the determination unit 114 can quickly and accurately determine the tag from the customer information.
In at least one embodiment of the present invention, the extracting unit 110 extracts the target tag from a pre-configured tag library.
For example: the target tag may be an LED in the tag.
The extraction unit 110 is not limited in the manner of extraction as long as the extraction effect can be achieved.
The screening unit 111 screens out the clients containing the target tags from a pre-configured client library as clients to be recommended.
Wherein at least one customer with a tag is stored in the customer base.
In at least one embodiment of the present invention, before the screening unit 111 screens out the customers containing the target tags from the pre-configured customer base, the method further comprises:
based on the customer information, the creation unit 120 creates a customer representation, and further, the generation unit 117 generates a customer base based on the customer representation.
In at least one embodiment of the present invention, the screening unit 111 screens the clients containing the target tags from a pre-configured client library, and the screening as the clients to be recommended includes:
when it is detected that the client library includes the client including the target tag, the screening unit 111 extracts the client to obtain the client to be recommended.
Through the implementation mode, the to-be-recommended clients can be quickly screened out from the client library without manual operation, and the efficiency is improved.
The calculating unit 112 calculates the association degree between the target tag and each of the clients to be recommended, to obtain a first association degree of each client to be recommended.
In at least one embodiment of the present invention, the calculating unit 112 calculates a degree of association between the target tag and each of the clients to be recommended, and obtaining a first degree of association of each of the clients to be recommended includes:
the calculating unit 112 determines all labels of each customer to be recommended, further calculates the proportion of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended, calculates the proportion of the customer to be recommended containing the target label in the customer base to obtain a second ratio of each customer to be recommended, and further, the calculating unit 112 multiplies the first ratio and the second ratio of each customer to be recommended to obtain a first value of each customer to be recommended, and squares each first value to obtain a first degree of association of each customer to be recommended.
For example: the known client library comprises 10000 enterprises, four enterprises with target labels of LEDs are respectively a client A to be recommended, a client B to be recommended, a client C to be recommended and a client D to be recommended, wherein the client A to be recommended comprises 1 label, the client B to be recommended comprises 4 labels, the client C to be recommended comprises 100 labels, and the client D to be recommended comprises 400 labels, so that the first ratio of the client A to be recommended is 1/10000, the first ratio of the client B to be recommended is 1/2500, the first ratio of the client C to be recommended is 1/100, the first ratio of the client D to be recommended is 1/25, the second ratios of the client A, the client B to be recommended, the client C to be recommended are 1/2500, the first value of the client A to be recommended is 1/25000000, the first value of the client B to be recommended is 1/6250000, The first value of the customer B to be recommended is 1/250000, and the first value of the customer D to be recommended is 1/62500, then the first degree of association of the customer A to be recommended is 1/5000, the first degree of association of the customer B to be recommended is 1/2500, the first degree of association of the customer C to be recommended is 1/500, and the first degree of association of the customer D to be recommended is 1/50.
Through the above embodiment, the calculating unit 112 can quickly and accurately calculate the first association degree of each customer to be recommended.
The acquisition unit 113 acquires an event generated between an existing client and a user.
In at least one embodiment of the invention, the event is generated between the existing customer and a user, by which the existing customer and the user can be associated. Specifically, the events include, but are not limited to: the customer to be recommended prepays money, the number of times of cooperation is mentioned in the mail, and the like.
In at least one embodiment of the present invention, the acquiring unit 113 acquires an event generated between an existing client and a user, including, but not limited to, one or more of the following:
(1) the obtaining unit 113 obtains the event related to the existing client from the mailbox.
The obtaining unit 113 obtains a first mailbox address of the existing client, when it is detected that a mailbox address of a sender in a mail is the first mailbox address or a mailbox address of a recipient is the first mailbox address, the obtaining unit 113 obtains the mail, and further, the obtaining unit 113 obtains an event in the mail.
For example: the event may include an event in the mail that mentions collaboration.
(2) The acquisition unit 113 acquires an event related to the existing customer from the customer relationship management system.
The customer relationship management system stores customer information, such as: customer name, turnover.
When detecting that the existing customer exists in the customer relationship management system, the obtaining unit 113 obtains an event of the existing customer in the customer relationship management system.
For example: the event comprises a percentage of the amount the existing customer has paid in advance.
Of course, in other embodiments, the obtaining unit 113 may obtain the event in other manners as long as it is legal and reasonable, and the present invention is not limited thereto.
The extracting unit 110 extracts a first feature of the existing customer.
Wherein the first characteristic comprises the size of the enterprise, the main product of the enterprise and the like. In at least one embodiment of the present invention, the extracting unit 110 extracts the first feature from the existing client's official website.
With the above embodiment, the extracting unit 110 can accurately extract the first feature of the existing client.
According to the event and the first feature, the determining unit 114 determines the association degree between each to-be-recommended client and the user, so as to obtain a second association degree of each to-be-recommended client.
In at least one embodiment of the present invention, the determining unit 114 determines, according to the event and the first feature, a degree of association between each client to be recommended and the user, and obtaining the second degree of association of each client to be recommended includes:
based on a sigmod function, the determining unit 114 calculates a second numerical value of each event, calculates a weight of each event, multiplies the second numerical value of each event with the weight to obtain a third numerical value of each event, adds all the third numerical values in the existing clients to obtain a first affinity of the existing clients and the users, further, the determining unit 114 calculates the affinity of the first feature with each client to be recommended by using a TF-IDF method to obtain a second affinity of the existing clients and each client to be recommended, multiplies the first affinity with the second affinity of each client to be recommended to determine an association between each client to be recommended and the users, and obtains a second association of each client to be recommended.
With the above embodiment, the determining unit 114 can automatically determine the association degree between each to-be-recommended client and the user, which not only reduces the trouble caused by calculating the second association degree, but also has high precision.
The determining unit 114 determines the target relevance of each to-be-recommended client according to the first relevance and the second relevance of each to-be-recommended client.
In at least one embodiment of the present invention, the determining unit 114 determines the target relevance degree of each to-be-recommended client according to the first relevance degree and the second relevance degree of each to-be-recommended client, where the determining unit includes:
the determining unit 114 multiplies the first relevance degree and the second relevance degree of each customer to be recommended to obtain a target relevance degree of each customer to be recommended.
With the above embodiment, the determining unit 114 can determine the target relevance of each to-be-recommended client, so as to order the to-be-recommended clients regularly and subsequently.
The sorting unit 115 sorts each to-be-recommended client according to the target relevance degree to obtain a queue.
In at least one embodiment of the present invention, before the sorting unit 115 sorts each to-be-recommended client according to the target relevance degree to obtain a queue, the method further includes:
the acquisition unit 113 acquires an existing customer, and the deletion unit 118 deletes the existing customer when detecting that the existing customer is included in the recommended customer.
Through the implementation mode, the existing clients can be filtered, the occupation of the memory of the electronic equipment is reduced, and the time for eliminating the existing clients is saved.
In at least one embodiment of the present invention, the sorting unit 115 sorts each to-be-recommended client according to the target relevance degree, including:
the sorting unit 115 sorts each client to be recommended from high to low or from low to high according to each target relevance.
Through the embodiment, preferential recommendation is favorably carried out on the clients to be recommended with high target association degree.
The recommendation unit 116 makes a customer recommendation based on the queue.
In at least one embodiment of the present invention, after the recommending unit 116 makes a customer recommendation according to the queue, the method further includes:
at preset intervals, the obtaining unit 113 obtains feedback information, and further, the updating unit 119 updates the queue according to the feedback information.
The value of the preset time may be configured in a user-defined manner, for example, 24 hours, which is not limited in the present invention.
The feedback information includes, but is not limited to: uninteresting clients, interested clients, etc.
Specifically, when the feedback information is a client who is not interested, and the number of times of detecting that the client who is not interested appears exceeds a preset number of times, the deleting unit 118 deletes the client who is not interested in the queue.
When the feedback information is the interested client, the labeling unit 121 labels the interested client, and further, the client recommending apparatus 11 arranges the interested client at the first or the last of the queue.
The value of the preset number of times can be configured in a user-defined manner, for example, 10 times, and the present invention is not limited.
Through the implementation mode, the sequence of the to-be-recommended clients in the queue can be adjusted according to the preference of the user, and the requirements of different users are met.
According to the technical scheme, the target label can be extracted from the preconfigured label library, the clients containing the target label are screened from the preconfigured client library to be the clients to be recommended, the association degree between the target label and each client to be recommended in the clients to be recommended is calculated to obtain the first association degree of each client to be recommended, the event generated between the existing client and the user is further obtained, the first characteristic of the existing client is extracted, the association degree between each client to be recommended and the user is determined according to the event and the first characteristic to obtain the second association degree of each client to be recommended, the target association degree of each client to be recommended is determined according to the first association degree and the second association degree of each client to be recommended, and each client to be recommended is sorted according to the target association degrees, and obtaining the queue, and recommending the client according to the queue, so that the potential client can be quickly and accurately obtained and intelligently recommended without manual operation, and the efficiency is improved.
Fig. 3 is a schematic structural diagram of an electronic device implementing a preferred embodiment of the method for recommending clients according to the present invention.
The electronic device 1 is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The electronic device 1 may also be, but not limited to, any electronic product that can perform human-computer interaction with a user through a keyboard, a mouse, a remote controller, a touch panel, or a voice control device, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an Internet Protocol Television (IPTV), an intelligent wearable device, and the like.
The electronic device 1 may also be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices.
The Network where the electronic device 1 is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
In one embodiment of the present invention, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and a computer program, such as a customer recommendation program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by a person skilled in the art that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and that it may comprise more or less components than shown, or some components may be combined, or different components, e.g. the electronic device 1 may further comprise an input output device, a network access device, a bus, etc.
The Processor 13 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The processor 13 is an operation core and a control center of the electronic device 1, and is connected to each part of the whole electronic device 1 by various interfaces and lines, and executes an operating system of the electronic device 1 and various installed application programs, program codes, and the like.
The processor 13 executes an operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps in the above-mentioned respective embodiments of the customer recommendation method, such as steps S10, S11, S12, S13, S14, S15, S16, S17, S18 shown in fig. 1.
Alternatively, the processor 13, when executing the computer program, implements the functions of the modules/units in the above device embodiments, for example: extracting a target label from a pre-configured label library; screening out the clients containing the target tags from a pre-configured client library to serve as clients to be recommended; calculating the association degree between the target tag and each to-be-recommended client in the to-be-recommended clients to obtain a first association degree of each to-be-recommended client; acquiring an event generated between an existing client and a user; extracting a first feature of the existing customer; determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended; determining the target association degree of each customer to be recommended according to the first association degree and the second association degree of each customer to be recommended; sequencing each client to be recommended according to the target relevance to obtain a queue; and recommending the clients according to the queue.
Illustratively, the computer program may be divided into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the electronic device 1. For example, the computer program may be divided into an extraction unit 110, a filtering unit 111, a calculation unit 112, an acquisition unit 113, a determination unit 114, a sorting unit 115, a recommendation unit 116, a generation unit 117, a deletion unit 118, an update unit 119, a creation unit 120, and an annotation unit 121. The memory 12 can be used for storing the computer programs and/or modules, and the processor 13 implements various functions of the electronic device 1 by running or executing the computer programs and/or modules stored in the memory 12 and calling data stored in the memory 12. The memory 12 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. Further, the memory 12 may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The memory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, the Memory 12 may be a circuit having a Memory function without any physical form In the integrated circuit, such as a RAM (Random-Access Memory), a FIFO (First In First Out), and the like. Alternatively, the memory 12 may be a memory in a physical form, such as a memory stick, a TF Card (Trans-flash Card), or the like.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
With reference to fig. 1, the memory 12 of the electronic device 1 stores a plurality of instructions to implement a customer recommendation method, and the processor 13 executes the plurality of instructions to implement:
extracting a target label from a pre-configured label library; screening out the clients containing the target tags from a pre-configured client library to serve as clients to be recommended; calculating the association degree between the target tag and each to-be-recommended client in the to-be-recommended clients to obtain a first association degree of each to-be-recommended client; acquiring an event generated between an existing client and a user; extracting a first feature of the existing customer; determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended; determining the target association degree of each customer to be recommended according to the first association degree and the second association degree of each customer to be recommended; sequencing each client to be recommended according to the target relevance to obtain a queue; and recommending the clients according to the queue.
According to a preferred embodiment of the present invention, the processor 13 further executes a plurality of instructions including:
adopting a web crawler technology and/or acquiring customer information from an official website of a customer;
determining a label according to the customer information;
and generating the label library according to the labels.
According to a preferred embodiment of the present invention, the processor 13 further executes a plurality of instructions including:
determining all labels of each customer to be recommended;
calculating the ratio of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended;
calculating the proportion of the to-be-recommended customers containing the target labels in the customer base to obtain a second ratio of each to-be-recommended customer;
multiplying the first ratio and the second ratio of each customer to be recommended to obtain a first numerical value of each customer to be recommended;
and squaring each first numerical value to obtain a first relevance of each client to be recommended.
According to a preferred embodiment of the present invention, the processor 13 further executes a plurality of instructions including:
acquiring events related to the existing clients from a mailbox; and/or
And acquiring events related to the existing clients from a client relationship management system.
According to a preferred embodiment of the present invention, the processor 13 further executes a plurality of instructions including:
calculating a second value for each event based on the sigmod function;
calculating the weight of each event;
multiplying the second numerical value and the weight of each event to obtain a third numerical value of each event;
adding all third numerical values in the existing clients to obtain a first compactness of the existing clients and the users;
calculating the closeness of the first characteristic and each client to be recommended by adopting a TF-IDF method to obtain a second closeness of the existing client and each client to be recommended;
and multiplying the first closeness by the second closeness of each client to be recommended, and determining the association degree between each client to be recommended and the user to obtain the second association degree of each client to be recommended.
According to a preferred embodiment of the present invention, the processor 13 further executes a plurality of instructions including:
acquiring an existing client;
and when detecting that the existing clients are contained in the clients to be recommended, deleting the existing clients from the clients to be recommended.
According to a preferred embodiment of the present invention, the processor 13 further executes a plurality of instructions including:
acquiring feedback information every preset time;
and updating the queue according to the feedback information.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A method for customer recommendation, the method comprising:
extracting a target label from a pre-configured label library;
screening out the clients containing the target tags from a pre-configured client library to serve as clients to be recommended;
determining all labels of each customer to be recommended;
calculating the ratio of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended;
calculating the proportion of the to-be-recommended customers containing the target labels in the customer base to obtain a second ratio of each to-be-recommended customer;
multiplying the first ratio and the second ratio of each customer to be recommended to obtain a first numerical value of each customer to be recommended;
squaring each first numerical value to obtain a first relevance degree of each client to be recommended;
acquiring an event generated between an existing client and a user;
extracting a first feature of the existing customer;
determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended;
determining the target association degree of each customer to be recommended according to the first association degree and the second association degree of each customer to be recommended;
sequencing each client to be recommended according to the target relevance to obtain a queue;
and recommending the clients according to the queue.
2. The customer recommendation method of claim 1, wherein prior to extracting the target tag from the preconfigured tag library, the method further comprises:
adopting a web crawler technology and/or acquiring customer information from an official website of a customer;
determining a label according to the customer information;
and generating the label library according to the labels.
3. The customer recommendation method of claim 1, wherein said obtaining events generated between existing customers and users comprises one or more of the following:
acquiring events related to the existing clients from a mailbox; and/or
And acquiring events related to the existing clients from a client relationship management system.
4. The client recommendation method according to claim 1, wherein the determining a degree of association between each client to be recommended and the user according to the event and the first characteristic, and obtaining a second degree of association of each client to be recommended comprises:
calculating a second value for each event based on the sigmod function;
calculating the weight of each event;
multiplying the second numerical value and the weight of each event to obtain a third numerical value of each event;
adding all third numerical values in the existing clients to obtain a first compactness of the existing clients and the users;
calculating the closeness of the first characteristic and each client to be recommended by adopting a TF-IDF method to obtain a second closeness of the existing client and each client to be recommended;
and multiplying the first closeness by the second closeness of each client to be recommended, and determining the association degree between each client to be recommended and the user to obtain the second association degree of each client to be recommended.
5. The client recommendation method according to claim 1, wherein before the ranking each client to be recommended according to the target relevance degree to obtain a queue, the method further comprises:
acquiring an existing client;
and when detecting that the existing clients are contained in the clients to be recommended, deleting the existing clients from the clients to be recommended.
6. The customer recommendation method of claim 1, wherein after making a customer recommendation based on the queue, the method further comprises:
acquiring feedback information every preset time;
and updating the queue according to the feedback information.
7. A customer recommendation apparatus, the apparatus comprising:
the extraction unit is used for extracting a target label from a pre-configured label library;
the screening unit is used for screening out the clients containing the target labels from a pre-configured client library to serve as clients to be recommended;
the calculation unit is used for determining all labels of each client to be recommended;
the calculation unit is further configured to calculate the ratio of the target label in all labels of each customer to be recommended to obtain a first ratio of each customer to be recommended;
the calculation unit is further used for calculating the proportion of the to-be-recommended customers containing the target labels in the customer base to obtain a second ratio of each to-be-recommended customer;
the calculation unit is further configured to multiply the first ratio and the second ratio of each to-be-recommended client to obtain a first numerical value of each to-be-recommended client;
the calculation unit is further configured to square each first numerical value to obtain a first association degree of each customer to be recommended;
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring an event generated between an existing client and a user;
the extracting unit is further used for extracting a first feature of the existing client;
the determining unit is used for determining the association degree between each client to be recommended and the user according to the event and the first characteristics to obtain a second association degree of each client to be recommended;
the determining unit is further used for determining the target relevance of each customer to be recommended according to the first relevance and the second relevance of each customer to be recommended;
the sequencing unit is used for sequencing each client to be recommended according to the target relevance degree to obtain a queue;
and the recommending unit is used for recommending the client according to the queue.
8. An electronic device, characterized in that the electronic device comprises:
a memory storing at least one instruction; and
a processor executing instructions stored in the memory to implement the customer recommendation method of any of claims 1 to 6.
9. A computer-readable storage medium characterized by: the computer-readable storage medium has stored therein at least one instruction that is executable by a processor in an electronic device to implement the customer recommendation method of any of claims 1-6.
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