CN108268660A - A kind of customer data recommends method, server and storage medium - Google Patents
A kind of customer data recommends method, server and storage medium Download PDFInfo
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- CN108268660A CN108268660A CN201810129161.9A CN201810129161A CN108268660A CN 108268660 A CN108268660 A CN 108268660A CN 201810129161 A CN201810129161 A CN 201810129161A CN 108268660 A CN108268660 A CN 108268660A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
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Abstract
This application discloses a kind of customer datas to recommend method, server and storage medium, this method to include：It obtains with screening the relevant keyword of client, and corresponding weight coefficient is distributed for keyword；The customer data with keyword match is crawled from internet using web crawlers technology；Using weight coefficient, the matching degree of customer data crawled is calculated；The customer data that matching degree is met to the first preset condition pushes to target terminal user.The application is compared to the previous artificial way of recommendation or visits mode, Customer mining efficiency can significantly be promoted and reduce the human cost during Customer mining, and, the application can also determine the matching degree of the customer data crawled according to the weight coefficient distributed in advance for keyword, it can determine that enterprise customer is relatively suitble to which client Cooperation is unfolded with based on the matching degree, thus it is advantageously ensured that the customer data that target terminal user is got has higher commercial value.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of customer data recommends method, server and storage to be situated between
Matter.
Background technology
Currently, enterprise customer is in order to expand own service, it is often necessary to excavate potential client.Present enterprise customer is usual
Be using it is traditional it is artificial recommend or visit in the form of excavate the client of needs, on the one hand these modes need to put into a large amount of
Personnel, time and fund, human cost is higher, is on the other hand that efficiency is very low, it is difficult to hold market trend in time,
Furthermore due to the influence of artificial subjective factor, cause final Result that there is very big randomness, reliability is poor.
In summary as can be seen that how to be effectively reduced the human cost of Customer mining, improve Customer mining efficiency with
And reliability is current urgent problem to be solved.
Invention content
In view of this, the purpose of the present invention is to provide a kind of customer datas to recommend method, server and storage medium, energy
It is enough effectively to reduce the human cost of Customer mining, and improve Customer mining efficiency and the reliability of Result.Its specific side
Case is as follows：
In a first aspect, the invention discloses a kind of customer datas to recommend method, including：
It obtains with screening the relevant keyword of client, and corresponding weight coefficient is distributed for the keyword；
The customer data with the keyword match is crawled from internet using web crawlers technology；
Using the weight coefficient, the matching degree of customer data crawled is calculated；
The customer data that matching degree is met to the first preset condition pushes to target terminal user.
Optionally, described the step of obtaining keyword relevant with screening client, including：
It is determined from the relevant information of target user and the relevant keyword of potential customers；
And/or
The keyword of target user's input is obtained using keyword input interface preset in the target terminal user.
Optionally, described the step of distributing corresponding weight coefficient for the keyword, including：
According to the relevant information of target user, determine the weight coefficient of the keyword, be then the keyword point
With corresponding weight coefficient.
Optionally, the basis and the relevant information of target user, the step of determining the weight coefficient of the keyword, packet
It includes：
According to the correlation between the relevant information of target user, the weight coefficient of the keyword is determined.
Optionally, include user's company information with the relevant information of target user, user has bought the information of product, user
The information of self-made products, user sold the information of product, user currently the information of product for sale, the business opportunity information of user,
Any one or a few in the information of relative clients, user's history location information and user current location information in history.
Optionally, it is described to be crawled from internet and the customer data of the keyword match using web crawlers technology
Step, including：
The keyword that weight coefficient meets the second preset condition is filtered out from the keyword, obtains target keywords；
It is crawled from internet and the matched customer data of the target keywords using web crawlers technology.
Optionally, described the step of utilizing the weight coefficient, calculating the matching degree of customer data crawled, including：
Determine the corresponding keyword of every part crawled customer data；
According to the quantity of the corresponding keyword of every part of customer data and corresponding weight coefficient, every part of customer data is calculated
Matching degree.
Optionally, the customer data by matching degree the first preset condition of satisfaction pushes to the step of target terminal user
Suddenly, it further includes：
According to the sequence of matching degree from big to small, the customer data that the first preset condition is met to matching degree is ranked up,
Customer data after being sorted；
Customer data after the sequence is pushed into target terminal user.
Second aspect, the invention discloses a kind of customer data recommendation server, including processor and memory；Wherein,
The processor realizes that aforementioned customer data recommends method when performing the customer data recommended program stored in the memory.
The third aspect, the invention discloses a kind of computer readable storage medium, for storing customer data recommended program；
Wherein, realize that aforementioned customer data recommends method when the customer data recommended program is executed by processor.
As it can be seen that the present invention is first got with screening the relevant keyword of user, and distributes corresponding weight system for keyword
Number, later use web crawlers technology crawls customer data corresponding with above-mentioned keyword from internet, and utilizes above-mentioned power
Weight coefficient calculates the matching degree of customer data, and the customer data that matching degree is finally met to preset condition is pushed to target user's end
End, therefore, the technical solution in the present invention is a kind of scheme that can be realized based on computer program, compared to previous
The artificial way of recommendation visits mode, can significantly promote Customer mining efficiency and reduce the manpower during Customer mining
Cost, also, the present invention can also determine of the customer data crawled according to the weight coefficient distributed in advance for keyword
With degree, it can determine that enterprise customer is relatively suitble to which client Cooperation is unfolded with based on the matching degree, thus be conducive to
Promote the reliability for the customer data for finally pushing to target terminal user, it is ensured that the customer data that target terminal user is got
With higher commercial value.To sum up, the present invention can effectively reduce the human cost of Customer mining, and improve Customer mining effect
The reliability of rate and Result.
Description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention, for those of ordinary skill in the art, without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 recommends method flow diagram for a kind of customer data disclosed by the embodiments of the present invention；
Fig. 2 recommends method flow diagram for a kind of specific customer data disclosed by the embodiments of the present invention；
Fig. 3 is a kind of specific customer data recommended flowsheet figure disclosed by the embodiments of the present invention；
Fig. 4 is a kind of customer data commending system structure diagram disclosed by the embodiments of the present invention.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without making creative work
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a kind of customer datas to recommend method, and shown in Figure 1, this method includes：
Step S11：It obtains with screening the relevant keyword of client, and corresponding weight coefficient is distributed for keyword.
In the present embodiment, the relevant key of client can be obtained and screened in a manner that background system automatically extracts
Word can also be obtained and be screened the relevant keyword of client by way of user entered keyword, naturally it is also possible to utilize
Above two mode obtains and screens the relevant keyword of client.
In addition, the present embodiment can automatically be distributed when distributing corresponding weight coefficient for keyword by background system
Mode to distribute corresponding weight coefficient for keyword, can also be by way of user's input weight coefficient come for keyword
Distribute corresponding weight coefficient.
Step S12：The customer data with keyword match is crawled from internet using web crawlers technology.
In the present embodiment, web crawlers technology can be utilized, is crawled from the different data sources on internet and keyword
The customer data to match.It should be noted that the data type of above-mentioned customer data can be existed on internet web page
Arbitrary data types, including but not limited to word, picture, audio, video etc..
Step S13：Using weight coefficient, the matching degree of customer data crawled is calculated.
In the present embodiment, the weight coefficient of keyword is bigger, illustrates that the keyword wishes the client found with active user
Between relevance it is stronger.Based on this feature, the present embodiment can calculate above-mentioned step by using the weight coefficient of keyword
The matching degree of customer data crawled in rapid S12.It should be noted that the matching degree of customer data refers to this in the present embodiment
Customer data and active user wish the matching degree between the client found, and matching degree is higher, illustrates that respective client data are got over
It is that active user is required.
Step S14：The customer data that matching degree is met to the first preset condition pushes to target terminal user.
It should be noted that above-mentioned first preset condition can be limited specifically according to actual needs.For example, this implementation
Example can be based only on the size of matching degree to determine above-mentioned first preset condition, that is, the present embodiment can be big by matching degree
Target terminal user is pushed in the customer data of preset matching degree threshold value.In addition, the present embodiment can also be based on matching degree
Size and the quantity of customer data determine above-mentioned first preset condition, that is, the present embodiment can be by preset quantity
And matching degree according to numerical values recited come front customer data push to target terminal user or the present embodiment can will
A customer data of matching degree maximum pushes to target terminal user.
It is understood that target terminal user in the present embodiment include but not limited to based on Android, iOS,
Mobile phone, tablet computer, personal computer or the other kinds of terminal of the operating systems such as Windows.In addition, in the present embodiment
Target terminal user get customer data after, can be shown by data disaply moudle preset in target terminal user
Show the customer data got.
As it can be seen that the embodiment of the present invention is first got with screening the relevant keyword of user, and corresponding for keyword distribution
Weight coefficient, later use web crawlers technology crawls customer data corresponding with above-mentioned keyword from internet, and utilizes
Above-mentioned weight coefficient calculates the matching degree of customer data, and the customer data that matching degree is finally met to preset condition is pushed to target
User terminal, therefore, the technical solution in the embodiment of the present invention is a kind of side that can be realized based on computer program
Case compared to the previous artificial way of recommendation or visits mode, can significantly promote Customer mining efficiency and reduce client and dig
Human cost during pick, also, the embodiment of the present invention can also be determined according to the weight coefficient distributed in advance for keyword
The matching degree of the customer data crawled can determine that enterprise customer is relatively suitble to be unfolded with which client based on the matching degree
Thus Cooperation is conducive to be promoted the reliability for the customer data for finally pushing to target terminal user, it is ensured that target user
The customer data that terminal is got has higher commercial value.To sum up, the embodiment of the present invention can effectively reduce Customer mining
Human cost, and improve Customer mining efficiency and the reliability of Result.
On the basis of previous embodiment, the embodiment of the invention discloses a kind of specific customer datas to recommend method, ginseng
As shown in Figure 2, this method includes：
Step S21：Keyword relevant with potential customers and/or profit are determined from the relevant information of target user
The keyword of target user's input is obtained with keyword input interface preset in target terminal user；According to target user's phase
The information of pass determines the weight coefficient of keyword, then distributes corresponding weight coefficient for keyword.
It should be noted that include but not limited to user company letter with the relevant information of target user in the present embodiment
Breath, user have bought that the information of product, the information of user's self-made products, user has sold the information of product, user currently treats pin
Sell information, in history the business opportunity information of user, the information of relative clients, user's history location information and the user's present bit of product
Confidence breath in any one or a few.
In the present embodiment, above-mentioned basis and the relevant information of target user, the step of determining the weight coefficient of keyword, tool
Body can include：According to the correlation between the relevant information of target user, the weight coefficient of keyword is determined.
For example, can the keyword be determined according to the correlation between some keyword and user current location information
Weight coefficient, such as A cities if the current user for being engaged in toy industry has gone on business, can be based on " Toy Factory of A cities " this
Between keyword and the current location information of the user there is this of strong correlation, be " Toy Factory of A cities " this key
Word determines bigger weight coefficient.
In another example can this be determined according to the correlation between some keyword and the information of relative clients in history
The weight coefficient of keyword, for example if the common ground for the client that some enterprise customer visits recently is to be to be engaged in children education
The client of this industry can then be based on " children English early education " this keyword and believe with the client that the enterprise customer visits recently
Have the characteristics that between breath strong correlation this, be children English early education " this keyword determines bigger weight coefficient.
It is understood that other than above-mentioned two example, the embodiment of the present invention can also be according to keyword and target
The correlation between a kind of any other information or several information in the relevant information of user determines the weight system of keyword
Number.
In the present embodiment, after getting keyword by above-mentioned steps S21, these keywords can be stored to default
Keyword database in.
Step S22：The keyword that weight coefficient meets the second preset condition is filtered out from keyword, obtains target critical
Word；It is crawled from internet and the matched customer data of target keywords using web crawlers technology.
That is, after the present embodiment gets keyword by above-mentioned steps S21, it is excessive in order to avoid the quantity of keyword
A large amount of operand is caused, and reduces the degree of redundancy of customer data subsequently pushed, the present embodiment is crawled using keyword
Before customer data, the keyword work for meeting the second preset condition is first filtered out from the keyword that above-mentioned steps S21 is got
For target keywords, the target keywords that later use screens crawl customer data.It is understood that above-mentioned second is pre-
If condition can be limited specifically according to actual needs.For example, the size that the present embodiment can be based only on weight coefficient is come
Above-mentioned second preset condition is determined, that is, the keyword that weight coefficient can be more than predetermined coefficient threshold value by the present embodiment determines
For target keywords.In addition, the present embodiment can also based on weight coefficient size and preset keyword quantity come determine on
State the second preset condition, that is, the present embodiment can by preset keyword quantity and weight coefficient according to numerical values recited
The keyword for coming front is determined as target keywords.
Step S23：Determine the corresponding keyword of every part crawled customer data；According to the corresponding pass of every part of customer data
The quantity of key word and corresponding weight coefficient calculate the matching degree of every part of customer data.
It is understood that since the information included in every part of customer data crawling is relatively more, so that every part
Multiple keywords are often included in customer data.In view of in same a customer data if comprising the quantity of keyword get over
It is more, illustrate that this part of customer data may be important for enterprise customer, based on this feature, the present embodiment is crawling
To after customer data, it will the keyword included in every part of customer data is determined, it is possible thereby to count every part of customer data
In the quantity of keyword that includes, quantity and corresponding keyword subsequently based on the corresponding keyword of every part of customer data correspond to
Weight coefficient, the matching degree of every part of customer data can be calculated.It should be noted that in the present embodiment customer data
Refer to the customer data with degree and active user wishes matching degree between the client found, matching degree is higher, illustrate accordingly
Active user is required for customer data.
Step S24：According to the sequence of matching degree from big to small, matching degree is met the customer data of the first preset condition into
Row sequence, the customer data after being sorted；Customer data after sequence is pushed into target terminal user.
It should be noted that above-mentioned first preset condition can be limited specifically according to actual needs.For example, this implementation
Example can be based only on the size of matching degree to determine above-mentioned first preset condition, that is, the present embodiment can be big by matching degree
Target terminal user is pushed in the customer data of preset matching degree threshold value.In addition, the present embodiment can also be based on matching degree
Size and the quantity of customer data determine above-mentioned first preset condition, that is, the present embodiment can be by preset quantity
And matching degree according to numerical values recited come front customer data push to target terminal user or the present embodiment can will
A customer data of matching degree maximum pushes to target terminal user.
Method is recommended to be described in detail the customer data in the present embodiment by way of example below.
It is shown in Figure 3, it is assumed that include with the relevant information of some user：User's Business Name " xxx clothes ", the use
Family currently logs in that place is " Shenzhen ", user's existing procucts have client and include for " xxx children's garment and xxx women's dresses ", the user
" xx supermarkets, xxx children's garment shop, xxx clothing factories ".User is " clothing factory " by the keyword that keyword input interface inputs, and
Background system is from above-mentioned with being determined in the relevant information of the user with the relevant keyword of potential customers including " clothes are (near
Adopted word：Dress ornament), supermarket, children's garment, Shenzhen, women's dress, clothing factory's (near synonym：Clothes company) ".Then according to above-mentioned each key
Correlation between word and the relevant information of the user determines the weight coefficient of each keyword.For example, it is assumed that " supermarket " this
A keyword is frequently occurred in client's list of user, then " supermarket " this keyword can be given to assign compared to other words more
Big weight coefficient.
In Fig. 3, by the keyword and web crawlers technology finally determined, corresponding visitor is crawled from internet
User data, and determine the matching degree of every part of customer data, for example, it is assumed that comprising " super in the customer data of " A client " crawled
The matching degree of the customer data of " A client " can be then determined as a comparison by multiple keywords such as city ", " Shenzhen ", " clothes "
Big numerical value, so that the customer data preferentially subsequently is pushed to corresponding user terminal.
Correspondingly, the embodiment of the invention also discloses a kind of customer data commending system, shown in Figure 4, the system packet
It includes：
Keyword acquisition module 11, for obtaining and screening the relevant keyword of client；
Weight distribution module 12, for distributing corresponding weight coefficient for keyword；
Customer data crawls module 13, for crawling the visitor with keyword match from internet using web crawlers technology
User data；
Matching degree computing module 14 for utilizing weight coefficient, calculates the matching degree of customer data crawled；
Customer data pushing module 15, the customer data for matching degree to be met to the first preset condition push to target and use
Family terminal.
The corresponding contents disclosed in previous embodiment can be referred to about the more specifical course of work of above-mentioned modules,
It is no longer repeated herein.
As it can be seen that the embodiment of the present invention is first got with screening the relevant keyword of user, and corresponding for keyword distribution
Weight coefficient, later use web crawlers technology crawls customer data corresponding with above-mentioned keyword from internet, and utilizes
Above-mentioned weight coefficient calculates the matching degree of customer data, and the customer data that matching degree is finally met to preset condition is pushed to target
User terminal, therefore, the technical solution in the embodiment of the present invention is a kind of side that can be realized based on computer program
Case compared to the previous artificial way of recommendation or visits mode, can significantly promote Customer mining efficiency and reduce client and dig
Human cost during pick, also, the embodiment of the present invention can also be determined according to the weight coefficient distributed in advance for keyword
The matching degree of the customer data crawled can determine that enterprise customer is relatively suitble to be unfolded with which client based on the matching degree
Thus Cooperation is conducive to be promoted the reliability for the customer data for finally pushing to target terminal user, it is ensured that target user
The customer data that terminal is got has higher commercial value.To sum up, the embodiment of the present invention can effectively reduce Customer mining
Human cost, and improve Customer mining efficiency and the reliability of Result.
Correspondingly, the invention also discloses a kind of customer data recommendation server, including processor and memory；Wherein,
Processor realizes that the customer data disclosed in previous embodiment is recommended when performing the customer data recommended program stored in memory
Method.Specific steps about this method can refer to the corresponding contents disclosed in previous embodiment, no longer be repeated herein.
Further, the invention also discloses a kind of computer readable storage mediums, recommend journey for storing customer data
Sequence；Wherein, realize that customer data recommends method disclosed in previous embodiment when customer data recommended program is executed by processor.It closes
The corresponding contents disclosed in previous embodiment can be referred in the specific steps of this method, are no longer repeated herein.
Each embodiment is described by the way of progressive in this specification, the highlights of each of the examples are with it is other
The difference of embodiment, just to refer each other for same or similar part between each embodiment.For dress disclosed in embodiment
For putting, since it is corresponded to the methods disclosed in the examples, so description is fairly simple, related part is referring to method part
Explanation.
Professional further appreciates that, with reference to each exemplary unit of the embodiments described herein description
And algorithm steps, can be realized with the combination of electronic hardware, computer software or the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is performed actually with hardware or software mode, specific application and design constraint depending on technical solution.Profession
Technical staff can realize described function to each specific application using distinct methods, but this realization should not
Think beyond the scope of this invention.
It can directly be held with reference to the step of method or algorithm that the embodiments described herein describes with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
Finally, it is to be noted that, herein, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, term " comprising ", "comprising" or its any other variant meaning
Covering non-exclusive inclusion, so that process, method, article or equipment including a series of elements not only include that
A little elements, but also including other elements that are not explicitly listed or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except also there are other identical elements in the process, method, article or apparatus that includes the element.
Method, server and storage medium is recommended to carry out detailed Jie a kind of customer data provided by the present invention above
It continues, specific case used herein is expounded the principle of the present invention and embodiment, and the explanation of above example is only
It is the method and its core concept for being used to help understand the present invention；Meanwhile for those of ordinary skill in the art, according to this hair
Bright thought, there will be changes in specific embodiments and applications, in conclusion the content of the present specification should not manage
It solves as limitation of the present invention.
Claims (10)
1. a kind of customer data recommends method, which is characterized in that including：
It obtains with screening the relevant keyword of client, and corresponding weight coefficient is distributed for the keyword；
The customer data with the keyword match is crawled from internet using web crawlers technology；
Using the weight coefficient, the matching degree of customer data crawled is calculated；
The customer data that matching degree is met to the first preset condition pushes to target terminal user.
2. customer data according to claim 1 recommends method, which is characterized in that the acquisition and screening client are relevant
The step of keyword, including：
It is determined from the relevant information of target user and the relevant keyword of potential customers；
And/or
The keyword of target user's input is obtained using keyword input interface preset in the target terminal user.
3. customer data according to claim 2 recommends method, which is characterized in that described corresponding for keyword distribution
Weight coefficient the step of, including：
According to the relevant information of target user, determine the weight coefficient of the keyword, then be the keyword distribution phase
The weight coefficient answered.
4. customer data according to claim 3 recommends method, which is characterized in that the basis and target user are relevant
Information, the step of determining the weight coefficient of the keyword, including：
According to the correlation between the relevant information of target user, the weight coefficient of the keyword is determined.
5. customer data according to claim 3 recommends method, which is characterized in that
Include user's company information, user with the relevant information of target user and buy the information of product, user's self-made products
Information, user have sold the information of product, the user currently information of product for sale, in history the business opportunity information of user, correlation
Any one or a few in the information of client, user's history location information and user current location information.
6. customer data according to any one of claims 1 to 5 recommends method, which is characterized in that described to be climbed using network
Worm technology crawls the step of customer data with the keyword match from internet, including：
The keyword that weight coefficient meets the second preset condition is filtered out from the keyword, obtains target keywords；
It is crawled from internet and the matched customer data of the target keywords using web crawlers technology.
7. customer data according to any one of claims 1 to 5 recommends method, which is characterized in that described to utilize the power
The step of weight coefficient, the matching degree of customer data that calculating crawls, including：
Determine the corresponding keyword of every part crawled customer data；
According to the quantity of the corresponding keyword of every part of customer data and corresponding weight coefficient, of every part of customer data is calculated
With degree.
8. customer data according to claim 7 recommends method, which is characterized in that described to preset matching degree satisfaction first
The step of customer data of condition pushes to target terminal user further includes：
According to the sequence of matching degree from big to small, the customer data that the first preset condition is met to matching degree is ranked up, obtains
Customer data after sequence；
Customer data after the sequence is pushed into target terminal user.
9. a kind of customer data recommendation server, which is characterized in that including processor and memory；Wherein, the processor is held
Such as claim 1 to 8 any one of them customer data is realized during the customer data recommended program stored in the row memory
Recommendation method.
10. a kind of computer readable storage medium, which is characterized in that for storing customer data recommended program；Wherein, the visitor
Realize that claim 1 to 8 any one of them customer data such as recommends method when user data recommended program is executed by processor.
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| CN110795471A (en) * | 2019-10-31 | 2020-02-14 | 北京金堤科技有限公司 | Data matching method and device, computer readable storage medium and electronic equipment |
| CN110874788A (en) * | 2019-11-13 | 2020-03-10 | 合肥美的电冰箱有限公司 | Product recommendation method and device and computer storage medium |
| CN112598471A (en) * | 2020-12-25 | 2021-04-02 | 北京知因智慧科技有限公司 | Product recommendation method and device and electronic equipment |
| CN113822596A (en) * | 2021-10-12 | 2021-12-21 | 深圳市我们在线教育有限公司 | Customer screening method based on big data |
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| CN110874788A (en) * | 2019-11-13 | 2020-03-10 | 合肥美的电冰箱有限公司 | Product recommendation method and device and computer storage medium |
| CN112598471A (en) * | 2020-12-25 | 2021-04-02 | 北京知因智慧科技有限公司 | Product recommendation method and device and electronic equipment |
| CN113822596A (en) * | 2021-10-12 | 2021-12-21 | 深圳市我们在线教育有限公司 | Customer screening method based on big data |
| CN113822596B (en) * | 2021-10-12 | 2023-08-29 | 深圳市单仁牛商科技股份有限公司 | Customer screening method based on big data |
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