CN108255957A - One kind recommends matching process based on Venture Capital field precision dataization - Google Patents
One kind recommends matching process based on Venture Capital field precision dataization Download PDFInfo
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- CN108255957A CN108255957A CN201711394613.8A CN201711394613A CN108255957A CN 108255957 A CN108255957 A CN 108255957A CN 201711394613 A CN201711394613 A CN 201711394613A CN 108255957 A CN108255957 A CN 108255957A
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Abstract
The present invention provides a kind of Venture Capital field precision dataization that is based on and recommends matching process, can efficiently dock the project of entrepreneur with investment Man's Demands.Entrepreneur uploads the structured message of project by user end to server, and investor uploads investment preference information by user end to server；The spliced content of all word contents of extraction is dispersed as keyword by server；By the word frequency of keyword divided by inverse word frequency, keyword importance score value in the project is obtained；By field and the incidence relation of keyword, show that investor segments score value；By keyword, importance score value and investor segment score value composition of vector in the project, then obtain field score value into row vector cosine angle calcu-lation；Investor is calculated finally to project score value=field score value × a%+ cities score value × b%+ financing stages score value × c%；It is finally sorted from high to low to project score value according to each investor, obtains investor in the top, the result exactly matched.
Description
Technical field
Matching process is recommended based on Venture Capital field precision dataization the present invention relates to one kind.
Background technology
Venture Capital is the abbreviation of venture capital investment.Venture capital investment refers to professional investment personnel（Initial investment expert）For using high-tech as
The Creative Company on basis provides the activity of financing.Different from general investor, initial investment expert is not only injected capital into, Er Qieyong
The experience of their long-term accumulations, Knowledge and information network help enterprise administrator preferably distributors.
Entrepreneur is extremely important to the approach of investor by project recommendation.Entrepreneur is during delivery project, not
Understanding which investor can be interested in the project of oneself, the delivery project of blindness, inefficiency；And for investor,
Its a large amount of uninterested project are received, needing to waste a large amount of time is screened and handled to these projects, investor master
Dynamic to go lookup project, in face of the project of magnanimity, screening is also the thing of a consuming energy.At present, there are no a kind of efficient
Method docks the project of entrepreneur with investment Man's Demands.
Invention content
It is an object of the invention to overcome above-mentioned deficiency in the prior art, and provide a kind of reasonable design based on
Venture Capital field precision dataization recommends matching process, can efficiently carry out the project of entrepreneur and investment Man's Demands pair
It connects.
Technical solution is used by the present invention solves the above problems：One kind is based on the recommendation of Venture Capital field precision dataization
Method of completing the square, it is characterised in that：Step is：
First, entrepreneur uploads the structured message of project by user end to server, and structured message includes the affiliated of project
City, financing stage, the financing amount of money, project material, server collect these structured messages；
Investor uploads investment preference information by user end to server, and investment preference information includes investment field, investment city
City, investment stage, investment amount；Server collects these investment preference information；
2nd, server extracts the word content of project from project material；
3rd, server splices all word contents of extraction；
4th, server breaks up spliced content to form keyword, and keyword root is according to the word frequency after splicing in content from height
It arranges on earth；
5th, the keyword in each step 4 is recycled, it is searched and appears in word frequency in general dictionary, keyword is in general term
Word frequency in library is referred to as inverse word frequency；
6th, the word frequency of keyword divided by inverse word frequency, obtain keyword importance score value in the project, by importance score value from height
To low, forward several keywords are taken；
7th, keyword is associated with field；
8th, by field and the incidence relation of keyword, show that investor segments score value；
9th, by keyword, importance score value and investor segment score value composition of vector in the project, are then pressed from both sides into row vector cosine
Field score value is calculated in angle；
Tenth, the affiliated city of project is set as a fixation score value, the investment city of investor is set as a certain score value, by two points
It is worth composition of vector, row vector of going forward side by side cosine angle calcu-lation obtains city score value；The financing stage of project is set as a fixation score value,
A certain score value is set as with the investment stage of investor, by two score value composition of vector, row vector of going forward side by side cosine angle calcu-lation obtains
Finance score value, then calculate investor finally to project score value=field score value × a%+ cities score value × b%+ financing stage score value ×
c%；
11, it is finally sorted from high to low to project score value according to each investor, obtains investor in the top, be exactly
It is matching as a result, again by investment amount in matching result be more than financing the amount of money investor feed back to entrepreneur.
Project material of the present invention includes brief introduction, in short introduces, report of business plan.
A of the present invention is 70, b 20, c 10.
Compared with prior art, the present invention haing the following advantages and effect：Entrepreneur is helped quickly to find to oneself project
Interested investor improves docking effect；Investor is helped to excavate interested project, improves working efficiency；It realizes precisely
The recommendation matching of digitization.
Specific embodiment
Below by embodiment, the present invention is described in further detail, following embodiment be explanation of the invention and
The invention is not limited in following embodiments.
Embodiment.
The present embodiment step is：
First, entrepreneur uploads the structured message of project by user end to server such as APP, and structured message includes project
Affiliated city, financing stage, the financing amount of money, project material, project material include brief introduction, in short introduction, report of business plan etc.
Written material；Server collects these structured messages；
Investor uploads investment preference information by user end to server such as APP, and investment preference information includes investment field, throws
Provide city, investment stage, investment amount；Server collects these investment preference information；
2nd, server extracts the word content of report of business plan in project material using the pdfbox libraries of Apache；
3rd, server introduced by brief introduction, in short and report of business plan these project materials in all word contents for extracting spell
It connects, joining method is serial to splice to be extracted by page；
4th, spliced content above is handled the TextRank keyword methods in the HanLP of library by Chinese and beaten by server
More than 2 words, the keywords less than 5 words are dissipated into, keyword root arranges from high in the end according to the word frequency in the content after splicing；
5th, the keyword in each step 4 is recycled, it is searched and appears in general dictionary（Such as Google search results）In
Word frequency, word frequency of the keyword in general dictionary are referred to as inverse word frequency；
6th, the word frequency of keyword divided by inverse word frequency, obtain keyword importance score value in the project, by importance score value from height
To low, 10 forward keywords are taken；
7th, the keyword that step 6 is taken out with field is associated, by manual examination and verification, judges whether the keyword belongs to and work as
One-to-one incidence relation is established in preceding field；
8th, by field and the incidence relation of keyword, score value of the investor to the fancy grade of keyword is calculated, if neck
Domain and keyword are relevant, then investor is then hobby journey of the investor to keyword to the score value of the fancy grade in field
The score value of degree, such as：Investor is 8 points to the fancy grade of field A, and field A it is relevant with keyword b, it is concluded that
Investor is also 8 points to the fancy grade of keyword b；Investor is investor's participle to the score value of the fancy grade of keyword
Score value；Investor is a digital quantization to some field degree of investor's preference to the score value of the fancy grade in certain field,
Either IR personnel maintenance is filled in by investor oneself to obtain or add the attention rate in certain field investor by system
Power is calculated；
9th, importance score value and investor segment score value composition of vector, Ran Houjin to the keyword for taking out step 6 in the project
Row vector cosine angle calcu-lation obtains field score value；
Tenth, the affiliated city of project is set as a fixation score value, the investment city of investor is set as a certain score value, by two points
It is worth composition of vector, row vector of going forward side by side cosine angle calcu-lation obtains city score value；The financing stage of project is set as a fixation score value,
A certain score value is set as with the investment stage of investor, by two score value composition of vector, row vector of going forward side by side cosine angle calcu-lation obtains
Finance score value, then calculate investor finally to project score value=field score value × a%+ cities score value × b%+ financing stage score value ×
c%；City and investment stage are invested by manually carrying out maintenance adjustment；
11, it is finally sorted from high to low to project score value according to each investor, obtains investor in the top, be exactly
It is matching as a result, again by investment amount in matching result be more than financing the amount of money investor feed back to entrepreneur.
Furthermore, it is necessary to illustrate, the specific embodiment described in this specification, the shape of parts and components is named
Claiming etc. can be different, described in this specification above content is only to structure example explanation of the present invention.It is all according to
According to equivalence changes or simple change that construction, feature and the principle described in inventional idea of the present invention are done, it is included in this hair
In the protection domain of bright patent.Those skilled in the art can do described specific embodiment various
The modification of various kinds or supplement substitute in a similar way, without departing from structure of the invention or surmount present claims
Range defined in book, is within the scope of protection of the invention.
Claims (3)
1. one kind recommends matching process based on Venture Capital field precision dataization, it is characterised in that：Step is：
First, entrepreneur uploads the structured message of project by user end to server, and structured message includes the affiliated of project
City, financing stage, the financing amount of money, project material, server collect these structured messages；
Investor uploads investment preference information by user end to server, and investment preference information includes investment field, investment city
City, investment stage, investment amount；Server collects these investment preference information；
2nd, server extracts the word content of project from project material；
3rd, server splices all word contents of extraction；
4th, server breaks up spliced content to form keyword, and keyword root is according to the word frequency after splicing in content from height
It arranges on earth；
5th, the keyword in each step 4 is recycled, it is searched and appears in word frequency in general dictionary, keyword is in general term
Word frequency in library is referred to as inverse word frequency；
6th, the word frequency of keyword divided by inverse word frequency, obtain keyword importance score value in the project, by importance score value from height
To low, forward several keywords are taken；
7th, keyword is associated with field；
8th, by field and the incidence relation of keyword, show that investor segments score value；
9th, by keyword, importance score value and investor segment score value composition of vector in the project, are then pressed from both sides into row vector cosine
Field score value is calculated in angle；
Tenth, the affiliated city of project is set as a fixation score value, the investment city of investor is set as a certain score value, by two points
It is worth composition of vector, row vector of going forward side by side cosine angle calcu-lation obtains city score value；The financing stage of project is set as a fixation score value,
A certain score value is set as with the investment stage of investor, by two score value composition of vector, row vector of going forward side by side cosine angle calcu-lation obtains
Finance score value, then calculate investor finally to project score value=field score value × a%+ cities score value × b%+ financing stage score value ×
c%；
11, it is finally sorted from high to low to project score value according to each investor, obtains investor in the top, be exactly
It is matching as a result, again by investment amount in matching result be more than financing the amount of money investor feed back to entrepreneur.
2. according to claim 1 recommend matching process based on Venture Capital field precision dataization, it is characterised in that：Described
Project material includes brief introduction, in short introduces, report of business plan.
3. according to claim 1 recommend matching process based on Venture Capital field precision dataization, it is characterised in that：Described
A is 70, b 20, c 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201711394613.8A CN108255957A (en) | 2017-12-21 | 2017-12-21 | One kind recommends matching process based on Venture Capital field precision dataization |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201711394613.8A CN108255957A (en) | 2017-12-21 | 2017-12-21 | One kind recommends matching process based on Venture Capital field precision dataization |
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| CN108255957A true CN108255957A (en) | 2018-07-06 |
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| CN201711394613.8A Pending CN108255957A (en) | 2017-12-21 | 2017-12-21 | One kind recommends matching process based on Venture Capital field precision dataization |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109118362A (en) * | 2018-07-26 | 2019-01-01 | 北京洪泰同创信息技术有限公司 | The method and apparatus of commerciality investment and financing transaction |
| CN110134761A (en) * | 2019-04-16 | 2019-08-16 | 深圳壹账通智能科技有限公司 | Adjudicate document information retrieval method, device, computer equipment and storage medium |
| CN112488842A (en) * | 2020-12-14 | 2021-03-12 | 天津北晟企业服务有限公司 | Investment institution recommendation method and device |
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| CN109118362A (en) * | 2018-07-26 | 2019-01-01 | 北京洪泰同创信息技术有限公司 | The method and apparatus of commerciality investment and financing transaction |
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Application publication date: 20180706 |