CN109087163A - The method and device of credit evaluation - Google Patents

The method and device of credit evaluation Download PDF

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CN109087163A
CN109087163A CN201810738014.1A CN201810738014A CN109087163A CN 109087163 A CN109087163 A CN 109087163A CN 201810738014 A CN201810738014 A CN 201810738014A CN 109087163 A CN109087163 A CN 109087163A
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user
level
association
vocabulary
assessed
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CN109087163B (en
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王峰伟
何慧梅
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Advanced New Technologies Co Ltd
Advantageous New Technologies Co Ltd
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Alibaba Group Holding Ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/06Buying, selling or leasing transactions
    • G06Q30/0601Electronic shopping [e-shopping]
    • G06Q30/0609Buyer or seller confidence or verification
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03Credit; Loans; Processing thereof

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Abstract

This specification embodiment provides a kind of method and apparatus of credit evaluation, according to this method embodiment, it is primarily based on the determining level-one association user with user to be assessed with incidence relation of human relation network, then according to the text information in the relating attribute of user to be assessed and level-one association user, obtain at least one level-one association vocabulary of user to be assessed, and level-one linked character is generated based at least one level-one association vocabulary, then determine user's to be assessed, there is at least one second level association user of incidence relation with level-one association user, and it is directed to each level-one association user, the second level linked character of user to be assessed is generated according to its corresponding second level association user, it is then based on level-one linked character and second level linked character, the credit rating of user to be assessed is assessed by credit evaluation model trained in advance.The accuracy of credit evaluation can be improved in the embodiment.

Description

The method and device of credit evaluation
Technical field
This specification one or more embodiment is related to field of computer technology, more particularly to carries out credit by computer The method and apparatus of assessment.
Background technique
With the development of computer and Internet technology, more and more business are realized by computing platform, such as quotient Product transaction, debt payment, finance debt-credit, settlement of insurance claim etc..However, in perhaps multiple services execution and processing, if not right The credit standing of service request people is assessed, and is just likely to bring greater risk, such as some criminals possibly also with electricity Sub-platform implements financial swindling, debt-credit arbitrage etc..
In routine techniques, in order to prevent with reduce above-mentioned risk, often through the identity information, assets information, purchase of user The information such as object behavior assess the credit of user.However, the usual negligible amounts of these information, shopping platform are also a variety of more Sample, may not necessarily Overall Acquisition these information to most of user, it is difficult to cover most users.Accordingly, it would be desirable to there is improved side Case improves the accuracy assessed user credit using more network datas.
Summary of the invention
This specification one or more embodiment describes a kind of method and apparatus, can be based on the incidence number between user According to, and influence of the other users to user credit to be assessed with user to be assessed with incidence relation, improve credit evaluation Accuracy.
According in a first aspect, providing a kind of method of credit evaluation, comprising: based on human relation network it is determining with it is to be evaluated Estimate the first level-one association user that user has incidence relation, wherein the human relation network is in the use for being associated operation Incidence relation is established between family, and there are between the user of incidence relation by the operation associated corresponding text information record Relating attribute;According to the text envelope in the relating attribute of the user to be assessed and the first level-one association user Breath, at least one level-one for obtaining the user to be assessed are associated with vocabulary, and raw based at least one level-one association vocabulary At the first level-one linked character of the user to be assessed;Based on the human relation network, determine the user's to be assessed At least one second level association user, wherein at least one described second level association user has with the first level-one association user Incidence relation;According to the text envelope in the relating attribute of the first level-one association user and each second level association user Breath, at least one second level for obtaining the user to be assessed are associated with vocabulary, and raw based at least one second level association vocabulary At the first second level linked character of the user to be assessed;At least it is based on the first level-one linked character and first second level Linked character assesses the credit rating of the user to be assessed by credit evaluation model trained in advance.
Embodiment according to one aspect, the text information include the operation associated corresponding phrase, sentence information; And the text information according in the relating attribute of the user to be assessed and the first level-one association user, At least one level-one association vocabulary for obtaining the user to be assessed includes: to carry out word cutting to the phrase, sentence information to obtain Initial vocabulary;Each initial vocabulary is matched with the keyword in pre-generated keyword set respectively;It will be described The initial vocabulary being matched in keyword set is associated with vocabulary as the level-one.
According to the embodiment of another aspect, the text information includes believing from the operation associated corresponding phrase, sentence The association vocabulary extracted in advance in breath;And the pass according to the user to be assessed and the first level-one association user The text information in attribute, at least one level-one association vocabulary for obtaining the user to be assessed includes: by the text This information is associated with vocabulary as at least one level-one.
In one embodiment, the keyword in the keyword set extracts by the following method: obtaining artificial calibration User's positive sample and user's negative sample;Based on human relation network determine user's positive sample and user's negative sample with Sample text information in the relating attribute of other users;The key in keyword set is determined according to the sample text information Word.
In one embodiment, each keyword in the keyword set is also corresponding with statistical indicator, the statistics Index includes at least one of the following: the number occurred in the text information of user's positive sample, in the text envelope of user's negative sample The probability of the number, deviation user's positive sample that occur in breath, the probability for being biased to user's negative sample.
According to a kind of possible design, each level-one association vocabulary is corresponding with the pass being matched in the keyword set The statistical indicator of keyword;And it is described based at least one described level-one association vocabulary generate the first of the user to be assessed Level-one linked character includes: to obtain the corresponding statistical indicator of each level-one association vocabulary;It is corresponding to each level-one association vocabulary Statistical indicator carries out the first predetermined process, generates the first level-one linked character, wherein first predetermined process include with At least one of lower: maximizing minimizes, sums, averaging, seeking weighted sum.
In one embodiment, first second level for generating the user to be assessed based on second level association vocabulary is closed Connection feature includes: to be associated with vocabulary according to the second level, generates the first level-one association user and each second level association user point Not corresponding second level-one linked character;Second predetermined process is carried out to each second level-one linked character, processing result is made For the first second level linked character of the user to be assessed, wherein second predetermined process, which includes at least one of the following:, to be asked most It is worth greatly, minimize, sum, average, seeks weighted sum.
In one embodiment, the predetermined operation include: transfer accounts, give bonus, plusing good friend.
According to second aspect, a kind of device of credit evaluation is provided, comprising: the first determination unit is configured to interpersonal The determining first level-one association user with user to be assessed with incidence relation of relational network, wherein the human relation network Incidence relation is established between the user for being associated operation, and is existed by the operation associated corresponding text information record Relating attribute between the user of incidence relation;First generation unit is configured to according to the user to be assessed and described first The text information in the relating attribute of level-one association user obtains at least one level-one conjunctive word of the user to be assessed It converges, and generates the first level-one linked character of the user to be assessed based at least one described level-one association vocabulary;Second really Order member, is configured to the human relation network, determines at least one second level association user of the user to be assessed, In, at least one described second level association user and the first level-one association user have incidence relation;Second generation unit, matches It is set to according to the text information in the relating attribute of the first level-one association user and each second level association user, obtains At least one second level of the user to be assessed is associated with vocabulary, and based at least one second level association vocabulary generate it is described to Assess the first second level linked character of user;Credit evaluation unit, be configured at least based on the first level-one linked character and The first second level linked character assesses the credit rating of the user to be assessed by credit evaluation model trained in advance.
According to the third aspect, a kind of computer readable storage medium is provided, computer program is stored thereon with, when described When computer program executes in a computer, enable computer execute first aspect method.
According to fourth aspect, a kind of calculating equipment, including memory and processor are provided, which is characterized in that described to deposit It is stored with executable code in reservoir, when the processor executes the executable code, the method for realizing first aspect.
The method and apparatus provided by this specification embodiment are primarily based on human relation network determination and use to be assessed Family has the level-one association user of incidence relation, then according to the text in the relating attribute of user to be assessed and level-one association user This information obtains at least one level-one association vocabulary of user to be assessed, and generates one based at least one level-one association vocabulary Grade linked character, then determine user to be assessed, be associated with at least one second level of incidence relation with level-one association user User, and it is directed to each level-one association user, it is closed according to the second level that its corresponding second level association user generates user to be assessed Join feature, be then based on level-one linked character and second level linked character, is assessed by credit evaluation model trained in advance to be evaluated Estimate the credit rating of user.In this way, the associated data between user can be made full use of, have due to considering with user to be assessed Influence of the other users of incidence relation to user credit to be assessed, so as to improve the accuracy of credit evaluation.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, required use in being described below to embodiment Attached drawing be briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for this For the those of ordinary skill of field, without creative efforts, it can also be obtained according to these attached drawings others Attached drawing.
Fig. 1 shows the implement scene schematic diagram of one embodiment of this specification disclosure;
Fig. 2 shows a specific examples of human relation network;
Fig. 3 shows the method flow diagram of the credit evaluation according to one embodiment;
Fig. 4 shows a specific example of credit evaluation model;
Fig. 5 shows the schematic block diagram of the device of the credit evaluation according to one embodiment.
Specific embodiment
With reference to the accompanying drawing, the scheme provided this specification is described.
Fig. 1 is the implement scene schematic diagram of one embodiment that this specification discloses.In the implement scene, multiple users It can be interacted by network and computing platform.Good friend can be added between user by computing platform, transfers accounts, is rubescent Packet etc. operation.Computing platform can be operated by these between user, human relation network be established, to indicate between user Incidence relation.Human relation network can be updated according to the period, such as be updated within 1 day once, can also be often to detect Operation between user is once updated, and is not construed as limiting in this implement scene to this.
It as described in Figure 2, is the specific example of a human relation network.In the human relation network, circle (node) Indicate user, line (side) indicates incidence relation, and being wired the user that (side) connects together has direct correlation relationship.Here Incidence relation, can be addition good friend, the connection relationship that predetermined operation of transferring accounts, give bonus etc. is realized.The category of line (side) Property in by above-mentioned predetermined operation corresponding text information record, there are the relating attributes between the user of incidence relation.In order to just Line between explanation, user can also be described as side, and the relating attribute between user can also be described as side attribute.
When needing to carry out credit evaluation to some user to be assessed, can first based on human relation network determine with it is to be evaluated Estimate the level-one association user that user has incidence relation, user to be assessed corresponds in the side attribute of each level-one association user Text information obtains one group of level-one and is associated with vocabulary, and a level-one linked character can be generated according to every group of level-one association vocabulary.
It then, can and its use with incidence relation determining based on human relation network to each level-one association user Second level association user of the family as user to be assessed.For each level-one association user, corresponding to second level association user Text information in side attribute, available one group of second level are associated with vocabulary.It is closed according to each group second level of a level-one association user Join vocabulary, a second level linked character for generating user to be assessed can be summarized.In one embodiment, it is closed according to every group of second level A level-one linked character of corresponding level-one association user can be generated in connection vocabulary, according to all the one of a level-one association user The second level linked character of user to be assessed can be generated in grade linked character.
Then, each level-one linked character and second level linked character based on user to be assessed pass through letter trained in advance The credit rating of user to be assessed is assessed with assessment models.In this way, being associated with for user to be assessed and other users can be made full use of Data extract more features, improve the accuracy assessed user credit.Specifically executing for above-mentioned scene is described below Journey.
Fig. 3 shows the method flow diagram of the credit evaluation according to one embodiment.The executing subject of this method, which can be, appoints What has calculating, the system of processing capacity, unit, platform or server, such as computing platform shown in FIG. 1 etc..Such as figure 3 show, method includes the following steps: step 31, has incidence relation with user to be assessed based on human relation network is determining First level-one association user, wherein human relation network establishes incidence relation between the user for being associated operation, and passes through There are the relating attributes between the user of incidence relation for operation associated corresponding text information record;Step 32, according to be assessed Text information in the relating attribute of user and the first level-one association user obtains at least one level-one association of user to be assessed Vocabulary, and the first level-one linked character that vocabulary generates user to be assessed is associated with based at least one level-one;Step 33, it is based on people Border relational network determines at least one second level association user of user to be assessed, wherein at least one above-mentioned second level association user There is incidence relation with the first level-one association user;Step 34, according to the first level-one association user and each second level association user Relating attribute in text information, obtain user to be assessed at least one second level association vocabulary, and based at least one two Grade association vocabulary generates the first second level linked character of user to be assessed;Step 35, at least based on the first level-one linked character and First second level linked character assesses the credit rating of user to be assessed by credit evaluation model trained in advance.
Firstly, in step 31, based on determining first level-one with user to be assessed with incidence relation of human relation network Association user.It is worth noting that human relation network is used for based on the network structure of the operation associated foundation between user Record the incidence relation between user and user.There can also be relating attribute between user, which passes through above-mentioned pass Text information record in connection operation.Level-one association user is that have the user of direct correlation relationship.In practice, between user It is operation associated for example can include but is not limited to it is at least one of following: plusing good friend transfers accounts, gives bonus, etc..
Human relation network as shown in connection with fig. 2, between user 21 and user 22 have it is operation associated, such as user 21 to User 22 has carried out transfer operation, then is connected between user 21 and user 22 by line 24, indicates that they have incidence relation. Line 24 can have the relating attribute between user 21 and user 22 with corresponding record, which passes through the text in operation associated This information record, such as the message of transferring accounts " dinner cost at noon " when user 21 transfers accounts to user 22.Equally, have operation associated It can be connected by line 25 between user 22 and user 23, line 25 can be by being associated between user 22 and user 23 Operate the relating attribute between corresponding text information record user 22 and user 23.
It is appreciated that in human relation network shown in Fig. 2: embodiment according to one aspect, line (such as line 24) Can be it is nondirectional, as long as recognizing specifically, one in user 21 and user 22 has carried out operation associated to another Mutually there is incidence relation for them;According to the embodiment of another aspect, line (such as line 24) is also possible to directive, tool For body, user 21 has carried out operation associated (such as transferring accounts) to user 22, then user 22 is that have incidence relation with user 21 User, and user 21 be not necessarily with user 22 have incidence relation user.For ease of description, in this specification embodiment with Line is directionless to be illustrated.
In human relation network shown in Fig. 2, user 21, user 23 etc. have direct correlation relationship with user 22, It can be the level-one association user of user 22.User 22 is the level-one association user of user 21, but user 23 and user 21 do not have Direct correlation relationship, therefore, user 23 and user 21 are not mutually the level-one association users of other side.
Design according to one aspect, above-mentioned text information can be phrase, sentence information.For example, when operation associated When being plusing good friend, above-mentioned text information can be verifying message when sending addition good friend's request, such as " owing you people of money ", or Person is the remarks or label for good friend's setting, such as " confirmed gambler ", " braggart ", " honest " etc..When it is operation associated be to transfer accounts Or when giving bonus, above-mentioned text information can be message sentence " credit card repayment ", " rent next month " etc..In some implementations In example, above-mentioned text information can also be the keyword extracted from the information such as message, remarks, label, such as: " credit card ", " refund ", " next month ", " rent ", " confirmed gambler ", " boast ", " king ", " honest " etc..
According to the design of another aspect, above-mentioned text information can also be the conjunctive word extracted from phrase, sentence information It converges.When above-mentioned text information is association vocabulary, the information such as message, remarks, label in predetermined operation can be first passed through The segmenting method of such as N-gram, Bigram etc carry out word cutting processing, obtain initial vocabulary.By taking Bigram as an example, for staying Say " dinner cost at noon ", participle the result is that " noon ", " noon ", " meal ", " dinner cost ".Then, participle can be obtained Initial vocabulary is matched with the keyword in pre-generated keyword set respectively, by what is be matched in keyword set Initial vocabulary conduct, for record the relating attribute between user it is operation associated in text information.
Wherein, the keyword in above-mentioned keyword set can extract in the following manner: firstly, selection user's positive sample With user's negative sample, user's positive sample and user's negative sample can be the user's samples artificially demarcated.For example, for some money The user of gold management platform (such as Alipay) checks its credit card repayment record or the creditor-debtor entry in the platform, if letter With good, such as refund on time every time, be then demarcated as user's positive sample, whereas if credit is bad, such as occur refunding it is overdue, Situations such as debt-credit is not gone back, then be demarcated as user's negative sample.
Then, it for user's positive sample and user's negative sample, respectively by above-mentioned human relation network, obtains and other use Sample text information in the relating attribute at family, and word cutting processing is carried out, obtained word cutting vocabulary is added crucial as keyword Set of words.Optionally, obtained word cutting vocabulary can also be filtered, such as removal stop words, function word, by remaining word It converges and keyword set is added.
In one embodiment, the keyword in above-mentioned keyword set can also respectively correspond statistical indicator, as Their statistical parameters in user's positive and negative samples.Statistical indicator can include but is not limited at least one of following: user just The number that occurs in the text information of sample, is biased to user's positive sample at the number occurred in the text information of user's negative sample Probability, be biased to the probability, etc. of user's negative sample.When user's positive and negative samples quantity is consistent, vocabulary is in user's positive sample The number occurred in text information and/or the number occurred in the text information of user's negative sample are biased to positive (negative) sample of user This probability all can serve as their statistical indicator.At this point, vocabulary is biased to user by taking the probability for being biased to user's positive sample as an example The probability of positive sample=number occurred in the text information of user's positive sample/(goes out in the text information of user's positive sample The number for existing number+occur in the text information of user's negative sample).It, can be with when user's positive and negative samples quantity is inconsistent The probability of positive (negative) sample of user is biased to as statistical indicator by vocabulary.It is false still for being biased to the probability of user's positive sample If user's positive and negative samples quantity ratio is M:N, then vocabulary is biased to probability=N × vocabulary of user's positive sample in user's positive sample The number occurred in text information/(N × number+M occurred in the text information of user's positive sample × in user's negative sample The number occurred in text information).
It is readily appreciated that, the number that vocabulary occurs in the text information of user's positive sample is more, is biased to user's positive sample Probability is bigger, is easier to be biased to user's positive sample, the number occurred in the text information of user's negative sample is more, is biased to use The probability of family positive sample is smaller, is easier to be biased to user's negative sample.For user's positive and negative samples of equivalent, in the positive sample of user The number difference occurred in the text information of this and user's negative sample is smaller (being less than preset times), or for arbitrarily distributing number User's positive and negative samples of amount, are biased to vocabulary of the probability in middle position (near such as 0.5) of positive (negative) sample of user, and vocabulary is inclined It is all weaker to user's positive sample and the trend for being biased to user's negative sample, it is believed that be neutral words.For neutral vocabulary, from statistics For, when user's sample size is sufficiently large, it is biased to a range of the Probabilistic Stability of positive (negative) sample of user near 0.5 It is interior, however, if the positive sample of user may be biased in primary divide using 0.5 as the boundary probability for being biased to user's positive and negative samples This, may also be biased to user's negative sample in another division, influence the stability of data processing.Therefore, in one embodiment In, it may filter out such neutral words, such as be biased to the probability of positive (negative) sample of user in preset range (such as 0.4-0.6 In) vocabulary improve the stability of data processing to keep the vocabulary in keyword set more stable.
It, can also be according to it in user's positive sample for the keyword in keyword set in a possible design Text information in the number occurred, the number that occurs in the text information of user's negative sample or be biased to positive (negative) sample of user This probability distinguishes positive vocabulary and negative sense vocabulary.Wherein, positive vocabulary is that occur in the text information of user's positive sample Number is on the high side, or is biased to the biggish vocabulary of probability of user's positive sample, and negative sense vocabulary is the text information in user's negative sample The number of middle appearance is on the high side, or is biased to the lesser vocabulary of probability of user's positive sample.
The incidence relation for being associated operation between user is made full use of by human relation network, not only can clearly be used Incidence relation between family, can also be according to the relating attribute between operation associated corresponding text information record user.Such as Fig. 2 Shown, user to be assessed may have level-one incidence relation with multiple users, for ease of description, in this specification embodiment It is described by some level-one association user of user to be assessed.In other words, " described in this specification embodiment " first " in one level-one association user ", is not offered as sequence or quantity, but refer to one of them, some.
Step 32, according to the text information in the relating attribute of user to be assessed and the first level-one association user, obtain to At least one level-one for assessing user is associated with vocabulary, and generates the first of user to be assessed based at least one level-one association vocabulary Level-one linked character.It is appreciated that the case where above-mentioned text information is the association vocabulary extracted from phrase, sentence information Under, at least one level-one that can directly acquire these association vocabulary as user to be assessed is associated with vocabulary.In above-mentioned text envelope In the case that breath is phrase, sentence information, phrase, sentence letter can be extracted according to the method above-mentioned for extracting association vocabulary Association vocabulary in breath, at least one level-one as user to be assessed are associated with vocabulary, it may be assumed that cut to phrase, sentence information Word obtains initial vocabulary;Each initial vocabulary is matched with the keyword in pre-generated keyword set respectively;It will The initial vocabulary being matched in keyword set is associated with vocabulary as level-one.Details are not described herein.
It is associated with vocabulary according at least one level-one of user to be assessed, the level-one that can further generate user to be assessed is closed Join feature.Wherein, level-one linked character can be for embodying the single people between user to be assessed and its level-one association user The feature of border relationship.
In one embodiment, the vocabulary in keyword set is corresponding with statistical indicator, since each level-one is associated with vocabulary It is the vocabulary being matched in keyword set, so each level-one association vocabulary also corresponds to the statistics of be matched to keyword Index.At this point it is possible to according to the corresponding statistical indicator of each level-one of user to be assessed and level-one association user association vocabulary into The first predetermined process of row generates level-one linked character.First predetermined process for example can be the statistical indicator to each keyword Summation, maximum value, minimum value, average value, seek weighted sum etc..For weighted sum, it is assumed that user to be assessed and one It having carried out transferring accounts for 2 times between level-one association user, the message transferred accounts for 2 times is " dinner cost of today ", " the deficient dinner cost of institute's last month " respectively, Acquired level-one association vocabulary is, for example: " dinner cost ", the corresponding Probability p for being biased to positive sample1=0.7, frequency of occurrence c1=2; " today ", the corresponding Probability p for being biased to positive sample2=0.7, frequency of occurrence c2=1;" last month ", the corresponding Probability p for being biased to positive sample3 =0.3, frequency of occurrence c3=1.Then calculate the level-one linked character that user to be assessed corresponds to the level-one association user are as follows: (p1× c1+p2×c2+p3×c3)/(c1+c2+c30.7 × 2+0.7+0.3 of)=()/(2+1+1).
In another embodiment, the keyword in keyword set can be corresponding with positive vocabulary and negative sense vocabulary mark Label, so each level-one association vocabulary is also corresponding with positive vocabulary and negative sense vocabulary label.At this point, can also count it is above-mentioned at least Positive vocabulary number in one level-one conjunctive word determines level-one linked character according to positive (negative) specific gravity to vocabulary.Also join It is illustrated according to example above.It is assumed that " dinner cost " is positive vocabulary 1, frequency of occurrence c1=2;" today " is positive vocabulary 2, out Occurrence number c2=1;" last month " is negative sense vocabulary 1, frequency of occurrence c3=1.The specific gravity of positive vocabulary may is that positive vocabulary Number/(positive vocabulary number+negative sense vocabulary number)=2/ (2+1).It can also be using the frequency of occurrence of level-one conjunctive word as weight It is calculated, the specific gravity of positive vocabulary may is that (positive 1 frequency of occurrence c of vocabulary12 frequency of occurrence c of+positive vocabulary2)/(is positive 1 frequency of occurrence c of vocabulary12 frequency of occurrence c of+positive vocabulary21 frequency of occurrence c of+negative sense vocabulary3)=(2+1)/(2+1+1).
In this way, the level-one linked character that user to be assessed corresponds to each level-one association user can be generated.Optionally, also These level-one linked characters can be added to the level-one linked character set of user to be assessed.
Step 33, it is based on human relation network, determines at least one second level association user of user to be assessed, wherein on Stating at least one second level association user and the first level-one association user has incidence relation.It is appreciated that with user's to be assessed Level-one association user has the user of incidence relation, can be referred to as the second level association user of user to be assessed.By taking Fig. 2 as an example, User 22 is the level-one association user of user 21, and user 23, user 26 etc. with user 22 there is the user of direct correlation relationship to be The second level association user of user 21.It optionally, further include user 21 with the associated user of user 22, in the second level for determining user 21 When association user, itself can also be excluded.
In this way, being directed to each corresponding level-one association user of user to be assessed, available user to be assessed is at least One second level association user.
Step 34, according to the text information in the relating attribute of the first level-one association user and each second level association user, At least one second level association vocabulary of user to be assessed is obtained, and user to be assessed is generated based at least one second level association vocabulary The first second level linked character.Wherein, " first " in " the first second level linked character " here is for expressing and the " the 1st The corresponding relationship of grade association user ", rather than the restriction to serial number or quantity.
It is appreciated that some level-one association user and any one second level association user associated there, in above-mentioned text In the case that this information is the keyword extracted from phrase, sentence information, these keywords can be directly acquired as to be evaluated Estimate at least one second level association vocabulary of user.It, can be according to preceding in the case where above-mentioned text information is phrase, sentence information The method for the extraction keyword stated extracts phrase, the keyword in sentence information, as user to be assessed at least one two Grade association vocabulary, details are not described herein.
It is associated with vocabulary according at least one second level of user to be assessed, the second level that can further generate user to be assessed is closed Join feature.Wherein, second level linked character can be used for embodying the whole man of some corresponding level-one association user of user to be assessed The feature of border relationship.
It is appreciated that user to be assessed is any user in human relation network, and therefore, the level-one with user to be assessed Similarly, for any one level-one association user of user to be assessed, its level-one linked character (side also can be generated in linked character Method is with described previously), the spy of the single interpersonal relationships to indicate the level-one association user and some above-mentioned second level association user Sign.And the second level linked character that user to be assessed corresponds to the level-one association user can be, the level-one of the level-one association user The comprehensive of linked character embodies.
In this way, according to an implementation, for any one corresponding level-one of user to be assessed (user 21 in such as Fig. 2) Association user (user 22 in such as Fig. 2) can firstly generate each of the level-one association user (user 22 in such as Fig. 2) Level-one linked character (the level-one linked character of such as user 22 and user 23, user 22 and the level-one linked character of user 26, user 22 and user 27 level-one linked character), then these level-ones of the level-one association user (user 22 in such as Fig. 2) are associated with Feature carries out the second predetermined process, to generate the second level linked character of user to be assessed (user 21 in such as Fig. 2).Its In, which can include but is not limited to: summation, minimum value, average value, seeks weighted sum etc. at maximum value.In the hope of For weighted sum, each second level association user can be corresponded to, one weight is set, the weight can with the level-one association user with Operation associated number between corresponding second level association user is positively correlated, be also possible to and with the level-one association user associated two Quantity (such as N, N are positive integer) negatively correlated (such as 1/N) of grade association user, etc., as long as second level association user can rationally be embodied Different degree, this specification embodiment is not construed as limiting this.
According to another embodiment, can also for user to be assessed (user 21 in such as Fig. 2) it is corresponding any one Level-one association user (user 22 in such as Fig. 2) obtains the level-one association user (user 22 in such as Fig. 2) and each second level The corresponding level-one of text information in the relating attribute of association user (such as user 23, user 26 with user 27) is associated with vocabulary, and These level-ones association vocabulary is summarized, and their statistical indicator is carried out including but not limited to below at least one pre- Fixed processing, to generate the second level linked character of user to be assessed (user 21 in such as Fig. 2): summation, maximum value, minimum Value, average value seek weighted sum etc..Wherein, in the hope of weighted sum, each level-one is associated with the corresponding weight of vocabulary can be with this Level-one conjunctive word remittance frequency of occurrence is positively correlated.
In this way, can correspond to each level-one association user of user to be assessed, a second level linked character is generated respectively. Optionally, these second level linked characters can also be added to the second level linked character set of user to be assessed.
Step 35, it is at least based on the first level-one linked character and the first second level linked character, passes through credit trained in advance Assessment models assess the credit rating of user to be assessed.It is appreciated that " the first level-one linked character " with " the first second level is associated with spy The feature that sign " is generated both for some corresponding level-one association user of user to be assessed, in fact, being directed to user to be assessed Each corresponding level-one association user can correspond to and generate a level-one linked character and a second level linked character.
The credit evaluation model that above-mentioned level-one linked character and the input of second level linked character can be trained in advance, to obtain The output of credit evaluation model is as a result, determine the credit rating of user to be assessed according to the output result.As shown in figure 4, at least will Level-one linked character 41 and second level linked character 42 input credit evaluation model, obtain output result.If there are also other features, Such as user identity feature, probability characteristics of honouring an agreement, can be by above-mentioned level-one linked character and second level linked character and other features Credit evaluation model trained in advance is inputted, together with the credit rating of determination user to be assessed.
It is appreciated that in the case where user to be assessed only corresponds to a level-one association user and other no features, it can To be based on above-mentioned first level-one linked character and the first second level linked character, by credit evaluation model assessment trained in advance to Assess the credit rating of user.
Wherein, credit evaluation model can be such as scorecard model, Random Forest model of training in advance, gradient is promoted The classification of decision tree (Gradient Boosting Decision Tree, GBTD) model etc or scoring model, herein no longer It repeats.The output result of credit evaluation model can be classification results, such as the good user of credit, the bad user of credit etc.;? Marking be can be as a result, such as any score in 1-1000;Etc., this specification embodiment is not construed as limiting this.This is defeated Result can also be further processed work after (such as normalized) directly as the credit rating assessment result of user out For the credit rating assessment result of user.
It, can be according to user's to be assessed in the case where above-mentioned credit rating result uses credit field in practical application Credit rating sets debt-credit condition, for example, credit score is lower than 600, can not carry out debt-credit activity, etc..
Above procedure is looked back, during carrying out credit evaluation to user, based on the associated data between user, sufficiently benefit Text information when with the incidence relation between user and incidence relation occurs.Specifically, based on user to be assessed and directly Associated level-one association user occurs corresponding text information when incidence relation and generates level-one linked character, is associated with and is used according to level-one The family second level that corresponding text information generates user to be assessed when incidence relation occurs with the second level association user of direct correlation is closed Join feature.Using the level-one linked character and second level linked character as a part of the feature of credit evaluation model, due to considering There is influence of the other users of incidence relation to user credit to be assessed with user to be assessed, comment so as to improve credit The accuracy estimated.
According to the embodiment of another aspect, a kind of device of credit evaluation is also provided.Fig. 5 is shown according to one embodiment The schematic block diagram of device for credit evaluation.As shown in figure 5, the device 500 for credit evaluation includes: first determining single Member 51 is configured to the determining first level-one association user with user to be assessed with incidence relation of human relation network, In, human relation network establishes incidence relation between the user for being associated operation, and passes through operation associated corresponding text There are the relating attributes between the user of incidence relation for information record;First generation unit 52, is configured to according to user to be assessed With the text information in the relating attribute of the first level-one association user, at least one level-one conjunctive word of user to be assessed is obtained It converges, and generates the first level-one linked character of user to be assessed based at least one level-one association vocabulary;Second determination unit 53, It is configured to human relation network, determines at least one second level association user of user to be assessed, wherein second level association user There is incidence relation with the first level-one association user;Second generation unit 54 is configured to according to the first level-one association user and each Text information in the relating attribute of a second level association user obtains at least one second level association vocabulary of user to be assessed, and The first second level linked character of user to be assessed is generated based at least one second level association vocabulary;Credit evaluation unit 55, matches It is set at least based on the first level-one linked character and the first second level linked character, is assessed by credit evaluation model trained in advance The credit rating of user to be assessed.
In the present embodiment, the first determination unit 51 can have pass with user to be assessed based on human relation network is determining The level-one association user of connection relationship.Wherein, human relation network is the network structure based on the operation associated foundation between user, For recording the incidence relation between user and user.There can also be relating attribute between user, which passes through upper State operation associated corresponding text information record.In practice, operation associated between user for example can include but is not limited to At least one of lower: plusing good friend transfers accounts, gives bonus, etc..
According to one embodiment, the text information recorded in above-mentioned relating attribute is when being associated operation between user Phrase, sentence information.First generation unit 52 can extract association vocabulary as at least one from phrase, sentence information Level-one is associated with vocabulary.Specifically, the first generation unit 52 can be with: carrying out word cutting to phrase, sentence information and obtains initial vocabulary;It will Each initial vocabulary is matched with the keyword in pre-generated keyword set respectively;It will be matched in keyword set The initial vocabulary arrived is associated with vocabulary as level-one.
According to another embodiment, the text information recorded in above-mentioned relating attribute is from above-mentioned phrase, sentence information The association vocabulary of middle extraction, the first generation unit 52 can directly acquire recorded association vocabulary and close as at least one level-one Join vocabulary.
According to a possible design, device 500 can also include keyword determination unit (not shown), be configured to pass through Following methods extract the keyword in keyword set: obtaining the user's positive sample artificially demarcated and user's negative sample;Based on people Border relational network determines the sample text information in user's positive sample and user's negative sample and the relating attribute of other users;According to Sample text information determines the keyword in keyword set.
In one embodiment, each keyword in keyword set can also be corresponding with statistical indicator.Statistical indicator It may include at least one of following: the number that occurs in the text information of user's positive sample, in the text envelope of user's negative sample The probability of the number, deviation user's positive sample that occur in breath, the probability for being biased to user's negative sample.
According to a kind of embodiment, each level-one association vocabulary is corresponding with the keyword being matched in keyword set Statistical indicator;And for some level-one association user, the first generation unit 52 is also configured as: being obtained each level-one and is closed Join the corresponding statistical indicator of vocabulary;First predetermined process is carried out to the corresponding statistical indicator of each level-one association vocabulary, generates one Grade linked character, wherein the first predetermined process include at least one of the following: maximizing, minimize, sum, averaging, Seek weighted sum.
In the present embodiment, the second determination unit 53 can be based on human relation network, for 51 institute of the first determination unit Determining each level-one association user determines at least one second level association user of user to be assessed, wherein second level association user There is incidence relation with the level-one association user.
Second generation unit 54 can be directed to each level-one association user, according to second level determined by the second determination unit 53 Association user is obtained at least one second level association vocabulary of user to be assessed, and is generated based at least one second level association vocabulary The second level linked character of user to be assessed.
In further carrying out example, the second generation unit 54 may include:
First generation module is configured to be associated with vocabulary for each level-one association user according to second level and generate level-one association User and the corresponding second level-one linked character of each second level association user;
Second generation module is configured to each second level-one linked character corresponding to each level-one association user and carries out the Two predetermined process, using processing result as the second level linked character of user to be assessed, wherein the second predetermined process include with At least one of lower: maximizing minimizes, sums, averaging, seeking weighted sum.
Credit evaluation unit 55 can be generated at least based on the level-one linked character and second that the first generation unit 52 generates The second level linked character that unit 54 generates assesses the credit rating of user to be assessed by credit evaluation model trained in advance.
It is worth noting that device 500 shown in fig. 5 is that device corresponding with the embodiment of the method shown in Fig. 3 is implemented , the corresponding description in the embodiment of the method shown in Fig. 3 is equally applicable to device 500, and details are not described herein.
By apparatus above, using the level-one linked character and second level linked character as the one of the feature of credit evaluation model Part has influence of the other users of incidence relation to user credit to be assessed with user to be assessed due to considering, thus The accuracy of credit evaluation can be improved.
According to the embodiment of another aspect, a kind of computer readable storage medium is also provided, is stored thereon with computer journey Sequence enables computer execute method described in conjunction with Figure 3 when the computer program executes in a computer.
According to the embodiment of another further aspect, a kind of calculating equipment, including memory and processor, the memory are also provided In be stored with executable code, when the processor executes the executable code, realize the method in conjunction with described in Fig. 3.
Those skilled in the art are it will be appreciated that in said one or multiple examples, function described in the invention It can be realized with hardware, software, firmware or their any combination.It when implemented in software, can be by these functions Storage in computer-readable medium or as on computer-readable medium one or more instructions or code transmitted.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention Protection scope, all any modification, equivalent substitution, improvement and etc. on the basis of technical solution of the present invention, done should all Including within protection scope of the present invention.

Claims (18)

1. a kind of method of credit evaluation, which comprises
Based on the determining first level-one association user with user to be assessed with incidence relation of human relation network, wherein described Human relation network establishes incidence relation between the user for being associated operation, and passes through the operation associated corresponding text There are the relating attributes between the user of incidence relation for information record;
According to the text information in the relating attribute of the user to be assessed and the first level-one association user, institute is obtained At least one level-one association vocabulary of user to be assessed is stated, and described to be evaluated based at least one level-one association vocabulary generation Estimate the first level-one linked character of user;
Based on the human relation network, determine at least one second level association user of the user to be assessed, wherein it is described extremely A few second level association user and the first level-one association user have incidence relation;
According to the text information in the relating attribute of the first level-one association user and each second level association user, obtain At least one second level of the user to be assessed is associated with vocabulary, and based at least one second level association vocabulary generate it is described to Assess the first second level linked character of user;
It is at least based on the first level-one linked character and the first second level linked character, passes through credit evaluation trained in advance The credit rating of user to be assessed described in model evaluation.
2. according to the method described in claim 1, wherein, the text information includes the operation associated corresponding phrase, language Sentence information;And
The text information according in the relating attribute of the user to be assessed and the first level-one association user, is obtained Take the user to be assessed at least one level-one be associated with vocabulary include:
Word cutting is carried out to the phrase, sentence information and obtains initial vocabulary;
Each initial vocabulary is matched with the keyword in pre-generated keyword set respectively;
Vocabulary is associated with using the initial vocabulary being matched in the keyword set as the level-one.
3. according to the method described in claim 2, wherein, the keyword in the keyword set extracts by the following method:
Obtain the user's positive sample artificially demarcated and user's negative sample;
It is determined in user's positive sample and user's negative sample and the relating attribute of other users based on human relation network Sample text information;
The keyword in keyword set is determined according to the sample text information.
4. according to the method described in claim 3, wherein, each keyword in the keyword set is also corresponding with statistics and refers to Mark, the statistical indicator include at least one of the following: the number occurred in the text information of user's positive sample, in the negative sample of user The probability of the number, deviation user's positive sample that occur in this text information, the probability for being biased to user's negative sample.
5. according to the method described in claim 4, wherein, each level-one association vocabulary is corresponding in the keyword set The statistical indicator for the keyword being fitted on;And
It is described the first level-one linked character of the user to be assessed is generated based at least one described level-one association vocabulary to include:
Obtain the corresponding statistical indicator of each level-one association vocabulary;
First predetermined process is carried out to the corresponding statistical indicator of each level-one association vocabulary, it is special to generate the first level-one association Sign, wherein first predetermined process include at least one of the following: maximizing, minimize, sum, averaging, ask plus Quan He.
6. according to the method described in claim 1, wherein, the text information includes, from the operation associated corresponding phrase, The association vocabulary extracted in advance in sentence information;And
The text information according in the relating attribute of the user to be assessed and the first level-one association user, is obtained Take the user to be assessed at least one level-one be associated with vocabulary include:
Vocabulary is associated with using the text information as at least one level-one.
7. according to the method described in claim 1, wherein, it is described based at least one second level association vocabulary generate it is described to Assessment user the first second level linked character include:
It is associated with vocabulary according to the second level, the first level-one association user is generated and each second level association user is corresponding Second level-one linked character;
Second predetermined process is carried out to each second level-one linked character, using processing result as the first of the user to be assessed Second level linked character, wherein second predetermined process includes at least one of the following: maximizing, minimizes, sums, asking Average value seeks weighted sum.
8. according to the method described in claim 1, wherein, the predetermined operation include: transfer accounts, give bonus, plusing good friend.
9. a kind of device of credit evaluation, described device include:
First determination unit is configured to determining first level-one with user to be assessed with incidence relation of human relation network Association user, wherein the human relation network establishes incidence relation between the user for being associated operation, and by described There are the relating attributes between the user of incidence relation for operation associated corresponding text information record;
First generation unit is configured to according in the relating attribute of the user to be assessed and the first level-one association user The text information, at least one level-one for obtaining the user to be assessed are associated with vocabulary, and based at least one described level-one Association vocabulary generates the first level-one linked character of the user to be assessed;
Second determination unit is configured to the human relation network, determines at least one second level of the user to be assessed Association user, wherein at least one described second level association user and the first level-one association user have incidence relation;
Second generation unit is configured to according in the relating attribute of the first level-one association user and each second level association user The text information, at least one second level for obtaining the user to be assessed is associated with vocabulary, and based on it is described at least one two Grade association vocabulary generates the first second level linked character of the user to be assessed;
Credit evaluation unit is configured at least lead to based on the first level-one linked character and the first second level linked character The credit rating of the user to be assessed is assessed after credit evaluation model trained in advance.
10. device according to claim 9, wherein the text information includes, the operation associated corresponding phrase, Sentence information;And
First generation unit is further configured to:
Word cutting is carried out to the phrase, sentence information and obtains initial vocabulary;
Each initial vocabulary is matched with the keyword in pre-generated keyword set respectively;
Vocabulary is associated with using the initial vocabulary being matched in the keyword set as the level-one.
11. device according to claim 10, wherein keyword determination unit is configured to extract institute by the following method State the keyword in keyword set:
Obtain the user's positive sample artificially demarcated and user's negative sample;
It is determined in user's positive sample and user's negative sample and the relating attribute of other users based on human relation network Sample text information;
The keyword in keyword set is determined according to the sample text information.
12. device according to claim 11, wherein each keyword in the keyword set is also corresponding with statistics Index, the statistical indicator include at least one of the following: the number occurred in the text information of user's positive sample, bear in user The probability of the number, deviation user's positive sample that occur in the text information of sample, the probability for being biased to user's negative sample.
13. device according to claim 12, wherein each level-one association vocabulary is corresponding in the keyword set The statistical indicator for the keyword being matched to;And
First generation unit is additionally configured to:
Obtain the corresponding statistical indicator of each level-one association vocabulary;
First predetermined process is carried out to the corresponding statistical indicator of each level-one association vocabulary, it is special to generate the first level-one association Sign, wherein first predetermined process include at least one of the following: maximizing, minimize, sum, averaging, ask plus Quan He.
14. device according to claim 9, wherein the text information includes, from described operation associated corresponding short The association vocabulary extracted in advance in language, sentence information;And
First generation unit is further configured to:
Vocabulary is associated with using the text information as at least one level-one.
15. device according to claim 9, wherein second generation unit includes:
First generation module is configured to be associated with vocabulary according to the second level, generates the first level-one association user and each two The corresponding second level-one linked character of grade association user;
Second generation module, be configured to each second level-one linked character carry out the second predetermined process, using processing result as The first second level linked character of the user to be assessed, wherein second predetermined process, which includes at least one of the following:, asks maximum It is worth, minimize, sum, average, seeks weighted sum.
16. device according to claim 9, wherein the predetermined operation include: transfer accounts, give bonus, plusing good friend.
17. a kind of computer readable storage medium, is stored thereon with computer program, when the computer program in a computer When execution, computer perform claim is enabled to require the method for any one of 1-8.
18. a kind of calculating equipment, including memory and processor, which is characterized in that be stored with executable generation in the memory Code realizes method of any of claims 1-8 when the processor executes the executable code.
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