CN109658193A

CN109658193A - Determination method, apparatus, electronic equipment and the storage medium of system object importance

Info

Publication number: CN109658193A
Application number: CN201811565644.XA
Authority: CN
Inventors: 郁延书
Original assignee: Lazhasi Network Technology Shanghai Co Ltd
Current assignee: Rajax Network Technology Co Ltd; Lazhasi Network Technology Shanghai Co Ltd
Priority date: 2018-12-20
Filing date: 2018-12-20
Publication date: 2019-04-19

Abstract

The embodiment of the present disclosure discloses determination method, apparatus, electronic equipment and the storage medium of a kind of system object importance.This method comprises: determining the multidimensional characteristic of system object；Based on training algorithm, the corresponding feature weight of the multidimensional characteristic of the system object is determined；According to the multidimensional characteristic and feature weight of the system object, the importance of the system object is determined.The embodiment of the present disclosure measures the importance of system object using the multidimensional characteristic of system object, increase the accuracy rate of the importance data of system object, the feature weight of multidimensional characteristic is also determined using minimal error rate training algorithm simultaneously, can effectively avoid manually set system object special efficacy weight and bring subjectivity so that the importance data of system object are more objective and accurate.

Description

Determination method, apparatus, electronic equipment and the storage medium of system object importance

Technical field

This disclosure relates to field of computer technology, and in particular to a kind of determination method, apparatus of system object importance, electricity Sub- equipment and storage medium.

Background technique

With the development of information technology, e-commerce has been deep into every field, occurs numerous information on network Platform, such as platform of ordering is taken out, the provider of information, product on these information platforms etc. by being on information platform Object of uniting provides various services, uses for user's on-line customization.Based on the difference of service provided by system object, each system The user group that object is agglomerated is also different.And for information platform, it is most important that number of users and user characteristics Data.Therefore, more users and user characteristic data can be brought for information platform, then system object is for information platform For it is then more important.Therefore, the importance of system object how is determined, and then according to the importance of system object to facilitate The long-run development of information platform be target and the technical issues of take corresponding strategy, be current information platform urgent need to resolve it One.

Summary of the invention

The embodiment of the present disclosure provides the method, apparatus of system object importance a kind of, electronic equipment and computer-readable deposits Storage media.

In a first aspect, providing a kind of method of system object importance in the embodiment of the present disclosure.

Specifically, the method for the system object importance, comprising:

Determine the multidimensional characteristic of system object；

Based on training algorithm, the corresponding feature weight of the multidimensional characteristic of the system object is determined；

According to the multidimensional characteristic and feature weight of the system object, the importance of the system object is determined.

With reference to first aspect, the disclosure determines that the multidimensional of system object is special in the first implementation of first aspect Sign, comprising:

Determine the primitive character value of the multidimensional characteristic of the system object；

The primitive character value is normalized, determines the normalization characteristic value of the multidimensional characteristic.

With reference to first aspect and/or the first implementation of first aspect, determine that the normalization of the multidimensional characteristic is special Value indicative, comprising:

In the importance correlation of the multidimensional characteristic and the system object, the normalizing of the multidimensional characteristic Change the ratio that characteristic value is determined as the primitive character value Yu the system object number；

In the importance negative correlation of the multidimensional characteristic and the system object, the normalizing of the multidimensional characteristic Change characteristic value and is determined as 1 ratio for subtracting the primitive character value Yu the system object number.

The first implementation with reference to first aspect, the disclosure in second of implementation of first aspect, according to The multidimensional characteristic and feature weight of the system object, determine the importance of the system object, comprising:

The system pair is determined after being weighted according to normalization characteristic value of the feature weight to the multidimensional characteristic The importance of elephant.

Second of implementation with reference to first aspect, the disclosure in the third implementation of first aspect, according to The feature weight determines the importance of the system object, packet after being weighted to the normalization characteristic value of the multidimensional characteristic It includes:

It is calculate by the following formula the importance values of the system object i:

Wherein, N indicates the characteristic dimension number of the system object i, w_jIndicate j-th of feature in the multidimensional characteristic Feature weight；f_score_ijIndicate the normalization characteristic value of j-th of feature of system object i.

With reference to first aspect, the first implementation of first aspect, first aspect second of implementation or first The third implementation of aspect, the disclosure in the 4th kind of implementation of first aspect, be based on training algorithm, determine described in The corresponding feature weight of the multidimensional characteristic of system object, comprising:

Obtain training sample；Wherein, the training sample include multiple system objects between any two importance One comparison result；

It is weighed using the training sample and minimal error rate the training algorithm feature corresponding to the multidimensional characteristic It is trained again, and then determines the corresponding feature weight of the multidimensional characteristic.

The 4th kind of implementation with reference to first aspect, the disclosure utilize in the 5th kind of implementation of first aspect The training sample and the minimal error rate training algorithm feature weight corresponding to the multidimensional characteristic are trained, into And determine the corresponding feature weight of the multidimensional characteristic, comprising:

For the corresponding target signature weight of target signature in the multidimensional characteristic, other in the multidimensional characteristic In the case that the corresponding feature weight of feature is constant, determine multiple system objects in any value of target signature weight Under candidate importance；

According in the training sample first comparison result and the candidate importance determine that the target is special Levy weight.

The 5th kind of implementation with reference to first aspect, the disclosure in the 6th kind of implementation of first aspect, according to First comparison result and the candidate importance in the training sample determine the target signature weight, comprising:

Determine candidate feature weight when the candidate importance of at least two system objects is equal；

Determine under the candidate feature weight the second comparison result of the candidate importance of system object two-by-two；

Determine the degree that is consistent of second comparison result, first comparison result corresponding with the training sample； Wherein, the size of the degree that is consistent is related to the identical quantity of the second comparison result and the first comparison result；

The highest candidate feature weight of the degree that is consistent is determined as the target signature weight.

With reference to first aspect, the first implementation, second of implementation of first aspect, first party of first aspect The 6th kind of implementation of the third implementation in face, the 5th kind of implementation of first aspect or first aspect, the disclosure In the 7th kind of implementation of first aspect, the multidimensional characteristic of the system object includes at least user at nearest first Between multiple purchase rate in the period, the average visitor of user be monovalent, new objective conversion ratio, the new number of users of cohesion, single user resources amount, new user Single stock number and whether two or more in chain trade company.

With reference to first aspect, the first implementation, second of implementation of first aspect, first party of first aspect The 6th kind of implementation of the third implementation in face, the 5th kind of implementation of first aspect or first aspect, the disclosure In the 8th kind of implementation of first aspect, the method also includes: it is determined and is used according to the importance of the system object The customer parameter of the system object.

The 8th kind of implementation with reference to first aspect, the disclosure in the 9th kind of implementation of first aspect, according to The importance of the system object determines the customer parameter using the system object, comprising:

It determines each discrete value of the customer parameter, and determines each discrete value in each discrete value institute Corresponding multiple first fractional bits in range；

Multiple system objects are ranked up according to the importance, and determine the sorting position of the system object Corresponding second fractional bits in entire sequence；

The customer parameter for using the system object is determined as to first fractional bits pair equal with second fractional bits The discrete value answered.

Second aspect, the embodiment of the present disclosure provide a kind of determining device of system object importance, comprising:

First determining module is configured to determine that the multidimensional characteristic of system object；

Second determining module is configured as determining the corresponding spy of the multidimensional characteristic of the system object based on training algorithm Levy weight；

Third determining module is configured as multidimensional characteristic and feature weight according to the system object, determines the system The importance of system object.

In conjunction with second aspect, the disclosure is in the first implementation of second aspect, the first determining module, comprising:

First determines submodule, is configured to determine that the primitive character value of the multidimensional characteristic of the system object；

Second determines submodule, is configured as that the primitive character value is normalized, and determines that the multidimensional is special The normalization characteristic value of sign.

In conjunction with the first of second aspect and/or second aspect implementation, described second determines submodule, comprising:

First normalization submodule, is configured as being positively correlated in the importance of the multidimensional characteristic and the system object When relationship, the normalization characteristic value of the multidimensional characteristic is determined as the ratio of the primitive character value and the system object number Value；

Second normalization submodule, is configured as negatively correlated in the importance of the multidimensional characteristic and the system object When relationship, the normalization characteristic value of the multidimensional characteristic is determined as 1 and subtracts the primitive character value and the system object number Ratio.

In conjunction with the first implementation of second aspect, the disclosure is in second of implementation of second aspect, third Determining module, comprising:

Third determines submodule, be configured as according to the feature weight to the normalization characteristic value of the multidimensional characteristic into The importance of the system object is determined after row weighting.

In conjunction with second of implementation of second aspect, the disclosure is in the third implementation of second aspect, third Determine submodule, comprising: computational submodule is configured as being calculate by the following formula the importance values of the system object i:

In conjunction with the first implementation of second aspect, second aspect, second of implementation or second of second aspect The third implementation of aspect, the disclosure is in the 4th kind of implementation of second aspect, the second determining module, comprising:

First acquisition submodule is configured as obtaining training sample；Wherein, the training sample includes multiple systems First comparison result of object importance between any two；

4th determines submodule, is configured as using the training sample and minimal error rate training algorithm to described more The corresponding feature weight of dimensional feature is trained, and then determines the corresponding feature weight of the multidimensional characteristic.

In conjunction with the 4th kind of implementation of second aspect, the disclosure is in the 5th kind of implementation of second aspect, and the 4th Determine submodule, comprising:

5th determines submodule, is configured as the corresponding target signature power of target signature in the multidimensional characteristic Weight, in the case that the corresponding feature weight of other features in the multidimensional characteristic is constant, determines multiple system objects Candidate importance under any value of target signature weight；

6th determine submodule, be configured as according in the training sample first comparison result and the time Importance is selected to determine the target signature weight.

In conjunction with the 4th kind of implementation of second aspect, the disclosure is in the 6th kind of implementation of second aspect, and the 6th Determine submodule, comprising:

7th determines submodule, is configured to determine that time when the candidate importance of at least two system objects is equal Select feature weight；

8th determines submodule, is configured to determine that under the candidate feature weight the candidate importance of system object two-by-two The second comparison result；

9th determines submodule, is configured to determine that second comparison result is corresponding with the training sample described The degree that is consistent of first comparison result；Wherein, the size of the degree that is consistent and the second comparison result and the first comparison result Identical quantity is related；

Tenth determines submodule, is configured as the highest candidate feature weight of the degree that is consistent being determined as the target Feature weight.

The first implementation, second of implementation of second aspect, second party in conjunction with second aspect, second aspect The 6th kind of implementation of the third implementation in face, the 5th kind of implementation of second aspect or second aspect, the disclosure In the 7th kind of implementation of second aspect, the multidimensional characteristic of the system object includes at least user at nearest first Between multiple purchase rate in the period, the average visitor of user be monovalent, new objective conversion ratio, the new number of users of cohesion, single user resources amount, new user Single stock number and whether two or more in chain trade company.

The first implementation, second of implementation of second aspect, second party in conjunction with second aspect, second aspect The 6th kind of implementation of the third implementation in face, the 5th kind of implementation of second aspect or second aspect, the disclosure In the 8th kind of implementation of second aspect, described device further include:

4th determining module is configured as determining the use using the system object according to the importance of the system object Family parameter.

In conjunction with the 8th kind of implementation of second aspect, the disclosure is described in the 9th kind of implementation of second aspect 4th determining module, comprising:

11st determines submodule, is configured to determine that each discrete value of the customer parameter, and determines described each Discrete value corresponding multiple first fractional bits in each discrete value location；

12nd determines submodule, is configured as multiple system objects being ranked up according to the importance, and Determine the sorting position of the system object corresponding second fractional bits in entire sequence；

13rd determines submodule, is configured as to be determined as and described second using the customer parameter of the system object The corresponding discrete value of the first equal fractional bits of fractional bits.

The function can also execute corresponding software realization by hardware realization by hardware.The hardware or Software includes one or more modules corresponding with above-mentioned function.

It include memory and processing in the structure of the determining device of system object importance in a possible design Device, the determining device that the memory is used to store one or more support system object importance execute in above-mentioned first aspect The computer instruction of the determination method of system object importance, the processor is configured to being deposited in the memory for executing The computer instruction of storage.The determining device of the system object importance can also include communication interface, be used for system object weight The determining device and other equipment or communication for the property wanted.

The third aspect, the embodiment of the present disclosure provide a kind of electronic equipment, including memory and processor；Wherein, described Memory is for storing one or more computer instruction, wherein one or more computer instruction is by the processor It executes to realize method and step described in first aspect.

Fourth aspect, the embodiment of the present disclosure provide a kind of computer readable storage medium, are used for storage system object weight Computer instruction used in the determining device for the property wanted, it includes for executing computer involved in method in above-mentioned first aspect Instruction.

After the embodiment of the present disclosure has determined the multidimensional characteristic of system object, system object is further determined that using training algorithm The corresponding feature weight of multidimensional characteristic；And the important of system object is determined according to the multidimensional characteristic of system object and feature weight Property.The technical solution measures the importance of system object using the multidimensional characteristic of system object, increases the important of system object The accuracy rate of property data, while the embodiment of the present disclosure also uses training algorithm to determine the feature weight of multidimensional characteristic, it can be effective Avoid manually set system object special efficacy weight and bring subjectivity so that the importance data of system object are more objective And it is accurate.

It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not The disclosure can be limited.

Detailed description of the invention

In conjunction with attached drawing, by the detailed description of following non-limiting embodiment, the other feature of the disclosure, purpose and excellent Point will be apparent.In the accompanying drawings:

Fig. 1 shows the flow chart of the determination method of the system object importance according to one embodiment of the disclosure；

Fig. 2 shows the flow charts of the step S102 of embodiment according to Fig. 1；

Fig. 3 shows the flow chart of the step S202 of embodiment according to Fig.2,；

Fig. 4 shows the importance values according to 5 system objects of one embodiment of the disclosure about target signature weight Coordinate schematic diagram；

Fig. 5 shows the flow chart of the step S302 of embodiment according to Fig.3,；

Fig. 6 shows the structural block diagram of the determining device of the system object importance according to one embodiment of the disclosure；

Fig. 7 shows the structural block diagram of the second determining module 602 of embodiment according to Fig.6,；

Fig. 8 shows the structural block diagram of the 4th determining submodule 702 of embodiment according to Fig.7,；

Fig. 9 shows the structural block diagram of the 6th determining submodule 802 of embodiment according to Fig.8,；

Figure 10 is adapted for the electricity for realizing the determination method of the system object importance according to one embodiment of the disclosure The structural schematic diagram of sub- equipment.

Specific embodiment

Hereinafter, the illustrative embodiments of the disclosure will be described in detail with reference to the attached drawings, so that those skilled in the art can Easily realize them.In addition, for the sake of clarity, the portion unrelated with description illustrative embodiments is omitted in the accompanying drawings Point.

In the disclosure, it should be appreciated that the term of " comprising " or " having " etc. is intended to refer to disclosed in this specification Feature, number, step, behavior, the presence of component, part or combinations thereof, and be not intended to exclude other one or more features, A possibility that number, step, behavior, component, part or combinations thereof exist or are added.

It also should be noted that in the absence of conflict, the feature in embodiment and embodiment in the disclosure It can be combined with each other.The disclosure is described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

The problem of based on prior art, the embodiment of the present disclosure propose a kind of determination side of system object importance Method, the determination method of the system object importance comprise determining that the multidimensional characteristic of system object；Based on training algorithm, institute is determined State the corresponding feature weight of multidimensional characteristic of system object；According to the multidimensional characteristic and feature weight of the system object, determine The importance of the system object.

Fig. 1 shows the flow chart of the determination method of the system object importance according to one embodiment of the disclosure.Such as Fig. 1 institute Show, the determination method of the system object importance includes the following steps S101-S103:

In step s101, the multidimensional characteristic of system object is determined；

In step s 102, it is based on training algorithm, determines the corresponding feature weight of the multidimensional characteristic of the system object；

In step s 103, according to the multidimensional characteristic and feature weight of the system object, the system object is determined Importance.

Step S101, S102 and S103 will be hereinafter further described respectively.

Step S101

Information platform is as informant by service publication provided by the service providers such as multiple information, product mutual In networking, for the service of each service provider offer of user's on-line customization.Information platform is virtual by different service providers For the system object in information platform, each system object can provide one or more services again on information platform.Such as Taking out platform of ordering virtually is each virtual store taken out in platform of ordering by various food and drink providers, and user logs in outer attraction It eats after platform, can be ordered food into virtual store.Information platform in the embodiment of the present disclosure is not limited to above-mentioned take-away and orders Platform, as long as user oriented, and be capable of providing one or more and possess service product and service product can be provided for user System object platform, all within the scope of the information platform of the embodiment of the present disclosure.

System object can be it is being issued in information platform, possess the virtual right of one or more user oriented services As, and each system object can correspond to real-life one or more service providers.One in information platform is System object can provide one or more services for user, and user can be by provided by custom-built system object on information platform Service, and after user has customized service by information platform, the corresponding service provider of system object can mention for user For real-life service.Service can include but is not limited to information, products in kind, virtual product etc..

Difference, the difference of customer group, the difference of self attributes of system object based on provided service etc. has not Same feature.The embodiment of the present disclosure extracts different characteristic possessed by different system objects, and is determined by the feature of various dimensions Importance of the system object in information platform.User's phase that importance of the system object in information platform is agglomerated with it It closes, such as some system object can bring more users for information platform, then the importance of the system object is higher, if Some system object can not bring user for information platform, then system object importance for information platform is lower.It is different The information platform of property can be different for the definition of the importance of system object, and same information platform can also be based on Different purposes is that system object defines different importance.Regardless of which kind of situation, weight of the system object for information platform The property wanted usually all is embodied in the different characteristic of system object, such as entity businessman is had in the corresponding actual life of system object Some features, the system object for user provide reflected some features in service process after issuing on information platform Deng, which feature can be specifically related to, it is related for actual conditions such as the desired orientations of system object with information platform, it does not do herein Limitation.

It is sold other than below for ordering platform, system object multidimensional involved by the importance of cohesion client's this respect Feature can be as shown in the table:

It will be understood by those skilled in the art that the feature in list above is only to take out the example for platform of ordering, it is different Feature corresponding to information platform, different emphasis can be different.And it will be understood by those skilled in the art that according to the disclosure The importance of the system object of embodiment determines that technical solution can be used for various information platforms, and be not limited to take out order it is flat Platform.

Step S102

The feature of system object can usually quantify namely the feature of each system object can be corresponding with characteristic value, And for different characteristic, different feature power can be determined according to influence degree of the feature for the importance of system object Weight.The present embodiment determines the feature weight of the multidimensional characteristic of system object based on training algorithm.Feature weight can refer to system Weight shared by every one-dimensional characteristic in the importance of object.Feature weight is mainly set by artificial mode in the prior art, But the mode of this artificial settings will cause the judgement strong depend-ence artificial experience of system object importance, subjectivity compares By force and it is necessary to the problems such as putting into manpower, be easy to causeing the waste of human resources.Therefore, in the present embodiment, using training algorithm Determine the corresponding feature weight of the multidimensional characteristic of the system object, it on the one hand can be to avoid engineer's weight, on the other hand It is more scientific objective dependent on the technical data and History Order data of system object.

Step S103

It in the present embodiment, can be according to the multidimensional characteristic of the system object in the importance for determining system object And feature weight makes comprehensive descision, it is since feature weight is obtained based on training algorithm, more objective, therefore calculate The importance of the system object arrived is also more objective, can really reflect importance of the system object in information platform.System The importance of object can be presented as scoring, assessment result or evaluation result to system object etc., scoring, assessment result or comment Valence result etc. is higher, and system object is more important.The specific algorithm of the importance of system object can be decided according to the actual requirements, Such as can be obtained using simple linear operation, also can use machine learning algorithm and obtain, machine learning algorithm include but It is not limited to: neural network, logistic regression, support vector machines, decision tree and Markovian decision process scheduling algorithm.

In an optional implementation of the present embodiment, the step S101 determines the multidimensional characteristic of system object The step of, it further includes steps of

In the optional implementation, by count or calculate etc. modes obtain the system object the multidimensional it is special The primitive character value of sign.The quantification manner of the primitive character value of each feature is different based on the difference of feature, but substantially may be used To be determined based on the relevant historical data of system object and/or empirical data.Such as whether agglomerate new number of users and system object Corresponding chain (KA) trade company can be obtained by statistical, and user's (can only consider new user in some embodiments) turns Rate can be obtained by calculation.Since the characteristic value quantification manner of every dimensional feature of system object is inconsistent, Before the characteristic value COMPREHENSIVE CALCULATING of multidimensional characteristic, the primitive character value can be normalized, and be based on normalizing Characteristic value after change determines the importance of system object.The characteristic value normalization method of different system object features can be different. For example, multiple purchase rate of user's (can only consider new user in some embodiments) within nearest period first time, user (can only consider new user in some embodiments) average visitor's unit price, user (can only consider newly to use in some embodiments Family) the higher system user of primitive characters value such as conversion ratio, the new number of users of cohesion, the corresponding importance of system user is higher, that Characteristic value correlation after primitive character value and normalization；And the single equal stock number of single user resources amount, new user The higher system object of equal primitive characters value, the corresponding importance of system object is lower instead, then primitive character value and normalizing Characteristic value negative correlation after change.Although the embodiment of the present disclosure is sold in addition order platform for be illustrated, the disclosure It is not limited to this, that is to say, that the technical solution of the embodiment of the present disclosure can be applied to various information platforms.

In an optional implementation of the present embodiment, the normalization characteristic value of the multidimensional characteristic is determined, comprising:

In the optional implementation, for every one-dimensional characteristic, original spy corresponding to this feature of all system objects Feature of the value indicative according to ascending ascending sort, after the dimensional feature normalization of each system object is calculated according to collating sequence Value.The characteristic value calculating process of each feature normalization of system object i are as follows:

For the feature with the characteristic value correlation after normalization, normalized characteristic value can be indicated are as follows:

Wherein, f-score_ijIndicate the normalized characteristic value of feature j of system object i, f-rank_ijIt indicates to all systems After object unite according to the primitive character value ascending sort of feature j, sorting position of the system object i in the list after the sequence, M Indicate the number of all system objects.

For the feature with the characteristic value negative correlation after normalization, normalized characteristic value can be indicated are as follows:

Wherein, f_score_ijIndicate the normalized characteristic value of feature j of system object i, f_rank_ijIt indicates to all systems After object unite according to the primitive character value ascending sort of feature j, sorting position of the system object i in the list after the sequence, M Indicate the number of all system objects.

Whether for feature " chain trade company ", the list of feature values is shown as after normalization:

In an optional implementation of the present embodiment, the step S103, i.e., according to the multidimensional of the system object Feature and feature weight, the step of determining the importance of the system object, comprising:

The importance of system object can be determined directly by the normalization characteristic value of the multidimensional characteristic, can also be passed through and be added It is determined again after power.In the case of not weighting, each dimensional feature is the same for the influence degree of the importance of system object.But It is different characteristic differences all for the influence degree of the importance of system object in practical situations, therefore after use weighting Characteristic value determine that the importance of system object is more objective.It therefore, in the present embodiment, can be according to the feature weight pair The normalization characteristic value of the multidimensional characteristic determines the importance of the system object after being weighted.

In an optional implementation of the present embodiment, the importance of the system object i can be calculate by the following formula Value:

In an optional implementation of the present embodiment, as shown in Fig. 2, the step S102, that is, determine system object Multidimensional characteristic the step of, further comprise the steps S201-S202:

In step s 201, training sample is obtained；Wherein, the training sample include multiple system objects two-by-two it Between importance the first comparison result；

It is corresponding to the multidimensional characteristic using the training sample and minimal error rate training algorithm in step S202 The feature weight be trained, and then determine the corresponding feature weight of the multidimensional characteristic.

Step S201

In the present embodiment, a large amount of training sample can be obtained according to historical data and/or historical experience, and passes through people The mode of work mark, marks the first comparison result of multiple system objects importance between any two, such as system object i's is important Property be greater than system object j importance etc..The first ratio of the importance between any two of system object in platform of ordering is sold other than hereafter To being illustrated for result, such as " KFC should have more resources than field teacher, i.e. KFC is more important than field teacher Property is big ", " McDonald should be fewer than the resource of KFC, i.e. McDonald is smaller than the importance of KFC " etc., can be by these institutes The first comparison result of system object importance between any two is stated as training sample, the quantity of these training samples can be according to tool Body business demand is determining, in the disclosure without limitation.

Step S202

In the present embodiment, it is determined that, can be using minimal error rate training algorithm to system in the case where training sample The feature weight of object is trained, final purpose be training is obtained the feature weight of each feature utmostly Match with the first comparison result in training sample.

The weight of the multidimensional characteristic of system object can be adjusted using minimal error rate training algorithm (mert).Most Minor error rate training algorithm can select an optimal feature weight for each multidimensional characteristic, and the selected feature power come out It is best suitable for the importance relativity between the importance for the system object being calculated and known system object.Example Such as, the corresponding businessman of system object 1 is chain businessman, and the corresponding businessman of system object 2 is a very small snacks shop, that According to historical experience it is recognised that for information platform, the importance of system object 1 is greater than system object 2.It is minimum Error rate training algorithm is some known relativities between the importance based on each system object, is the more of system object An optimal feature weight is selected per one-dimensional in dimensional feature, so that the system object being calculated according to these feature weights Importance be best suitable for above-mentioned relativity.

Such as, it is first determined the initial value of the corresponding feature weight of all dimensional features, it can be using by all dimensional features pair The method that the feature weight answered is set as preset value, wherein preset value can be some fixed numbers, such as 1；It can also pass through The mode that machine automatically generates random number is the corresponding feature weight assignment of all dimensional features；It can also be adopted according to training sample The initial value of the corresponding feature weight of all dimensional features is obtained with common machine learning algorithm.Due to the mark number of training sample According to can embody to a certain extent system object between any two important ratio pair as a result, when having determined that all dimensional features are corresponding After the initial value of feature weight, training sample can use, by minimal error rate training algorithm to the multidimensional characteristic pair The feature weight answered is trained, so that it is determined that the corresponding feature weight of the multidimensional characteristic.

In an optional implementation of the present embodiment, as shown in figure 3, the step S202, that is, utilize the training Sample and the minimal error rate training algorithm feature weight corresponding to the multidimensional characteristic are trained, and then determine institute The step of stating multidimensional characteristic corresponding feature weight, further comprises the steps S301-S302:

In step S301, for the corresponding target signature weight of target signature in the multidimensional characteristic, described more In the case that the corresponding feature weight of other features in dimensional feature is constant, determine that multiple system objects are special in the target Levy the candidate importance under any value of weight；

In step s 302, according in the training sample first comparison result and the candidate importance it is true The fixed target signature weight.

Step S301

In the present embodiment, minimal error rate training algorithm can use multidimensional characteristic of the mode to system object of iteration Feature weight optimize.During the corresponding feature weight of the multidimensional characteristic to system object optimizes, every time The corresponding feature weight of a feature of the system object can only be optimized in iteration.For example, from the system object multidimensional The corresponding target signature weight w of a target signature p is arbitrarily chosen in feature_p, to target signature weight w_pIt optimizes.? To above-mentioned target signature weight w_pWhen optimizing, the corresponding feature weight of other features in multidimensional characteristic is remained unchanged.Its In, the corresponding feature weight of other features in multidimensional characteristic is constant to be referred to, for the corresponding feature of feature of optimized mistake Weight, feature weight are the feature weight after optimization；For the corresponding feature weight of feature that other are not optimised, feature power Weight can be an initial value (fixed value or random number).At this point, the system object i importance values Score_iIt can be expressed as one A slope is f_score_ipStraight line:

As target signature weight w_pWhen taking different value, the importance values of the system object i are different, can determine institute System object i is stated in the target signature weight w_pCandidate importance values namely the slope under any value are f_score_ip Line correspondences all y values (x value be target signature weight w_p)。

It can determine on information platform that multiple system objects are weighed in the target signature using above-mentioned same method The candidate importance that important task is anticipated under value.The embodiment of the present disclosure will be with the target signature weight w of 5 system objects_pFor carry out Illustrate, as shown in figure 4, it is f_score that the importance values Score of 5 system objects can be expressed as slope respectively_1p、f_ score_2p、f_score_3p、f_score_4pAnd f_score_5pStraight line, the y value of this five line correspondences then be respectively 5 systems The candidate importance of object.

Step S302

It, can be according to the first comparison result in training sample after the candidate importance of each system object has been determined It is determined to farthest meet the corresponding target signature power of importance of the first comparison result from these candidate importance Weight.

In an optional implementation of the present embodiment, as shown in figure 5, the step S302, i.e., according to the training The step of first comparison result and the candidate importance in sample determine the target signature weight, further wraps Include following steps S501-S504:

In step S501, candidate feature power when the candidate importance of at least two system objects is equal is determined Weight；

In step S502, determine that the second of the candidate importance of system object compares two-by-two under the candidate feature weight As a result.

In step S503, determine that second comparison result corresponding with the training sample described first compares knot The degree that is consistent of fruit；Wherein, the identical quantity phase of the size of the degree that is consistent and the second comparison result and the first comparison result It closes；

In step S504, the highest candidate feature weight of the degree that is consistent is determined as the target signature weight.

In the present embodiment, the candidate importance values of each system object can be expressed as straight line, as shown in figure 4, There is intersection point between the corresponding straight line of candidate importance values of different system objects, and only can just change at the position of intersection point Become the importance of system object, therefore, it is first determined when the candidate importance of at least two system objects is equal (namely two At straight-line intersection) candidate feature weight (in Fig. 4 a total of 10 intersection points, namely can determine ten candidate features power Weight)；Secondly the second comparison result of the candidate importance of system object two-by-two is determined under described candidate feature weight；Then judge Whether the second comparison result and corresponding first comparison result are identical, and count identical quantity, for example, a certain candidate special Levy q0 under weight, the first comparison result a total of 10, and the same quantity of the second corresponding comparison result is 9； And at another candidate feature weight q1, the same quantity of the second comparison result corresponding with the first comparison result is 8, that Compared to candidate feature weight q1, it is believed that at candidate feature weight q0, the second comparison result and the first comparison result The degree that is consistent it is higher.The highest candidate feature weight of the degree that can will finally be consistent is determined as target signature weight.

In an optional implementation of the present embodiment, the multidimensional characteristic of the system object include at least user ( New user can be only considered in some embodiments) multiple purchase rate within nearest period first time, user is (in some embodiments In can only consider new user) average visitor is monovalent, new objective conversion ratio, the new number of users of cohesion, single user resources amount, new user are single Two or more in equal stock number and the whether chain trade company of system object.It such as may include new objective conversion ratio and cohesion New number of users, or including agglomerating new number of users, single user resources amount, new user singly equal stock number.Realization, which makes full use of, is The multidimensional characteristic of system object, and a few features are not limited to, to realize the important sex determination for more objectively embodying system object.

In an optional implementation of the present embodiment, the method still further comprises following steps:

The customer parameter using the system object is determined according to the importance of the system object.

In the optional implementation, the customer parameter determines method the following steps are included: according to the weight of system object The property wanted determines the customer parameter for using system object；Wherein, the importance of the system object is according in the embodiment of the present disclosure The determination method of system importance determines.

In the present embodiment, after system importance has been determined, customer parameter can also be determined according to system importance. It is the resource that user provides that customer parameter, which can include but is not limited to information platform,.In some embodiments, the weight of system object The property wanted is higher, and information platform can be more for the resource that the user that the system object is agglomerated provides, in this way being capable of promotion system Object agglomerates more users, provides more user resources for information platform.

In an optional implementation of the present embodiment, is determined according to the importance of the system object and use the system It the step of customer parameter of system object, further includes steps of

In the optional implementation, the determination process of customer parameter is described by following one specific application scenarios, It should be noted that the embodiment of the present disclosure is not limited to the application scenarios.

The importance of all system objects is sorted according to sequence from small to large first；Secondly joined according to the user of configuration The size of number interval divides the quantile of customer parameter, and the quantile divided is determined as quartile node；Root again According to the quartile node, the system object after sequence is grouped；Situation is finally grouped according to system object, from small to large Parameter in the corresponding parameter section of user is determined as to the parameter of user.Such as: run configuration customer parameter section be [3, 7], then parameter section size is 5, and quantile can be respectively 0.20,0.40,0.60,0.80,1.00.First by all systems The importance of object sorts according to sequence from small to large, secondly finds 20% quartile node, 40% quartile node, 60% respectively Quartile node and 80% quartile node, then the customer parameter value by sorting position less than the system object of 20% quartile node It is 3, is 4 by the customer parameter value of system object that sorting position is 20% to 40%, is 40% to 60% by sorting position The customer parameter value of system object be 5, be by the customer parameter value of system object that sorting position is 60% to 80% 6, the customer parameter value of the system object by sorting position greater than 80% is 7.

Other details in the present embodiment can be found in the description that the above-mentioned importance to system object determines embodiment of the method, Details are not described herein.

Following is embodiment of the present disclosure, can be used for executing embodiments of the present disclosure.

Fig. 6 shows the structural block diagram of the determining device of the system object importance according to one embodiment of the disclosure, the dress Setting being implemented in combination with as some or all of of electronic equipment by software, hardware or both.As shown in fig. 6, institute The determining device for stating system object importance includes the first determining module 601, the second determining module 602 and third determining module 603:

First determining module 601, is configured to determine that the multidimensional characteristic of system object；

Second determining module 602 is configured as determining that the multidimensional characteristic of the system object is corresponding based on training algorithm Feature weight；

Third determining module 603, is configured as multidimensional characteristic and feature weight according to the system object, determine described in The importance of system object.

The first determining module 601, the second determining module 602 and third determining module 603 will hereinafter be done respectively into The description of one step.

First determining module 601

Second determining module 602

Third determining module 603

In an optional implementation of the present embodiment, first determining module 601 further comprises:

In an optional implementation of the present embodiment, described second determines submodule, comprising:

In an optional implementation of the present embodiment, the third determining module 603, comprising:

Wherein, N indicates the characteristic dimension number of the system object i,_wjIndicate j-th of feature in the multidimensional characteristic Feature weight；f_score_ijIndicate the normalization characteristic value of j-th of feature of system object i.

In an optional implementation of the present embodiment, as shown in fig. 7, second determining module 602, comprising:

First acquisition submodule 701 is configured as obtaining training sample；Wherein, the training sample includes multiple described First comparison result of system object importance between any two；

4th determines submodule 702, is configured as using the training sample and minimal error rate training algorithm to institute It states the corresponding feature weight of multidimensional characteristic to be trained, and then determines the corresponding feature weight of the multidimensional characteristic.

First acquisition submodule 701

4th determines submodule 702

In an optional implementation of the present embodiment, as shown in figure 8, the described 4th determines submodule 702, comprising:

5th determines submodule 801, is configured as the corresponding target signature of target signature in the multidimensional characteristic Weight determines multiple systems pair in the case that the corresponding feature weight of other features in the multidimensional characteristic is constant As the candidate importance under any value of target signature weight；

6th determine submodule 802, be configured as according in the training sample first comparison result and institute It states candidate importance and determines the target signature weight.

5th determines submodule 801

It can determine on information platform that multiple system objects are weighed in the target signature using above-mentioned same method The candidate importance that important task is anticipated under value.The embodiment of the present disclosure will be with the target signature weight w of 5 system objects_pFor carry out Illustrate, as shown in figure 4, it is f_score that the importance values Score of 5 system objects can be expressed as slope respectively_1p、f_ score_2p、f_score_3p、f_score_4pAnd fscore_5pStraight line, the y value of this five line correspondences then be respectively 5 systems pair The candidate importance of elephant.

6th determines submodule 802

In an optional implementation of the present embodiment, as shown in figure 9, the described 6th determines submodule 802, comprising:

7th determines submodule 901, when being configured to determine that the candidate importance of at least two system objects is equal Candidate feature weight；

8th determines submodule 902, is configured as under the candidate feature weight the candidate importance of system object two-by-two The second comparison result.

9th determines submodule 903, is configured to determine that second comparison result is corresponding with the training sample The degree that is consistent of first comparison result；Wherein, the size of the degree that is consistent and the second comparison result and first compare knot The identical quantity of fruit is related；

Tenth determines submodule 904, is configured as the highest candidate feature weight of the degree that is consistent being determined as described Target signature weight.

In the present embodiment, the candidate importance values of each system object can be expressed as straight line, as shown in figure 4, There is intersection point between the corresponding straight line of candidate importance values of different system objects, and only can just change at the position of intersection point Become the importance of system object, therefore, determines that submodule 901 determines the time of at least two system objects by the 7th first (a total of 10 intersection points, also can candidate feature weight when selecting importance equal (namely at two straight-line intersections) in Fig. 4 Determine ten candidate feature weights)；Determine that submodule 902 determines under the candidate feature weight system two-by-two secondly by the 8th Second comparison result of the candidate importance of object；Then determine that submodule 903 judges the second comparison result and right by the 9th Whether the first comparison result answered is identical, and counts identical quantity, for example, the q0 under a certain candidate feature weight, first Comparison result a total of 10, and the same quantity of the second corresponding comparison result is 9；And in another candidate feature Under weight q1, the same quantity of the second comparison result corresponding with the first comparison result is 8, then compared to candidate feature Weight q1, it is believed that at candidate feature weight q0, the second comparison result and the degree that is consistent of the first comparison result are higher.Most Determine that the highest candidate feature weight of degree that can will be consistent of submodule 904 is determined as target signature weight by the tenth afterwards.

In an optional implementation of the present embodiment, the determining device of system object importance further include:

In an optional implementation of the present embodiment, the 4th determining module, comprising:

Figure 10 is adapted for the electronics for realizing the determination method of the system object importance according to disclosure embodiment The structural schematic diagram of equipment.

As shown in Figure 10, electronic equipment 1000 includes central processing unit (CPU) 1001, can be read-only according to being stored in Program in memory (ROM) 1002 is loaded into the journey in random access storage device (RAM) 1003 from storage section 1008 Sequence and execute the various processing in above-mentioned embodiment shown in FIG. 1.In RAM1003, it is also stored with the behaviour of electronic equipment 1000 Various programs and data needed for making.CPU1001, ROM1002 and RAM1003 are connected with each other by bus 1004.Input/defeated (I/O) interface 1005 is also connected to bus 1004 out.

I/O interface 1005 is connected to lower component: the importation 1006 including keyboard, mouse etc.；Including such as cathode The output par, c 1007 of ray tube (CRT), liquid crystal display (LCD) etc. and loudspeaker etc.；Storage section including hard disk etc. 1008；And the communications portion 1009 of the network interface card including LAN card, modem etc..Communications portion 1009 passes through Communication process is executed by the network of such as internet.Driver 1010 is also connected to I/O interface 1005 as needed.It is detachable to be situated between Matter 1011, such as disk, CD, magneto-optic disk, semiconductor memory etc. are mounted on as needed on driver 1010, so as to In being mounted into storage section 1008 as needed from the computer program read thereon.

Particularly, according to embodiment of the present disclosure, it is soft to may be implemented as computer above with reference to Fig. 1 method described Part program.For example, embodiment of the present disclosure includes a kind of computer program product comprising be tangibly embodied in and its readable Computer program on medium, the computer program include the program code for executing the method for Fig. 1.In such implementation In mode, which can be downloaded and installed from network by communications portion 1009, and/or from detachable media 1011 are mounted.

Flow chart and block diagram in attached drawing illustrate system, method and computer according to the various embodiments of the disclosure The architecture, function and operation in the cards of program product.In this regard, each box in course diagram or block diagram can be with A part of a module, section or code is represented, a part of the module, section or code includes one or more Executable instruction for implementing the specified logical function.It should also be noted that in some implementations as replacements, institute in box The function of mark can also occur in a different order than that indicated in the drawings.For example, two boxes succeedingly indicated are practical On can be basically executed in parallel, they can also be executed in the opposite order sometimes, and this depends on the function involved.Also it wants It is noted that the combination of each box in block diagram and or flow chart and the box in block diagram and or flow chart, Ke Yiyong The dedicated hardware based system of defined functions or operations is executed to realize, or can be referred to specialized hardware and computer The combination of order is realized.

Being described in unit or module involved in disclosure embodiment can be realized by way of software, can also It is realized in a manner of through hardware.Described unit or module also can be set in the processor, these units or module Title do not constitute the restriction to the unit or module itself under certain conditions.

As on the other hand, the disclosure additionally provides a kind of computer readable storage medium, the computer-readable storage medium Matter can be computer readable storage medium included in device described in above embodiment；It is also possible to individualism, Without the computer readable storage medium in supplying equipment.Computer-readable recording medium storage has one or more than one journey Sequence, described program is used to execute by one or more than one processor is described in disclosed method.

Above description is only the preferred embodiment of the disclosure and the explanation to institute's application technology principle.Those skilled in the art Member is it should be appreciated that invention scope involved in the disclosure, however it is not limited to technology made of the specific combination of above-mentioned technical characteristic Scheme, while should also cover in the case where not departing from the inventive concept, it is carried out by above-mentioned technical characteristic or its equivalent feature Any combination and the other technical solutions formed.Such as features described above has similar function with (but being not limited to) disclosed in the disclosure Can technical characteristic replaced mutually and the technical solution that is formed.

Claims

1. a kind of determination method of system object importance characterized by comprising

Determine the multidimensional characteristic of system object；

The corresponding feature weight of multidimensional characteristic of the system object is determined based on training algorithm；

2. the method according to claim 1, wherein determining the multidimensional characteristic of system object, comprising:

3. according to the method described in claim 2, it is characterized in that, determining the normalization characteristic value of the multidimensional characteristic, comprising:

In the importance correlation of the multidimensional characteristic and the system object, the normalization of the multidimensional characteristic is special Value indicative is determined as the ratio of the primitive character value Yu the system object number；

In the importance negative correlation of the multidimensional characteristic and the system object, the normalization of the multidimensional characteristic is special Value indicative is determined as 1 ratio for subtracting the primitive character value Yu the system object number.

4. according to the method in claim 2 or 3, which is characterized in that according to the multidimensional characteristic and feature of the system object Weight determines the importance of the system object, comprising:

The system object is determined after being weighted according to normalization characteristic value of the feature weight to the multidimensional characteristic Importance.

5. according to the method described in claim 4, it is characterized in that, according to the feature weight to the normalizing of the multidimensional characteristic Change the importance that the system object is determined after characteristic value is weighted, comprising:

Wherein, N indicates the characteristic dimension number of the system object i, w_jIndicate the spy of j-th of feature in the multidimensional characteristic Levy weight；f_score_ijIndicate the normalization characteristic value of j-th of feature of system object i.

6. -3,5 described in any item methods according to claim 1, which is characterized in that be based on training algorithm, determine the system The corresponding feature weight of the multidimensional characteristic of object, comprising:

Obtain training sample；Wherein, the training sample include multiple system objects between any two importance first ratio To result；

Using the training sample and minimal error rate the training algorithm feature weight corresponding to the multidimensional characteristic into Row training, and then determine the corresponding feature weight of the multidimensional characteristic.

7. according to the method described in claim 6, it is characterized in that, being calculated using the training sample and minimal error rate training The method feature weight corresponding to the multidimensional characteristic is trained, and then determines the corresponding feature power of the multidimensional characteristic Weight, comprising:

Other features for the corresponding target signature weight of target signature in the multidimensional characteristic, in the multidimensional characteristic In the case that corresponding feature weight is constant, determine multiple system objects under any value of target signature weight Candidate importance；

According in the training sample first comparison result and the candidate importance determine the target signature power Weight.

8. a kind of determining device of system object importance characterized by comprising

Second determining module is configured as determining the corresponding feature power of the multidimensional characteristic of the system object based on training algorithm Weight；

Third determining module is configured as multidimensional characteristic and feature weight according to the system object, determines the system pair The importance of elephant.

9. a kind of electronic equipment, which is characterized in that including memory and processor；Wherein,

The memory is for storing one or more computer instruction, wherein one or more computer instruction is by institute Processor is stated to execute to realize the described in any item method and steps of claim 1-7.

10. a kind of computer readable storage medium, is stored thereon with computer instruction, which is characterized in that the computer instruction quilt Claim 1-7 described in any item method and steps are realized when processor executes.