CN110046799A - Decision optimization method and device - Google Patents
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Abstract
Present disclose provides a kind of decision optimization method and devices.This method comprises: determining prediction result contribution degree of each decision characteristic variable under user characteristic data in prediction model, the prediction model is created based on decision objective, and the prediction model includes decision characteristic variable and non-decision characteristic variable；Based on the prediction result contribution degree for each decision characteristic variable determined, decision characteristic variable combination to be optimized is constructed；Optimizing processing is carried out to the variable-value of each decision characteristic variable in constructed decision characteristic variable combination, so that the correspondence prediction result of the prediction model is best；And decision optimization processing is carried out according to the variable-value of each decision characteristic variable in the decision characteristic variable combination obtained after optimizing is handled.Using this method, the personalized decision optimization for user may be implemented.
Description
Technical field
The disclosure is usually directed to field of computer technology, more particularly, to the method and device for decision optimization.
Background technique
In many business scenarios, service provider wishes to provide customization service for user, to improve most possibly
The experience of user, ensures the equity of user, or reaches other business goals.For example, maximizing client's in marketing scene
Register conversion ratio or retention ratio.In transaction risk control, the risk that the every transaction fund of user is swindled is minimized.This kind of need
Seeking essence is personalized decision to be carried out for different users, and this personalized decision can be realized optimizing decision effect,
To maximumlly realize decision purpose.
Summary of the invention
In view of above-mentioned, present disclose provides a kind of method and devices for decision optimization.Utilize this method and device, energy
Enough personalized decision optimization is realized for different users.
According to one aspect of the disclosure, a kind of method for decision optimization is provided, comprising: determine in prediction model
Prediction result contribution degree of each decision characteristic variable under user characteristic data, the prediction model is based on decision objective
Creation, the prediction model includes decision characteristic variable and non-decision characteristic variable；It is special based on each decision determined
The prediction result contribution degree for levying variable constructs decision characteristic variable combination to be optimized；To constructed decision characteristic variable group
The variable-value of each decision characteristic variable in conjunction carries out optimizing processing, so that the correspondence prediction result of the prediction model
Most preferably；And it is taken according to the variable of each decision characteristic variable in the decision characteristic variable combination obtained after optimizing is handled
Value carries out decision optimization processing.
Optionally, in an example of above-mentioned aspect, determine each decision characteristic variable in prediction model in user
Prediction result contribution degree under characteristic includes: each decision characteristic variable determined in prediction model using interpretation model
Prediction result contribution degree under user characteristic data.
Optionally, in an example of above-mentioned aspect, the interpretation model includes one of following interpretation models:
Shap value model, LIME model and DeepLift model.
Optionally, in an example of above-mentioned aspect, the prediction knot based on each decision characteristic variable determined
Fruit contribution degree, constructing decision characteristic variable combination to be optimized may include: based on each decision characteristic variable determined
Prediction result contribution degree, each decision characteristic variable is ranked up；And it is special from each decision after the sequence
It levies and selects the biggish predetermined number decision characteristic variable of contribution degree in variable, combined as decision characteristic variable to be optimized.
Optionally, in an example of above-mentioned aspect, to each decision in constructed decision characteristic variable combination
It may include: using one of following optimizing algorithms optimizing algorithm come to institute that the variable-value of characteristic variable, which carries out optimizing processing,
The variable-value progress optimizing processing of each decision characteristic variable in the decision characteristic variable combination of building: particle swarm algorithm,
Genetic algorithm, annealing algorithm.
Optionally, in an example of above-mentioned aspect, to each decision in constructed decision characteristic variable combination
It may include: to determine in predetermined decision variable value range to constructed that the variable-value of characteristic variable, which carries out optimizing processing,
The variable-value of each decision characteristic variable in the combination of plan characteristic variable carries out optimizing processing.
According to another aspect of the present disclosure, a kind of device for decision optimization is provided, comprising: contribution degree determination unit,
Prediction result contribution degree of each decision characteristic variable under user characteristic data being configured to determine that in prediction model, it is described
Prediction model is created based on decision objective, and the prediction model includes decision characteristic variable and non-decision characteristic variable；Certainly
Plan characteristic variable combines construction unit, is configured as the prediction result contribution based on each decision characteristic variable determined
Degree constructs decision characteristic variable combination to be optimized；Optimizing processing unit is configured as to constructed decision characteristic variable group
The variable-value of each decision characteristic variable in conjunction carries out optimizing processing, so that the correspondence prediction result of the prediction model
Most preferably；And decision optimization unit, it is configured as according to each in the decision characteristic variable combination obtained after optimizing is handled
The variable-value of a decision characteristic variable carries out decision optimization processing.
Optionally, in an example of above-mentioned aspect, the contribution degree determination unit is configured as: using interpretation model
To determine prediction result contribution degree of each decision characteristic variable under user characteristic data in prediction model.
Optionally, in an example of above-mentioned aspect, the interpretation model includes one of following interpretation models:
Shap value model, LIME model and DeepLift model.
Optionally, in an example of above-mentioned aspect, the decision characteristic variable combination construction unit includes: sequence mould
Block is configured as the prediction result contribution degree based on each decision characteristic variable determined, to each decision feature
Variable is ranked up；And feature selection module, it is configured as selecting tribute from each decision characteristic variable after the sequence
The biggish predetermined number decision characteristic variable of degree of offering is combined as decision characteristic variable to be optimized.
Optionally, in an example of above-mentioned aspect, the optimizing processing unit is configured as: being calculated using following optimizing
One of method to carry out optimizing to the variable-value of each decision characteristic variable in constructed decision characteristic variable combination
Processing: particle swarm algorithm, genetic algorithm, annealing algorithm.
Optionally, in an example of above-mentioned aspect, the optimizing processing unit is configured as: in predetermined decision variable
In value range, the variable-value of each decision characteristic variable in constructed decision characteristic variable combination is carried out at optimizing
Reason.
According to another aspect of the present disclosure, a kind of calculating equipment is provided, comprising: at least one processor, and with it is described
The memory of at least one processor coupling, the memory store instruction, when described instruction is by least one described processor
When execution, so that at least one described processor executes the method for being used for decision optimization as described above.
According to another aspect of the present disclosure, a kind of non-transitory machinable medium is provided, is stored with executable
Instruction, described instruction make the machine execute the method for being used for decision optimization as described above upon being performed.
Detailed description of the invention
By referring to following attached drawing, may be implemented to further understand the nature and advantages of present disclosure.?
In attached drawing, similar assembly or feature can have identical appended drawing reference.
Fig. 1 shows the flow chart of decision optimization method according to an embodiment of the present disclosure；
Fig. 2 shows the one of the prediction result contribution degree determination process of decision characteristic variable according to an embodiment of the present disclosure
A exemplary flow chart；
Fig. 3 shows an exemplary process of decision characteristic variable combination building process according to an embodiment of the present disclosure
Figure；
Fig. 4 shows an exemplary stream of the online handoff procedure of personalized decision-making mechanism according to an embodiment of the present disclosure
Cheng Tu；
Fig. 5 shows the block diagram of decision optimization device according to an embodiment of the present disclosure；
Fig. 6 shows an exemplary box of decision characteristic variable combination construction unit according to an embodiment of the present disclosure
Figure；
Fig. 7 shows the block diagram of the calculating equipment according to an embodiment of the present disclosure for decision optimization.
Specific embodiment
Theme described herein is discussed referring now to example embodiment.It should be understood that discussing these embodiments only
It is in order to enable those skilled in the art can better understand that being not to claim to realize theme described herein
Protection scope, applicability or the exemplary limitation illustrated in book.It can be in the protection scope for not departing from present disclosure
In the case of, the function and arrangement of the element discussed are changed.Each example can according to need, omit, substitute or
Add various processes or component.For example, described method can be executed according to described order in a different order, with
And each step can be added, omits or combine.In addition, feature described in relatively some examples is in other examples
It can be combined.
As used in this article, term " includes " and its modification indicate open term, are meant that " including but not limited to ".
Term "based" indicates " being based at least partially on ".Term " one embodiment " and " embodiment " expression " at least one implementation
Example ".Term " another embodiment " expression " at least one other embodiment ".Term " first ", " second " etc. may refer to not
Same or identical object.Here may include other definition, either specific or implicit.Unless bright in context
It really indicates, otherwise the definition of a term is consistent throughout the specification.
Decision optimization method and device according to an embodiment of the present disclosure is described in detail below in conjunction with attached drawing.
Fig. 1 shows the flow chart of decision optimization method according to an embodiment of the present disclosure.
As shown in Figure 1, determining each decision characteristic variable in prediction model under user characteristic data in block 110
Prediction result contribution degree.Here, the prediction model is to be created based on decision objective, and the prediction model includes decision
Characteristic variable and non-decision characteristic variable.The user characteristic data is corresponding with decision characteristic variable and non-decision characteristic variable
User characteristic data.
For example, it is assumed that decision objective is user's registration conversion number, in transaction risk control scene in marketing scene
In, decision objective is amount of money summation of the user because swindling trade loss.For this decision objective, suitable characteristic variable group is created
Prediction model is established, thus based on user characteristic data come forecast and decision target, for example, whether prediction user converts, or pre-
Survey the amount of money in customer transaction because of fraud loss.In the disclosure, term " non-decision characteristic variable " refers to that decision-making party can not be done
The characteristic variable related to, for example, the age of user, personality, behavior over history etc..Term " decision characteristic variable " refers to decision
Side can interfere the characteristic variable of change, for example, in marketing scene, the case where decision objective is user's registration conversion number
Under the characteristic variables such as the preferential and equity for being issued to user, or in transaction risk control scene, export to the wind of user
The characteristic variables such as danger prompting.
In addition, in the disclosure, the prediction model can have previously been based on decision objective to utilize training data to create,
It is also possible to utilize training data to create in real time after getting decision objective.Equally, the decision objective can be in advance
Input or real-time input.In addition, being used to carry out prediction model training to improve the accuracy rate of prediction model
Training data should cover the user group with multiple features as far as possible, and attempt decision variable value as much as possible, from
And ensure the rich of training data dimension.
In an example of the disclosure, interpretation model can be used to determine that each decision feature in prediction model becomes
Measure the prediction result contribution degree under user characteristic data.The interpretation model includes one of following interpretation models: Shap
Value model, LIME model and DeepLift model.On how to use interpretation model determine in prediction model it is each certainly
Prediction result contribution degree of the plan characteristic variable under user characteristic data, will be illustrated below in reference to Fig. 2.
After the prediction result contribution degree for as above determining each decision characteristic variable, in block 120, based on what is determined
The prediction result contribution degree of each decision characteristic variable constructs decision characteristic variable combination to be optimized.On how to based on institute
The prediction result contribution degree for each decision characteristic variable determined combines to construct decision characteristic variable to be optimized, will be under
Face combines example shown in Fig. 3 to be illustrated.
After constructing decision characteristic variable combination to be optimized, in block 130, constructed decision characteristic variable is combined
In each decision characteristic variable variable-value carry out optimizing processing so that the correspondence prediction result of the prediction model is most
It is good.
For example, in one example, the change to each decision characteristic variable in constructed decision characteristic variable combination
It may include: using one of following optimizing algorithms optimizing algorithm come special to constructed decision that measurement value, which carries out optimizing processing,
The variable-value for levying each decision characteristic variable in variable combination carries out optimizing processing: particle swarm algorithm, genetic algorithm, annealing
Algorithm.Here it is possible to determine used optimizing algorithm based on the type of the decision characteristic variable of pending optimizing processing.Than
Such as, in the case where the decision characteristic variable handled to optimizing is classifying type variable, optimizing algorithm it is preferable to use particle swarm algorithm,
For example, discrete particle cluster algorithm.
In addition, in another example of the disclosure, to each decision feature in constructed decision characteristic variable combination
It may include: in predetermined decision variable value range, to constructed decision spy that the variable-value of variable, which carries out optimizing processing,
The variable-value for levying each decision characteristic variable in variable combination carries out optimizing processing.In addition, in order to enable optimizing effect more
It is good, it can carry out properly to adjust the size of decision variable value range according to the actual situation.
After the variable-value for determining each decision characteristic variable in the combination of decision characteristic variable, in block 140, according to
The variable-value of each decision characteristic variable in the combination of decision characteristic variable obtained after optimizing is handled carries out decision
Optimization processing.For example, using the variable-value of each decision characteristic variable come to decision-making mechanism used in decision engine
Optimize adjustment.
Fig. 2 shows the one of the prediction result contribution degree determination process of decision characteristic variable according to an embodiment of the present disclosure
A exemplary flow chart.
As shown in Fig. 2, for each decision characteristic variable in prediction model, firstly, selecting a decision in block 210
Characteristic variable is as initial current decision characteristic variable.For example, one can be randomly choosed from each decision characteristic variable
Decision characteristic variable is as initial current decision characteristic variable.
Then, for the current decision characteristic variable, perfoming block 220 arrives the operation of block 270, until special for all decisions
Sign variable all determines corresponding prediction result contribution degree.Specifically, in block 220, determine in the combination of decision characteristic variable in addition to
Prediction result corresponding to any combination of remaining decision characteristic variable except the current decision characteristic variable.For example, it is assumed that
Prediction model is current decision characteristic variable there are 4 decision characteristic variables V1, V2, V3, V4 and V1, then using prediction mould
Type is V2 the input that calculates in prediction model, in the case where the corresponding user characteristic data of the arbitrary characteristics combination of V3, V4
Model prediction result.Then, in block 230, appointing comprising the current decision characteristic variable in the combination of decision characteristic variable is determined
The corresponding prediction result of what characteristic variable combination.Characteristic variable combination described here is for every kind of feature in block 220
Combination all increases the obtained characteristic variable combination of characteristic variable V1.Then, the model determined in block 240, calculation block 220
Corresponding model prediction prediction result (model prediction result when the no current decision characteristic variable) and determined in block 230
As a result the difference for (having the model prediction result when current decision characteristic variable).
After the difference for determining each model prediction result, in block 350, all model prediction result differences are carried out asking flat
, to obtain the prediction result contribution degree of the current decision characteristic variable.Then, in block 260, it is determined whether there are untreated
Decision characteristic variable.If it is present selecting a decision feature to become from untreated decision characteristic variable in block 270
Amount is used as next current decision characteristic variable, is then return to block 220, re-executes the operation that block 220 arrives block 270.
Fig. 3 shows an exemplary process of decision characteristic variable combination building process according to an embodiment of the present disclosure
Figure.
As shown in figure 3, firstly, the prediction result based on each decision characteristic variable determined is contributed in block 310
Degree, is ranked up each decision characteristic variable.Then, in block 320, from each decision characteristic variable after the sequence
The middle biggish predetermined number decision characteristic variable of selection contribution degree is combined as decision characteristic variable to be optimized.Here, institute
Stating predetermined number can be determined based on practical application scene or other appropraite conditions.In addition, in other examples of the disclosure
In, decision characteristic variable combination to be optimized can also be constructed using other suitable modes.
Come for different user by using the user characteristic data of the user using the decision optimization method of the disclosure
The prediction result contribution degree for determining each decision characteristic variable in prediction model, is constructed based on the prediction result determined
Decision characteristic variable to be optimized combines, and each decision in decision characteristic variable combination is determined using optimizing algorithm
The variable-value of characteristic variable.In the decision optimization method, due to available each for different user characteristic datas
The different prediction result contribution degrees of decision characteristic variable combine, thus needle so as to construct different decision characteristic variables
To different users, the decision characteristic variable combination optimized also can be different, and then realize personalized decision optimization.
Using the decision optimization method of the disclosure, decision characteristic variable value model is properly adjusted by according to the actual situation
The size enclosed, and in decision characteristic variable value range, to each decision in constructed decision characteristic variable combination
The variable-value of characteristic variable carries out optimizing processing, it is possible thereby to promote optimizing effect, and then improves decision optimization effect.
Fig. 4 shows an exemplary stream of the online handoff procedure of personalized decision-making mechanism according to an embodiment of the present disclosure
Cheng Tu.
As shown in figure 4, being determined online after as above determining optimized personalized decision-making mechanism and engine
When plan, to the customer data to decision of inflow, which is randomly distributed to after former decision-making mechanism or optimization
Property decision-making mechanism is handled, and then is made a policy, and record corresponding decision-making results.After at one section of such operation,
The decision-making results of personalized decision-making mechanism after former decision-making mechanism or optimization is assessed, if the decision effect of former decision-making mechanism
Fruit is more preferable, then keeps former decision-making mechanism, if the personalized decision-making mechanism after optimization is preferable, is determined using the personalization after optimization
Plan mechanism replaces former decision-making mechanism, thereby guarantees that the stationarity of decision business.
Fig. 5 shows the block diagram of decision optimization device 500 according to an embodiment of the present disclosure.As shown in figure 5, decision is excellent
It includes contribution degree determination unit 510, decision characteristic variable combination construction unit 520,530 and of optimizing processing unit that makeup, which sets 500,
Decision optimization unit 540.
Contribution degree determination unit 510 is configured to determine that each decision characteristic variable in prediction model in user characteristics number
Prediction result contribution degree under, the prediction model are created based on decision objective, and the prediction model includes decision spy
Levy variable and non-decision characteristic variable.The operation of contribution degree determination unit 510 can be with reference to the block 110 described above with reference to Fig. 1
Operation and referring to Fig. 2 describe operation.
Decision characteristic variable combination construction unit 520 is configured as based on the pre- of each decision characteristic variable determined
Result contribution degree is surveyed, decision characteristic variable combination to be optimized is constructed.The operation that decision characteristic variable combines construction unit 520 can
With reference to the operation above with reference to Fig. 1 block 120 described and the operation described referring to Fig. 3.
Optimizing processing unit 530 is configured as to each decision characteristic variable in constructed decision characteristic variable combination
Variable-value carry out optimizing processing so that the correspondence prediction result of the prediction model is best.One in the disclosure is shown
In example, optimizing processing unit 530 is configured as: using one of following optimizing algorithms come to constructed decision characteristic variable
The variable-value of each decision characteristic variable in combination carries out optimizing processing: particle swarm algorithm, genetic algorithm, annealing algorithm.
In another example of the disclosure, optimizing processing unit 530 is configured as: in predetermined decision variable value range, to institute's structure
The variable-value for each decision characteristic variable in the combination of decision characteristic variable built carries out optimizing processing.Optimizing processing unit
530 operation can be with reference to the operation above with reference to Fig. 1 block 130 described.
Decision optimization unit 540 is configured as according to each in the decision characteristic variable combination obtained after optimizing is handled
The variable-value of a decision characteristic variable carries out decision optimization processing.The operation of decision optimization unit 540 can be with reference to above
The operation of the block 140 described referring to Fig.1.
In an example of the disclosure, contribution degree determination unit 510 be can be configured as: be determined using interpretation model
Prediction result contribution degree of each decision characteristic variable under user characteristic data in prediction model.The interpretation model can be with
Including one of following interpretation models: Shap value model, LIME model and DeepLift model.
Fig. 6 show decision characteristic variable according to an embodiment of the present disclosure combination construction unit 520 one is exemplary
Block diagram.As shown in fig. 6, decision characteristic variable combination construction unit 520 includes sorting module 521 and feature selection module 523.
Sorting module 521 is configured as the prediction result contribution degree based on each decision characteristic variable determined, right
Each decision characteristic variable is ranked up.
Feature selection module 523 is configured as selecting contribution degree larger from each decision characteristic variable after the sequence
Predetermined number decision characteristic variable, combined as decision characteristic variable to be optimized.
Above with reference to Fig. 1 to Fig. 6, the embodiment of the decision optimization method and device according to the disclosure is described.
Decision optimization device above can use hardware realization, can also be realized using the combination of software or hardware and software.
Fig. 7 shows the hardware structure diagram of the calculating equipment 700 according to an embodiment of the present disclosure for decision optimization.Such as
Shown in Fig. 7, calculating equipment 700 may include at least one processor 710, memory 720, memory 730 and communication interface 740,
And at least one processor 710, memory 720, memory 730 and communication interface 740 link together via bus 760.Extremely
A few processor 710 executes at least one computer-readable instruction for storing or encoding in memory (that is, above-mentioned with software
The element that form is realized).
In one embodiment, computer executable instructions are stored in memory, make at least one when implemented
Processor 710: prediction result contribution degree of each decision characteristic variable under user characteristic data in prediction model, institute are determined
Stating prediction model is created based on decision objective, and the prediction model includes decision characteristic variable and non-decision characteristic variable；
Based on the prediction result contribution degree for each decision characteristic variable determined, decision characteristic variable combination to be optimized is constructed；
Optimizing processing is carried out to the variable-value of each decision characteristic variable in constructed decision characteristic variable combination, so that institute
The correspondence prediction result for stating prediction model is best；And according in the decision characteristic variable combination obtained after optimizing is handled
The variable-value of each decision characteristic variable carries out decision optimization processing.
It should be understood that the computer executable instructions stored in memory make at least one processor when implemented
710 carry out the above various operations and functions described in conjunction with Fig. 1-6 in each embodiment of the disclosure.
In the disclosure, calculating equipment 700 can include but is not limited to: personal computer, server computer, work
It stands, desktop computer, laptop computer, notebook computer, mobile computing device, smart phone, tablet computer, bee
Cellular telephone, personal digital assistant (PDA), hand-held device, messaging devices, wearable calculating equipment, consumer-elcetronics devices etc.
Deng.
According to one embodiment, a kind of program product of such as non-transitory machine readable media is provided.Non-transitory
Machine readable media can have instruction (that is, above-mentioned element realized in a software form), which when executed by a machine, makes
It obtains machine and executes the above various operations and functions described in conjunction with Fig. 1-6 in each embodiment of the disclosure.Specifically, Ke Yiti
For being furnished with the system or device of readable storage medium storing program for executing, store on the readable storage medium storing program for executing any in realization above-described embodiment
The software program code of the function of embodiment, and read and execute the computer of the system or device or processor and be stored in
Instruction in the readable storage medium storing program for executing.
According to one embodiment, a kind of program product of such as non-transitory machine readable media is provided.Non-transitory
Machine readable media can have instruction (that is, above-mentioned element realized in a software form), which when executed by a machine, makes
It obtains machine and executes the above various operations and functions described in conjunction with Fig. 1-6 in each embodiment of the disclosure.Specifically, Ke Yiti
For being furnished with the system or device of readable storage medium storing program for executing, store on the readable storage medium storing program for executing any in realization above-described embodiment
The software program code of the function of embodiment, and read and execute the computer of the system or device or processor and be stored in
Instruction in the readable storage medium storing program for executing.
In this case, it is real that any one of above-described embodiment can be achieved in the program code itself read from readable medium
The function of example is applied, therefore the readable storage medium storing program for executing of machine readable code and storage machine readable code constitutes of the invention one
Point.
The embodiment of readable storage medium storing program for executing include floppy disk, hard disk, magneto-optic disk, CD (such as CD-ROM, CD-R, CD-RW,
DVD-ROM, DVD-RAM, DVD-RW, DVD-RW), tape, non-volatile memory card and ROM.It selectively, can be by communication network
Network download program code from server computer or on cloud.
It will be appreciated by those skilled in the art that each embodiment disclosed above can be in the situation without departing from invention essence
Under make various changes and modifications.Therefore, protection scope of the present invention should be defined by the appended claims.
It should be noted that step and unit not all in above-mentioned each process and each system construction drawing is all necessary
, certain step or units can be ignored according to the actual needs.Each step execution sequence be not it is fixed, can be according to need
It is determined.Apparatus structure described in the various embodiments described above can be physical structure, be also possible to logical construction, that is, have
A little units may be realized by same physical entity, be realized alternatively, some units may divide by multiple physical entities, alternatively, can be with
It is realized jointly by certain components in multiple autonomous devices.
In the above various embodiments, hardware cell or module mechanically or can be realized electrically.For example, one
Hardware cell, module or processor may include permanent dedicated circuit or logic (such as special processor, FPGA or
ASIC) corresponding operating is completed.Hardware cell or processor can also include programmable logic or circuit (such as general processor or
Other programmable processors), interim setting can be carried out by software to complete corresponding operating.Concrete implementation mode is (mechanical
Mode or dedicated permanent circuit or the circuit being temporarily arranged) it can be determined based on cost and temporal consideration.
The specific embodiment illustrated above in conjunction with attached drawing describes exemplary embodiment, it is not intended that may be implemented
Or fall into all embodiments of the protection scope of claims." exemplary " meaning of the term used in entire this specification
Taste " be used as example, example or illustration ", be not meant to than other embodiments " preferably " or " there is advantage ".For offer pair
The purpose of the understanding of described technology, specific embodiment include detail.However, it is possible in these no details
In the case of implement these technologies.In some instances, public in order to avoid the concept to described embodiment causes indigestion
The construction and device known is shown in block diagram form.
The foregoing description of present disclosure is provided so that any those of ordinary skill in this field can be realized or make
Use present disclosure.To those skilled in the art, the various modifications carried out to present disclosure are apparent
, also, can also answer generic principles defined herein in the case where not departing from the protection scope of present disclosure
For other modifications.Therefore, present disclosure is not limited to examples described herein and design, but disclosed herein with meeting
Principle and novel features widest scope it is consistent.
Claims (14)
1. a kind of method for decision optimization, comprising:
Determine prediction result contribution degree of each decision characteristic variable under user characteristic data in prediction model, the prediction
Model is created based on decision objective, and the prediction model includes decision characteristic variable and non-decision characteristic variable；
Based on the prediction result contribution degree for each decision characteristic variable determined, decision characteristic variable group to be optimized is constructed
It closes；
Optimizing processing is carried out to the variable-value of each decision characteristic variable in constructed decision characteristic variable combination, so that
The correspondence prediction result for obtaining the prediction model is best；And
According to obtained after optimizing is handled decision characteristic variable combination in each decision characteristic variable variable-value come
Carry out decision optimization processing.
2. the method for claim 1, wherein determining each decision characteristic variable in prediction model in user characteristics number
Prediction result contribution degree under includes:
Prediction result of each decision characteristic variable under user characteristic data in prediction model is determined using interpretation model
Contribution degree.
3. method according to claim 2, wherein the interpretation model includes one of following interpretation models:
Shap value model, LIME model and DeepLift model.
4. the method for claim 1, wherein prediction result contribution based on each decision characteristic variable determined
Degree, constructing decision characteristic variable combination to be optimized includes:
Based on the prediction result contribution degree for each decision characteristic variable determined, each decision characteristic variable is carried out
Sequence；And
The biggish predetermined number decision characteristic variable of contribution degree is selected from each decision characteristic variable after the sequence, is made
It is combined for decision characteristic variable to be optimized.
5. the method for claim 1, wherein becoming to each decision feature in constructed decision characteristic variable combination
The variable-value of amount carries out optimizing processing
Using one of following optimizing algorithms optimizing algorithm come to each decision in constructed decision characteristic variable combination
The variable-value of characteristic variable carries out optimizing processing:
Particle swarm algorithm, genetic algorithm, annealing algorithm.
6. the method for claim 1, wherein becoming to each decision feature in constructed decision characteristic variable combination
The variable-value of amount carries out optimizing processing
In predetermined decision variable value range, to each decision characteristic variable in constructed decision characteristic variable combination
Variable-value carries out optimizing processing.
7. a kind of device for decision optimization, comprising:
Contribution degree determination unit is configured to determine that each decision characteristic variable in prediction model under user characteristic data
Prediction result contribution degree, the prediction model are created based on decision objective, and the prediction model includes decision characteristic variable
With non-decision characteristic variable；
Decision characteristic variable combines construction unit, is configured as the prediction result based on each decision characteristic variable determined
Contribution degree constructs decision characteristic variable combination to be optimized；
Optimizing processing unit is configured as the variable to each decision characteristic variable in constructed decision characteristic variable combination
Value carries out optimizing processing, so that the correspondence prediction result of the prediction model is best；And
Decision optimization unit is configured as according to each decision in the decision characteristic variable combination obtained after optimizing is handled
The variable-value of characteristic variable carries out decision optimization processing.
8. device as claimed in claim 7, wherein the contribution degree determination unit is configured as:
Prediction result of each decision characteristic variable under user characteristic data in prediction model is determined using interpretation model
Contribution degree.
9. device as claimed in claim 8, wherein the interpretation model includes one of following interpretation models:
Shap value model, LIME model and DeepLift model.
10. device as claimed in claim 7, wherein the decision characteristic variable combines construction unit and includes:
Sorting module is configured as the prediction result contribution degree based on each decision characteristic variable determined, to described each
A decision characteristic variable is ranked up；And
Feature selection module is configured as selecting contribution degree biggish predetermined from each decision characteristic variable after the sequence
Number decision characteristic variable is combined as decision characteristic variable to be optimized.
11. device as claimed in claim 7, wherein the optimizing processing unit is configured as:
Using one of following optimizing algorithms come to each decision characteristic variable in constructed decision characteristic variable combination
Variable-value carry out optimizing processing:
Particle swarm algorithm, genetic algorithm, annealing algorithm.
12. device as claimed in claim 7, wherein the optimizing processing unit is configured as:
In predetermined decision variable value range, to each decision characteristic variable in constructed decision characteristic variable combination
Variable-value carries out optimizing processing.
13. a kind of calculating equipment, comprising:
At least one processor, and
The memory coupled at least one described processor, the memory store instruction, when described instruction by it is described at least
When one processor executes, so that at least one described processor executes the method as described in any in claims 1 to 6.
14. a kind of non-transitory machinable medium, is stored with executable instruction, described instruction makes upon being performed
The machine executes the method as described in any in claims 1 to 6.
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| WO2020181907A1 (en) * | 2019-03-08 | 2020-09-17 | 阿里巴巴集团控股有限公司 | Decision-making optimization method and apparatus |
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