CN110110139A

CN110110139A - The method, apparatus and electronic equipment that a kind of pair of recommendation results explain

Info

Publication number: CN110110139A
Application number: CN201910317239.4A
Authority: CN
Inventors: 孙成龙; 郭正凯; 宋华
Original assignee: Beijing QIYI Century Science and Technology Co Ltd
Current assignee: Beijing QIYI Century Science and Technology Co Ltd
Priority date: 2019-04-19
Filing date: 2019-04-19
Publication date: 2019-08-09
Anticipated expiration: 2039-04-19
Also published as: CN110110139B

Abstract

The method, apparatus and electronic equipment explained the embodiment of the invention provides a kind of pair of recommendation results, the described method includes: obtaining the corresponding prediction model of recommendation results to be explained and receiving the target user of the recommendation results to be explained, and initial sample is constructed, which includes: the content characteristic of recommendation included by recommendation results to be explained and the user characteristics of target user；Prediction model and initial sample are input in the preset local feature diagnostic model unrelated with prediction model, the importance values that each feature clicks prediction probability are obtained；According to the importance values, recommendation results to be explained are explained.It is a kind of model unrelated with prediction model, can be adapted for any prediction model due to preset local feature diagnostic model using the embodiment of the present invention.This improves the versatilities of means of interpretation.

Description

The method, apparatus and electronic equipment that a kind of pair of recommendation results explain

Technical field

The present invention relates to machine learning techniques field, the method explained more particularly to a kind of pair of recommendation results, dress It sets and electronic equipment.

Background technique

Currently, many network application systems all can recommend some contents to user, to better meet user demand.Example Such as: audio/video player system can recommend some videos for user, select for user.Specific recommended method mainly includes walking as follows It is rapid:

Firstly, acquisition training sample, the personal information etc. of the video and user watched including user；Then, Recommended models are trained with the sample of acquisition；Finally by the trained recommended models of the information input of candidate video and target user In.Firstly, the prediction model in recommended models, from extracting feature in the information of candidate video and target user, comprising: video is special It seeks peace user characteristics, candidate video is predicted by the probability that target user clicks, show that target user candidate regards to each Probability is clicked in the prediction of frequency, and then, prediction is clicked the highest candidate video of probability as video to be recommended and pushed away by recommended models It recommends to target user.

In order to be improved to prediction model, it is thus necessary to determine that each feature extracted is to prediction result, i.e., prediction is by point The probability role hit, and then recommendation results are explained.

Currently, there are many common prediction models of recommender system, it can be general to clicking according to the feature of the sample of input Rate is predicted.Such as: GBDT (Gradient Boosting Decision Tree, gradient promote decision tree) prediction model, The prediction model or GBDT and LR that GBDT and FM (Factorization Machine, Factorization machine) are composed The prediction model etc. that (Logistic Regression Classifier, logistic regression) is composed.

For tree-model as GBDT, prediction result can be analyzed using information gain method in the prior art, Information gain method is to carry out judging characteristic by ranking results by being ranked up after each feature calculation information gain to sample Importance, and then recommendation results are explained.Such as: feature A and feature B importance highest are obtained with information gain method, then It is can be when explanation due to there is this 2 highest features of corresponding importance in video to be recommended, so the click probability of prediction is high.

However, inventor has found in the implementation of the present invention, at least there are the following problems for existing scheme:

Existing information gain method can only analyze the prediction result of tree-model as GBDT, cannot be to combination die The prediction result of type is analyzed, such as: GBDT and the FM prediction model being composed or GBDT and LR cannot be composed The prediction result of prediction model analyzed, and after the feature for introducing the above quantity of million ranks, information gain method Also can not it is another one calculate characteristic information gain, can not determine the importance of each feature.

As it can be seen that the method explained in the prior art to recommendation results can not determine each some prediction models The importance of feature, therefore all recommended models are not applied for, versatility is not high.

Summary of the invention

The embodiment of the present invention be designed to provide method, apparatus that a kind of pair of recommendation results explain and electronics is set It is standby, to improve the versatility of means of interpretation.

In order to achieve the above objectives, the embodiment of the invention discloses the methods that a kind of pair of recommendation results explain, comprising:

Obtain the corresponding prediction model of recommendation results to be explained and target user；The recommendation results to be explained include to recommend Content；The target user is the user for receiving the recommendation results to be explained；

Construct initial sample；The initial sample, comprising: the content characteristic of the recommendation and the user of target user Feature；

By the prediction model and initial sample, it is input to the preset local feature diagnosis unrelated with the prediction model In model；To in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain multiple disturbance samples；And it will The initial sample is input to the prediction model, obtains corresponding initial predicted and clicks probability, each disturbance sample is distinguished It is input to the prediction model, probability is clicked in prediction after obtaining corresponding multiple disturbances；And according to initial predicted click probability and The difference between probability is clicked in prediction after each disturbance, obtains the importance values that each feature clicks prediction probability；Wherein, Probability is clicked in the prediction are as follows: the target user that the prediction model is predicted according to input sample is to the recommendation Click probability；

According to the importance values of each feature, the recommendation results to be explained are explained.

Optionally, the content characteristic and/or user characteristics in initial sample is repeatedly disturbed, and obtains multiple disturb The step of dynamic sample, comprising:

From the content characteristic and user characteristics in initial sample, a feature being undisturbed is selected, as spy undetermined Sign；

From the initial sample, the feature undetermined is hidden, primary disturbance is completed, obtains the corresponding disturbance of feature undetermined Sample；Return described from the content characteristic and user characteristics in initial sample, select a feature being undisturbed, as to The step of determining feature.

Optionally, described that the initial sample is input to the prediction model, it obtains corresponding initial predicted and clicks generally The step of rate, comprising:

By the content characteristic and the user characteristics input prediction model in initial sample, corresponding initial predicted point is obtained Hit probability；

It is described that each disturbance sample is separately input into the prediction model, it predicts to click after obtaining corresponding multiple disturbances The step of probability, comprising:

The corresponding content characteristic disturbed in sample of each feature undetermined and user characteristics are inputted into the prediction model, obtained Probability is clicked in prediction after obtaining the corresponding disturbance of each feature undetermined.

Optionally, described that difference between probability is clicked according to prediction after initial predicted click probability and each disturbance, it obtains The step of importance values of probability are clicked for prediction to each feature, comprising:

Prediction after initial predicted clicks probability disturbance corresponding with each feature undetermined is calculated separately to click between probability Difference；

Using each difference of acquisition as the importance values of corresponding feature undetermined.

Optionally, the prediction model includes: the first submodel and the second submodel；

By the prediction model and initial sample, it is input to the preset local feature unrelated with the prediction model and examines Before step in disconnected model, further includes:

To the content characteristic and user characteristics in initial sample, by preset algorithm, all features are divided into dense characteristic And sparse features；

The dense characteristic is input to the first submodel, obtains assemblage characteristic；

It is described that the initial sample is input to the prediction model, obtain the step that corresponding initial predicted clicks probability Suddenly, comprising:

The assemblage characteristic and sparse features are inputted into second submodel, initial predicted is obtained and clicks probability.

Optionally, described by the prediction model and initial sample, it is input to preset unrelated with the prediction model Step in local feature diagnostic model, comprising:

The assemblage characteristic and sparse features are spliced, spliced feature is obtained；

By the prediction model and the spliced feature, it is input to the preset part unrelated with the prediction model In feature diagnostic model.

The spliced feature is repeatedly disturbed, multiple disturbance samples are obtained；

Sample or the sparse spy of disturbance for each disturbance sample, from the disturbance sample, after obtaining disturbance dense characteristic Sample after sign；

Sample after disturbance dense characteristic is input to the first submodel, obtains assemblage characteristic after the first disturbance；

By the sparse features input described the in assemblage characteristic after first disturbance and the sample after disturbance dense characteristic Two submodels, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance dense characteristic；

Or, the dense characteristic of the sample after disturbance sparse features is input to the first submodel, group after the second disturbance is obtained Close feature；

By the sparse features input described the in assemblage characteristic after second disturbance and the sample after disturbance sparse features Two submodels, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance sparse features.

Optionally, first submodel are as follows: gradient promotes decision tree GBDT model, and second submodel returns for logic Return LR model or Factorization machine FM model.

In order to achieve the above object, the embodiment of the invention also discloses the device that a kind of pair of recommendation results explain, packets Include: first obtains module, building module, the second acquisition module and explanation module, wherein

Described first obtains module, for obtaining the corresponding prediction model of recommendation results to be explained and target user；It is described Recommendation results to be explained include recommendation；The target user is the user for receiving the recommendation results to be explained；

The building module, for constructing initial sample；The initial sample, comprising: the content of the recommendation is special It seeks peace the user characteristics of target user；

Described second obtains module, comprising: input submodule, disturbance submodule, probability obtain submodule, disturbance generally Rate obtains submodule and importance values obtain submodule；

The input submodule, for being input to the preset and prediction mould for the prediction model and initial sample In the unrelated local feature diagnostic model of type；

The disturbance submodule, for in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain Obtain multiple disturbance samples；

The probability obtains submodule and is corresponded to for the initial sample to be input to the prediction model Initial predicted click probability；

The disturbance probability obtains submodule, for each disturbance sample to be separately input into the prediction model, obtains Probability is clicked in prediction after corresponding multiple disturbances；

The importance values obtain submodule, general for prediction click after clicking probability and each disturbance according to initial predicted Difference between rate obtains the importance values that each feature clicks prediction probability；Wherein, probability is clicked in the prediction are as follows: Click probability of the target user that the prediction model is predicted according to input sample to the recommendation；

The explanation module carries out the recommendation results to be explained for the importance values according to each feature It explains.

Optionally, the disturbance submodule, comprising:

Selecting unit, for selecting a spy being undisturbed from the content characteristic and user characteristics in initial sample Sign, as feature undetermined；

Unit is disturbed, for from the initial sample, hiding the feature undetermined, primary disturbance is completed, obtains undetermined The corresponding disturbance sample of feature, triggers the selecting unit.

Optionally, the probability obtains submodule, specifically for by initial sample content characteristic and user it is special Sign inputs the prediction model, obtains corresponding initial predicted and clicks probability；

The disturbance probability obtains submodule, specifically for the corresponding content disturbed in sample of each feature undetermined is special User characteristics of seeking peace input the prediction model, and probability is clicked in prediction after obtaining the corresponding disturbance of each feature undetermined.

Optionally, the importance values obtain submodule, specifically for calculate separately initial predicted click probability with it is each The difference between probability is clicked in prediction after the corresponding disturbance of feature undetermined；Using each difference of acquisition as corresponding feature undetermined Importance values.

Described device, further includes:

Division module, for by the prediction model and initial sample, be input to it is preset with the prediction model without Before in the local feature diagnostic model of pass, will own to the content characteristic and user characteristics in initial sample by preset algorithm Feature is divided into dense characteristic and sparse features；

Input module obtains assemblage characteristic for the dense characteristic to be input to the first submodel；

The probability obtains submodule, is specifically used for the assemblage characteristic and sparse features input second son Model obtains initial predicted and clicks probability.

Optionally, the input submodule, comprising:

Concatenation unit obtains spliced feature for splicing the assemblage characteristic and sparse features；

First input unit, for by the prediction model and the spliced feature, be input to it is preset with it is described In the unrelated local feature diagnostic model of prediction model.

Optionally, the disturbance submodule obtains multiple specifically for repeatedly being disturbed to the spliced feature Disturb sample；

The disturbance probability obtains submodule, comprising: obtaining unit, the second input unit and third input unit, or It include: obtaining unit, the 4th input unit and the 5th input unit；

The obtaining unit, for from the disturbance sample, obtaining after disturbing dense characteristic for each disturbance sample Sample after sample or disturbance sparse features；

Second input unit obtains first for the sample after disturbance dense characteristic to be input to the first submodel Assemblage characteristic after disturbance；

The third input unit, for will it is described first disturbance after assemblage characteristic and disturbance dense characteristic after sample in Sparse features input second submodel, prediction is clicked general after the corresponding disturbance of sample after obtaining the disturbance dense characteristic Rate；

4th input unit, for the dense characteristic in the sample after disturbance sparse features to be input to the first submodule Type obtains assemblage characteristic after the second disturbance；

5th input unit, for will it is described second disturbance after assemblage characteristic and disturbance sparse features after sample in Sparse features input second submodel, prediction is clicked general after the corresponding disturbance of sample after obtaining the disturbance sparse features Rate.

In order to achieve the above object, the electronics explained the embodiment of the invention also discloses a kind of pair of recommendation results is set It is standby, including processor, communication interface, memory and communication bus, wherein the processor, the communication interface, the storage Device completes mutual communication by the communication bus；

The memory, for storing computer program；

The processor when for executing the program stored on the memory, realizes any description above to recommendation As a result the method and step explained.

At the another aspect that the present invention is implemented, a kind of computer readable storage medium is additionally provided, it is described computer-readable Instruction is stored in storage medium, when run on a computer so that computer execute it is any of the above-described described to recommendation As a result the method explained.

At the another aspect that the present invention is implemented, the embodiment of the invention also provides a kind of, and the computer program comprising instruction is produced Product, when run on a computer, so that computer executes any of the above-described method explained to recommendation results.

As seen from the above technical solution, the method, apparatus provided in an embodiment of the present invention that recommendation results are explained and Electronic equipment obtains the corresponding prediction model of recommendation results to be explained and receives the target user of the recommendation results to be explained, and Initial sample is constructed, which includes: that the content characteristic of recommendation included by recommendation results to be explained and target are used The user characteristics at family；Prediction model and initial sample are inputted into the preset local feature diagnostic model unrelated with prediction model In, the importance values that each feature clicks prediction probability are obtained, according to the importance values, recommendation results to be explained are carried out It explains.Due to preset local feature diagnostic model, it is a kind of model unrelated with prediction model, can be adapted for any prediction Model.It is thus possible to improve the versatility of means of interpretation.

Certainly, it implements any of the products of the present invention or method must be not necessarily required to reach all the above excellent simultaneously Point.

Detailed description of the invention

In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described.

Fig. 1 a is a kind of flow diagram of the method provided in an embodiment of the present invention explained to recommendation results；

Fig. 1 b is the schematic diagram of the treatment process of local feature diagnostic model in Fig. 1 a illustrated embodiment；

Fig. 2 is another flow diagram of the method provided in an embodiment of the present invention explained to recommendation results；

Fig. 3 is another flow diagram of the method provided in an embodiment of the present invention explained to recommendation results；

Fig. 4 is the structural schematic diagram of the device provided in an embodiment of the present invention explained to recommendation results；

Fig. 5 is the electronic equipment schematic diagram provided in an embodiment of the present invention explained to recommendation results.

Specific embodiment

Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention is described.

In order to solve prior art problem, the method that is explained the embodiment of the invention provides a kind of pair of recommendation results, Device and electronic equipment.This method and device can be applied to various electronic equipments.The embodiment of the present invention is provided first below The method that explains of a kind of pair of recommendation results be introduced.

As shown in Figure 1a, Fig. 1 a is a kind of process of the method provided in an embodiment of the present invention explained to recommendation results Schematic diagram may include:

S101: the corresponding prediction model of recommendation results to be explained and target user are obtained；The recommendation results to be explained include Recommendation；The target user is the user for receiving the recommendation results to be explained.

In practical applications, prediction model can be single model, such as GBDT or DNN (Deep Neural Network, deep neural network) etc. models, be also possible to the built-up pattern of multiple models, such as the built-up pattern of GBDT and LR Or built-up pattern of DNN and FM etc..Such as: for video recommendations, which be can be by inputting multiple use The personal information of video and user that family had been watched and user click the probability for the video that these had been watched, and pass through Repeatedly training, the prediction model that obtained training is completed.

S102: initial sample is constructed；The initial sample, comprising: the content characteristic of recommendation and the user of target user Feature.

Such as: for video recommendations, which may include: feature and the target user of the video of recommendation User characteristics.

S103: by prediction model and initial sample, it is input to the preset local feature diagnosis mould unrelated with prediction model In type；To in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain multiple disturbance samples；And it will be first Beginning sample is input to prediction model, obtains corresponding initial predicted and clicks probability, each disturbance sample is separately input into prediction Model, probability is clicked in prediction after obtaining corresponding multiple disturbances；And it is predicted after according to initial predicted clicking probability and each disturbance The difference between probability is clicked, the importance values that each feature clicks prediction probability are obtained.

In S103, the importance values that each feature clicks probability for prediction refer to: each feature pair of initial sample The importance values of probability are clicked in prediction.

As shown in Figure 1 b, the treatment process of local feature diagnostic model includes:

1) initial sample is input in local feature diagnostic model, and local feature diagnostic model disturbs it, is obtained Multiple disturbance samples；

Specifically, local feature diagnostic model in initial sample content characteristic and/or user characteristics repeatedly disturbed It is dynamic, obtain multiple disturbance samples.

2) simultaneously, initial sample is also directly input to prediction model by local feature diagnostic model, is obtained corresponding initial Probability is clicked in prediction.

3) then, each disturbance sample is separately input into prediction model by local feature diagnostic model, is obtained corresponding more Probability is clicked in prediction after a disturbance.

4) probability is clicked in prediction after then, local diagnosis model clicks probability and each disturbance according to initial predicted, is calculated Importance values of each feature of initial sample for prediction clicking rate.

Specifically, local feature diagnostic model, which can first calculate initial predicted, clicks future position after probability and each disturbance The difference between probability is hit, predicts to click the difference between probability after then clicking probability and each disturbance according to initial predicted, The each feature for obtaining initial sample clicks prediction the importance values of probability.

5) finally, local feature diagnostic model exports the importance values being calculated.

The above are the treatment processes of local feature diagnostic model, and process, can obtain the every of initial sample through this process A feature clicks prediction the importance values of probability, so as to further the importance values be utilized to tie recommendation to be explained Fruit explains.

S104: according to the importance values of each feature, recommendation results to be explained are explained.

The importance values of each feature of initial sample, it is big can to represent the contribution that this feature clicks probability for prediction It is small.

Embodiment shown in application drawing 1a, the initial sample of the corresponding prediction model of recommendation results to be explained and building is defeated Enter in the preset local feature diagnostic model unrelated with prediction model, obtains each feature and the important of probability is clicked for prediction Property value explains recommendation results to be explained according to the importance values.It is one due to preset local feature diagnostic model The kind model unrelated with prediction model, can be adapted for any prediction model.This improves the versatilities of means of interpretation.

Further, the embodiment of the invention provides another flow chart of the method explained to recommendation results, In the embodiment, using local feature diagnostic model, select to be undisturbed from the content characteristic and user characteristics in initial sample Feature, this feature is hidden, obtain disturbance sample, so that the importance values of each feature are obtained, to explain recommendation to be explained As a result.Specifically, as shown in Fig. 2, may include:

S201: the corresponding prediction model of recommendation results to be explained and target user are obtained；The recommendation results to be explained include Recommendation；The target user is the user for receiving the recommendation results to be explained.

Recommendation can be video, be also possible to audio or image etc., specifically without limitation.Recommendation knot to be explained The corresponding prediction model of fruit can predict the probability that the recommendation results are clicked by target user.

S202: initial sample is constructed；The initial sample, comprising: the content characteristic of recommendation and the user of target user Feature.

In one case, recommendation is video, then the content characteristic of recommendation, can be the label of video, such as Performer, film, sitcom etc. or video length, video issuing date etc..The user characteristics of target user, can be use The personal information at family, such as user's gender, age of user or user are in the number of registration etc. of audio/video player system.In this way, building Initial sample can be the content characteristic and user characteristics for constructing initial sample.

In the present embodiment, one or more initial samples can be constructed.Specifically, when target user is one, it can be with Using the user characteristics of the target user and the target user received recommendation results to be explained recommendation content it is special Sign is to construct an initial sample.When target user is multiple, it can be directed to each target user, utilize the target user's User characteristics and the target user the recommendation of received recommendation results to be explained content characteristic it is initial to construct one Sample, to construct multiple initial samples.

S203: by prediction model and initial sample, it is input to the preset local feature diagnosis mould unrelated with prediction model In type；To in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain multiple disturbance samples；It will be initial Content characteristic and user characteristics input prediction model in sample obtain corresponding initial predicted and click probability；It will be each undetermined Content characteristic and user characteristics input prediction model in the corresponding disturbance sample of feature, each feature undetermined of acquisition is corresponding to disturb Probability is clicked in prediction after dynamic；Prediction after initial predicted clicks probability disturbance corresponding with each feature undetermined is calculated separately to click generally Difference between rate；Using each difference of acquisition as the importance values of corresponding feature undetermined；Wherein, probability is clicked in prediction Are as follows: click probability of the target user that prediction model is predicted according to input sample to recommendation.

In a kind of situation, if one initial sample of building, the feature of prediction model and initial sample can be formed Vector inputs in above-mentioned local feature diagnostic model.

In another case, if the multiple initial samples of building, it can be defeated with a matrix type by multiple initial samples Enter in above-mentioned local feature diagnostic model, in the matrix, one initial sample of each behavior, above-mentioned local feature diagnostic model Data in matrix can be handled line by line.Here processing includes: the processing 1 in above-mentioned steps 103) -5).

In S203 in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain multiple disturbance samples This, may include steps of:

1), from the content characteristic and user characteristics in initial sample, a feature being undisturbed is selected, as undetermined Feature.

2), from initial sample, feature undetermined is hidden, primary disturbance is completed, obtains the corresponding disturbance sample of feature undetermined.

Wherein, feature undetermined is hidden, can be and set 0 for the value of feature undetermined.

3) it, returns from the content characteristic and user characteristics in initial sample, selects a feature being undisturbed, as The step of feature undetermined.

By above three step, each feature in initial sample can once be disturbed, to obtain multiple Disturb sample.

For example, it is assumed that the content characteristic and user characteristics in initial sample are five features of A, B, C, D, E, one is selected A feature A being undisturbed hides feature A as feature undetermined, obtain the corresponding disturbance sample of feature A be [0, B, C, D, E], then the reselection one feature B being undisturbed hides feature B as feature undetermined, obtains the corresponding disturbance of feature B Sample is [A, 0, C, D, E], and so on, obtain multiple disturbance samples.

It, can be by the content characteristic and user characteristics input prediction mould in initial sample after obtaining multiple disturbance samples Type obtains corresponding initial predicted and clicks probability；By the corresponding content characteristic disturbed in sample of each feature undetermined and user Feature input prediction model, probability is clicked in prediction after obtaining the corresponding disturbance of each feature undetermined；Calculate separately initial predicted point It hits prediction after probability disturbance corresponding with each feature undetermined and clicks difference between probability；Using each difference of acquisition as pair The importance values for the feature undetermined answered.

Continue above-mentioned example, it is assumed that by the content characteristic and user characteristics A, B, C, D, E in initial sample, input prediction mould Type, obtaining initial predicted and clicking probability is 0.6, and feature A is hidden, the corresponding disturbance sample of feature A [0, B, C, D, E] is inputted Prediction model, prediction click probability is 0.1 after obtaining the corresponding disturbance of feature A, then calculates initial predicted and click probability and feature A The difference between probability is clicked in prediction after corresponding disturbance, and obtaining the difference is 0.6-0.1=0.5, in this way, the difference 0.5 is just It is the importance values of feature A undetermined.

Certainly, which is also possible to negative, for example, obtaining the corresponding disturbance sample of feature B after feature B undetermined is hidden This [A, 0, C, D, E], by the disturbance sample input prediction model, prediction click probability is after obtaining the corresponding disturbance of feature B 0.8, then it calculates after initial predicted clicks probability disturbance corresponding with feature B and predicts to click the difference between probability, obtain the difference Value is 0.6-0.8=-0.2, in this way, difference -0.2 is exactly the importance values of feature B undetermined.

Above-mentioned input sample refers to: the user characteristics of target user and the feature of Candidate Recommendation result.For example, Candidate Recommendation As a result are as follows: candidate video.

S204: according to the importance values of each feature, recommendation results to be explained are explained.

Due to initial predicted click prediction after corresponding with the feature undetermined disturbance of probability click difference between probability can be with It is positive number, negative or 0, and the difference just represents importance values, so, the importance values of each feature are also possible to positive number, bear Number or 0.

The importance values can represent the contribution that feature clicks prediction probability.Importance values are bigger, corresponding The contribution that feature clicks probability for prediction is bigger, on the contrary, importance values are smaller, corresponding feature clicks probability for prediction Contribution it is smaller.

For example, it is assumed that the importance values of feature A are 0.1, the importance values of feature B are -0.3, then feature A treats solution It releases recommendation results and plays positive contribution, and feature B plays negative sense contribution to recommendation results to be explained.

In a kind of situation, according to the importance values of each feature, recommendation results to be explained are explained, may is that pair The importance values of each feature, are ranked up by size, according to ranking results, choose the maximum importance values pair of preset quantity The feature answered explains recommendation results to be explained.

For example, the importance values of feature A are 0.1, the importance values of feature B are -0.3, and the importance values of feature C are 0.5, The importance values of feature D are 0.8, then choose importance values maximum two features D and C, explain recommendation results to be explained.

In another case, can also be ranked up, the contribution margin of each feature according to contribution margin by contribution margin size Size chooses the maximum feature of preset quantity contribution margin, explains recommendation results to be explained；Or it chooses and recommendation to be explained is tied Fruit plays the feature of positive contribution, explains recommendation results to be explained.

For example, continuing above-mentioned example, the maximum feature D of contribution margin is chosen, explains recommendation results to be explained；Or selection pair Recommendation results to be explained play feature D, C and A of positive contribution, explain recommendation results to be explained.

2 the embodiment described of application drawing is somebody's turn to do by obtaining the corresponding prediction model of recommendation results to be explained and receiving wait solve The target user of recommendation results is released, and constructs initial sample, prediction model and initial sample are inputted into local feature diagnostic model In；Intact feature is selected from the content characteristic and user characteristics in initial sample, this feature is hidden, obtains disturbance sample This, to obtain initial sample and disturb the difference of sample, as the importance values for being hidden feature, to explain recommendation to be explained As a result.Due to preset local feature diagnostic model, it is a kind of model unrelated with prediction model, can be adapted for any prediction Model.This improves the versatilities of means of interpretation.

Further, the embodiment of the invention also provides another processes of the method explained to recommendation results Scheme, in the embodiment, prediction model may include the first submodel and the second submodel, by all features in initial sample It is combined and splices, obtain splicing feature, input in preset local feature diagnosis algorithm, obtain the importance of each feature Value, to explain recommendation results.Specifically, as shown in figure 3, may include:

S301: the corresponding prediction model of recommendation results to be explained and target user are obtained；The recommendation results to be explained include Recommendation；The target user is the user for receiving the recommendation results to be explained.

In a kind of situation, the corresponding prediction model of recommendation results to be explained, may include the first submodel and the second submodule Type, the first submodel can be GBDT or DNN, for handling dense characteristic, not support processing higher-dimension sparse features；Second son Model can be LR or FM, for handling sparse features.

S302: initial sample is constructed；The initial sample, comprising: the content characteristic of recommendation and the user of target user Feature.

S303: all features are divided into dense by the content characteristic and user characteristics in initial sample by preset algorithm Feature and sparse features.

For example, if initial sample includes the feature for the video recommended and the user characteristics of target user, this is first May include in the feature of beginning sample the dense characteristics such as age of user, user's gender or video length and user number of registration or The sparse features such as video tab.

After constructing initial sample, all features in initial sample can be numbered, by preset algorithm, will be owned Feature is divided into dense characteristic and sparse features.Specifically, can be vector by each character representation in initial sample, it will be to The corresponding feature of number for being less than preset threshold is divided into dense characteristic, will be greater than pre- by number of the length of amount as feature If the corresponding feature of the number of threshold value is divided into sparse features.For example, preset threshold is 100,000, then the spy less than 100,000 will be numbered Sign is determined as dense characteristic, and the feature by number greater than 100,000 is determined as sparse features.

S304: dense characteristic is input to the first submodel, obtains assemblage characteristic.

In practical applications, after dense characteristic being inputted the first submodel, dense characteristic can traverse the first submodel automatically In all feature classifications all features in dense characteristic are automatically selected according to entropy production so that dense spy All features of sign are divided into multiclass, and every one kind is an assemblage characteristic, and the feature discretization in every one kind is encoded, group is obtained Feature vector is closed, so as to machine recognition processing.Since the feature quantity of initial sample is huge, the assemblage characteristic typically resulted in is height Tie up assemblage characteristic.

It, can be by assemblage characteristic and sparse features input prediction model after obtaining assemblage characteristic in a kind of situation Second submodel, the second submodel can calculate the feature of input, obtain initial predicted and click probability.

S305: assemblage characteristic and sparse features are spliced, and obtain spliced feature.

In a kind of situation, assemblage characteristic and sparse features are spliced, spliced feature is obtained, can be will combine Feature generate feature vector, and by sparse features generate higher-dimension sparse features vector spliced, obtain splicing feature to Amount.

For example, the feature vector of assemblage characteristic is [a₁、a₂、…a_n], higher-dimension sparse features vector is [b₁、0、0、 0…b₂], the feature vector of assemblage characteristic and higher-dimension sparse features vector are spliced, obtained splicing feature vector is [a₁、 a₂、…a_n、b₁、0、0、0…b₂]。

S306: it by prediction model and spliced feature, is input to the preset local feature unrelated with prediction model and examines In disconnected model, the importance values that each feature clicks prediction probability are obtained.

In practical applications, using local feature diagnostic model, the spliced feature of input is repeatedly disturbed, is obtained Obtain multiple disturbance samples；Sample or disturbance for each disturbance sample, from the disturbance sample, after obtaining disturbance dense characteristic Sample after sparse features；

For example, continuing above-mentioned example, for the spliced feature [a of input₁、a₂、…a_n、b₁、0、0、0…b₂], it will be thick Close feature A₁Corresponding vector element a₁It hides, obtains disturbance dense characteristic A₁Sample [0, a afterwards₂、…a_n、b₁、0、0、0… b₂], or by sparse features B₁Corresponding vector element b₁It hides, obtains disturbance sparse features B₁Sample [a afterwards₁、a₂、…a_n、 0、0、0、0…b₂]。

Sample after disturbance dense characteristic is input to the first submodel, obtains assemblage characteristic after the first disturbance；By first Assemblage characteristic and the sparse features in the sample after disturbance dense characteristic input the second submodel after disturbance, and it is dense to obtain the disturbance Probability is clicked in prediction after the corresponding disturbance of sample after feature；

Specifically, the first submodel handles the dense characteristic in the sample after disturbance dense characteristic, first is obtained Assemblage characteristic after disturbance, the second submodel is to the sparse spy in assemblage characteristic after the first disturbance and the sample after disturbance dense characteristic Sign is handled, and probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance dense characteristic.

Alternatively, the dense characteristic in the sample after disturbance sparse features is input to the first submodel, the second disturbance is obtained Assemblage characteristic afterwards；Sparse features in assemblage characteristic after second disturbance and the sample after disturbance sparse features are inputted into the second submodule Type, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance sparse features.

Specifically, the first submodel handles the dense characteristic in the sample after disturbance sparse features, second is obtained Assemblage characteristic after disturbance, the second submodel is to the sparse spy in assemblage characteristic after the second disturbance and the sample after disturbance sparse features Sign is handled, and probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance sparse features.

After probability is clicked in prediction after obtaining the corresponding disturbance of sample after disturbing dense characteristic, according to initial predicted point The difference between probability is clicked in prediction after the corresponding disturbance of sample after hitting probability and each disturbance dense characteristic, alternatively, obtaining After probability is clicked in prediction after the corresponding disturbance of sample after sparse features must be disturbed, probability and each is clicked according to initial predicted The difference between probability is clicked in prediction after the corresponding disturbance of sample after disturbing sparse features, obtains each feature for future position Hit the importance values of probability.Here, the importance values that each feature clicks probability for prediction refer to: in splicing feature vector Each feature clicks prediction the importance values of probability.

S307: according to the importance values of each feature, recommendation results to be explained are explained.

It using embodiment shown in Fig. 3, is combined, spliced by the feature to initial sample, obtain splicing feature, it will Splicing feature inputs in the preset local feature diagnostic model unrelated with prediction model, obtains each feature and prediction is clicked The importance values of probability, and according to the importance values, explain recommendation results.It is one due to preset local feature diagnostic model The kind model unrelated with prediction model, can be adapted for any prediction model.This improves the versatilities of means of interpretation.

Corresponding to embodiment of the method shown in Fig. 1 a, the embodiment of the invention also provides a kind of pair of recommendation results to explain Device, as shown in figure 4, the apparatus may include: first obtain module 401, building module 402, second obtain module 403 With explanation module 404, wherein

Described first obtains module 401, for obtaining the corresponding prediction model of recommendation results to be explained and target user；Institute Stating recommendation results to be explained includes recommendation；The target user is the user for receiving the recommendation results to be explained；

The building module 402, for constructing initial sample；The initial sample, comprising: the content of the recommendation The user characteristics of feature and target user；

Described second obtains module 403, comprising: input submodule, disturbance submodule, probability obtain submodule, disturb Dynamic probability obtains submodule and importance values obtain submodule；

The explanation module 404, for the importance values according to each feature, to the recommendation results to be explained into Row is explained.

Using embodiment shown in Fig. 4, obtains the corresponding prediction model of recommendation results to be explained and receive this and to be explained push away The target user of result is recommended, and constructs initial sample, which includes: recommendation included by recommendation results to be explained Content characteristic and target user user characteristics；Prediction model and initial sample are inputted preset unrelated with prediction model In local feature diagnostic model, obtains the importance values that each feature clicks probability for prediction and treated according to the importance values Explain that recommendation results explain.It is a kind of model unrelated with prediction model due to preset local feature diagnostic model, it can To be suitable for any prediction model.This improves the versatilities of means of interpretation.

Specifically, in the present embodiment, the disturbance submodule may include:

Specifically, in the present embodiment, the probability obtains submodule, specifically for by the content in initial sample Feature and user characteristics input the prediction model, obtain corresponding initial predicted and click probability；

Specifically, in the present embodiment, the importance values obtain submodule, specifically for calculating separately initial predicted point It hits prediction after probability disturbance corresponding with each feature undetermined and clicks difference between probability；Using each difference of acquisition as pair The importance values for the feature undetermined answered.

Specifically, in the present embodiment, the prediction model includes: the first submodel and the second submodel；

Described device, further includes:

Division module (not shown), for being input to preset and institute by the prediction model and initial sample Before stating in the unrelated local feature diagnostic model of prediction model, to the content characteristic and user characteristics in initial sample, by pre- All features are divided into dense characteristic and sparse features by imputation method；

Input module (not shown) obtains assemblage characteristic for the dense characteristic to be input to the first submodel；

Specifically, in the present embodiment, the input submodule, comprising:

Specifically, in the present embodiment, the disturbance submodule is specifically used for carrying out the spliced feature multiple Disturbance, obtains multiple disturbance samples；

The obtaining unit is used for for each disturbance sample, from the disturbance sample, after being disturbed after dense characteristic Sample or disturbance after sample after sparse features；

Specifically, in the present embodiment, first submodel are as follows: gradient promotion decision tree GBDT model, described second Submodel is logistic regression LR model or Factorization machine FM model.

The embodiment of the invention also provides the electronic equipments that a kind of pair of recommendation results explain, as shown in figure 5, including place Manage device 501, communication interface 502, memory 503 and communication bus 504, wherein processor 501, communication interface 502, memory 503 complete mutual communication by communication bus 504,

Memory 503, for storing computer program；

Processor 501 when for executing the program stored on memory 503, realizes following steps:

As it can be seen that obtaining the corresponding prediction model of recommendation results to be explained in scheme provided by the embodiment of the present invention and connecing The target user of the recommendation results to be explained is received, and constructs initial sample, which includes: that recommendation results to be explained are wrapped The content characteristic of the recommendation included and the user characteristics of target user；The input of the feature of prediction model and initial sample is default The local feature diagnostic model unrelated with prediction model in, obtain each feature for prediction click probability importance values, According to the importance values, recommendation results to be explained are explained.It is a kind of and pre- due to preset local feature diagnostic model The unrelated model of model is surveyed, can be adapted for any prediction model.This improves the versatilities of means of interpretation.

The communication bus that above-mentioned electronic equipment is mentioned can be Peripheral Component Interconnect standard (Peripheral Component Interconnect, abbreviation PCI) bus or expanding the industrial standard structure (Extended Industry Standard Architecture, abbreviation EISA) bus etc..The communication bus can be divided into address bus, data/address bus, control bus etc.. Only to be indicated with a thick line in figure, it is not intended that an only bus or a type of bus convenient for indicating.

Communication interface is for the communication between above-mentioned electronic equipment and other equipment.

Memory may include random access memory (Random Access Memory, abbreviation RAM), also may include Nonvolatile memory (non-volatile memory), for example, at least a magnetic disk storage.Optionally, memory may be used also To be storage device that at least one is located remotely from aforementioned processor.

Above-mentioned processor can be general processor, including central processing unit (Central Processing Unit, Abbreviation CPU), network processing unit (Network Processor, abbreviation NP) etc.；It can also be digital signal processor (Digital Signal Processing, abbreviation DSP), specific integrated circuit (Application Specific Integrated Circuit, abbreviation ASIC), field programmable gate array (Field-Programmable Gate Array, Abbreviation FPGA) either other programmable logic device, discrete gate or transistor logic, discrete hardware components.

In another embodiment provided by the invention, a kind of computer readable storage medium is additionally provided, which can It reads to be stored with instruction in storage medium, when run on a computer, so that computer executes any institute in above-described embodiment The method that recommendation results are explained stated, to obtain identical technical effect.

In another embodiment provided by the invention, a kind of computer program product comprising instruction is additionally provided, when it When running on computers, so that computer executes any side explained to recommendation results in above-described embodiment Method, to obtain identical technical effect.

In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real It is existing.When implemented in software, it can entirely or partly realize in the form of a computer program product.The computer program Product includes one or more computer instructions.When loading on computers and executing the computer program instructions, all or It partly generates according to process or function described in the embodiment of the present invention.The computer can be general purpose computer, dedicated meter Calculation machine, computer network or other programmable devices.The computer instruction can store in computer readable storage medium In, or from a computer readable storage medium to the transmission of another computer readable storage medium, for example, the computer Instruction can pass through wired (such as coaxial cable, optical fiber, number from a web-site, computer, server or data center User's line (DSL)) or wireless (such as infrared, wireless, microwave etc.) mode to another web-site, computer, server or Data center is transmitted.The computer readable storage medium can be any usable medium that computer can access or It is comprising data storage devices such as one or more usable mediums integrated server, data centers.The usable medium can be with It is magnetic medium, (for example, floppy disk, hard disk, tape), optical medium (for example, DVD) or semiconductor medium (such as solid state hard disk Solid State Disk (SSD)) etc..

It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that There is also other identical elements in process, method, article or equipment including the element.

Each embodiment in this specification is all made of relevant mode and describes, same and similar portion between each embodiment Dividing may refer to each other, and each embodiment focuses on the differences from other embodiments.Especially for device reality For applying example, apparatus embodiments and storage medium embodiment, since it is substantially similar to the method embodiment, so the comparison of description Simply, the relevent part can refer to the partial explaination of embodiments of method.

The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all Any modification, equivalent replacement, improvement and so within the spirit and principles in the present invention, are all contained in protection scope of the present invention It is interior.

Claims

1. the method that a kind of pair of recommendation results explain, which is characterized in that the described method includes:

Obtain the corresponding prediction model of recommendation results to be explained and target user；The recommendation results to be explained include in recommending Hold；The target user is the user for receiving the recommendation results to be explained；

Construct initial sample；The initial sample, comprising: the content characteristic of the recommendation and the user of target user are special Sign；

By the prediction model and initial sample, it is input to the preset local feature diagnostic model unrelated with the prediction model In；To in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain multiple disturbance samples；And it will be described Initial sample is input to the prediction model, obtains corresponding initial predicted and clicks probability, each disturbance sample is inputted respectively To the prediction model, probability is clicked in prediction after obtaining corresponding multiple disturbances；And probability and each is clicked according to initial predicted The difference between probability is clicked in prediction after disturbance, obtains the importance values that each feature clicks prediction probability；Wherein, described Probability is clicked in prediction are as follows: click of the target user that the prediction model is predicted according to input sample to the recommendation Probability；

2. the method according to claim 1, wherein

The content characteristic and/or user characteristics in initial sample is repeatedly disturbed, and the step of multiple disturbance samples is obtained Suddenly, comprising:

From the content characteristic and user characteristics in initial sample, a feature being undisturbed is selected, as feature undetermined；

From the initial sample, the feature undetermined is hidden, primary disturbance is completed, obtains the corresponding disturbance sample of feature undetermined This；Return to described from the content characteristic and user characteristics in initial sample, one feature being undisturbed of selection, as undetermined The step of feature.

3. according to the method described in claim 2, it is characterized in that,

It is described that the initial sample is input to the prediction model, obtain the step of corresponding initial predicted clicks probability, packet It includes:

By the content characteristic and the user characteristics input prediction model in initial sample, obtains corresponding initial predicted and click generally Rate；

Described that each disturbance sample is separately input into the prediction model, probability is clicked in prediction after obtaining corresponding multiple disturbances The step of, comprising:

The corresponding content characteristic disturbed in sample of each feature undetermined and user characteristics are inputted into the prediction model, obtained each Probability is clicked in prediction after the corresponding disturbance of a feature undetermined.

4. according to the method described in claim 3, it is characterized in that,

It is described that difference between probability is clicked according to prediction after initial predicted click probability and each disturbance, obtain each feature pair In the step of importance values of probability are clicked in prediction, comprising:

It calculates separately initial predicted and clicks prediction after probability disturbance corresponding with each feature undetermined and click difference between probability；

5. the method according to claim 1, wherein the prediction model, includes: the first submodel and the second son Model；

By the prediction model and initial sample, it is input to the preset local feature diagnosis mould unrelated with the prediction model Before step in type, further includes:

To the content characteristic and user characteristics in initial sample, by preset algorithm, all features are divided into dense characteristic and dilute Dredge feature；

6. according to the method described in claim 5, it is characterized in that,

It is described by the prediction model and initial sample, be input to the preset local feature diagnosis unrelated with the prediction model Step in model, comprising:

By the prediction model and the spliced feature, it is input to the preset local feature unrelated with the prediction model In diagnostic model.

7. according to the method described in claim 6, it is characterized in that,

For each disturbance sample, from the disturbance sample, after the sample or disturbance sparse features after obtaining disturbance dense characteristic Sample；

By sparse features input second son in assemblage characteristic after first disturbance and the sample after disturbance dense characteristic Model, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance dense characteristic；

Or, the dense characteristic of the sample after disturbance sparse features is input to the first submodel, spy is combined after obtaining the second disturbance Sign；

By sparse features input second son in assemblage characteristic after second disturbance and the sample after disturbance sparse features Model, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance sparse features.

8. according to the described in any item methods of claim 5~7, which is characterized in that first submodel are as follows: gradient promotion is determined Plan tree GBDT model, second submodel are logistic regression LR model or Factorization machine FM model.

9. the device that a kind of pair of recommendation results explain, which is characterized in that described device includes: the first acquisition module, building Module, second obtain module and explanation module, wherein

Described first obtains module, for obtaining the corresponding prediction model of recommendation results to be explained and target user；It is described wait solve Releasing recommendation results includes recommendation；The target user is the user for receiving the recommendation results to be explained；

The building module, for constructing initial sample；The initial sample, comprising: the content characteristic of the recommendation and The user characteristics of target user；

Described second obtains module, comprising: input submodule, disturbance submodule, probability obtain submodule, disturbance probability obtains It obtains submodule and importance values obtains submodule；

The input submodule, for by the prediction model and initial sample, be input to it is preset with the prediction model without In the local feature diagnostic model of pass；

The disturbance submodule, for in initial sample content characteristic and/or user characteristics repeatedly disturbed, obtain it is more A disturbance sample；

The probability obtains submodule, for the initial sample to be input to the prediction model, obtains corresponding first Begin prediction click probability；

The disturbance probability obtains submodule and is corresponded to for each disturbance sample to be separately input into the prediction model Multiple disturbances after prediction click probability；

The importance values obtain submodule, for according to initial predicted click prediction after probability and each disturbance click probability it Between difference, obtain each feature for prediction click probability importance values；Wherein, probability is clicked in the prediction are as follows: described Click probability of the target user that prediction model is predicted according to input sample to the recommendation；

The explanation module explains the recommendation results to be explained for the importance values according to each feature.

10. device according to claim 9, which is characterized in that the disturbance submodule, comprising:

Selecting unit is made for selecting a feature being undisturbed from the content characteristic and user characteristics in initial sample For feature undetermined；

Unit is disturbed, for from the initial sample, hiding the feature undetermined, primary disturbance is completed, obtains feature undetermined Corresponding disturbance sample, triggers the selecting unit.

11. device according to claim 10, which is characterized in that

The probability obtains submodule, specifically for by initial sample content characteristic and user characteristics input it is described pre- Model is surveyed, corresponding initial predicted is obtained and clicks probability；

The disturbance probability obtains submodule, specifically for by the corresponding disturbance sample of each feature undetermined content characteristic and User characteristics input the prediction model, and probability is clicked in prediction after obtaining the corresponding disturbance of each feature undetermined.

12. device according to claim 11, which is characterized in that

The importance values obtain submodule, corresponding with each feature undetermined specifically for calculating separately initial predicted click probability Disturbance after prediction click probability between difference；Using each difference of acquisition as the importance values of corresponding feature undetermined.

13. device according to claim 9, which is characterized in that the prediction model includes: the first submodel and second Submodel；

Described device, further includes:

Division module, for being input to preset unrelated with the prediction model by the prediction model and initial sample Before in local feature diagnostic model, to the content characteristic and user characteristics in initial sample, by preset algorithm, by all features It is divided into dense characteristic and sparse features；

The probability obtains submodule, is specifically used for the assemblage characteristic and sparse features inputting second submodule Type obtains initial predicted and clicks probability.

14. device according to claim 13, which is characterized in that the input submodule, comprising:

First input unit, for being input to the preset and prediction for the prediction model and the spliced feature In the unrelated local feature diagnostic model of model.

15. device according to claim 14, which is characterized in that

The disturbance submodule obtains multiple disturbance samples specifically for repeatedly being disturbed to the spliced feature；

The disturbance probability obtains submodule, comprising: obtaining unit, the second input unit and third input unit, or include: Obtaining unit, the 4th input unit and the 5th input unit；

The obtaining unit, for the sample for each disturbance sample, from the disturbance sample, after obtaining disturbance dense characteristic Or the sample after disturbance sparse features；

Second input unit obtains the first disturbance for the sample after disturbance dense characteristic to be input to the first submodel Assemblage characteristic afterwards；

The third input unit, for will it is described first disturbance after assemblage characteristic and disturbance dense characteristic after sample in it is dilute It dredges feature and inputs second submodel, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance dense characteristic；

4th input unit, for the dense characteristic in the sample after disturbance sparse features to be input to the first submodel, Assemblage characteristic after the second disturbance of acquisition；

5th input unit, for will it is described second disturbance after assemblage characteristic and disturbance sparse features after sample in it is dilute It dredges feature and inputs second submodel, probability is clicked in prediction after the corresponding disturbance of sample after obtaining the disturbance sparse features.

16. 3~15 described in any item devices according to claim 1, which is characterized in that first submodel are as follows: gradient mentions Decision tree GBDT model is risen, second submodel is logistic regression LR model or Factorization machine FM model.

17. a kind of electronic equipment, which is characterized in that including processor, communication interface, memory and communication bus, wherein described Processor, the communication interface, the memory complete mutual communication by the communication bus；

The memory, for storing computer program；

The processor when for executing the program stored on the memory, realizes any side claim 1-8 Method step.