CN103455938B - A kind of data processing method, device and server apparatus - Google Patents

Abstract

The invention discloses a kind of data processing method, device and server apparatus.Described method includes: record the Times of Feedback of the numerical value that the feedback data of each version of product is corresponding and feedback data；Obtain the weighted value that each version of product is corresponding；The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version.

Description

A kind of data processing method, device and server apparatus

Technical field

The present invention relates to computer science and technology field, particularly relate to a kind of data processing method, device and server apparatus.

Background technology

Along with the fast development of information technology and the Internet, derive the website of various user-generated content (UGC), forum and mhkc etc., and in these UGC, user's participation is the highest, threshold is minimum comments on marking system exactly.Almost there is the comment marking system that user participates in each forum, mhkc, portal website, video website.For a user, comment scoring is almost without any difficult point, if general registration of website, it is provided that and some individual true or false essential informations can be carried out scoring.Exactly because comment scoring is simple, do not have a threshold, but the reason that user's attention rate, participation are high, also allow some information such as advertisement, pornographic be full of easily wherein, affect Consumer's Experience.

Additionally, user is also by other people comment scoring in its comment marking system, decide whether to buy or use a certain commodity, and determine that, in the factor whether user downloaded, bought, browses some commodity, the scoring of these commodity is an important factor at these.Scoring likely directly affects these commodity ranking on whole market and sales volume.

Current score calculation method is only the most original weight computation method.What adopt based on the score calculation of weighting is the average of different specific weight data, calculates according to rational ratio by initial data, if in n number, x1 occurs f1 time, and x2 occurs f2 time ..., xk occurs fk time, then (x1f1+x2f2+...xkfk)/(f1+f2+...+fk) is called x1, x2, ..., the weighted mean of xk, wherein f1, f2 ..., fk can regard x1 as, x2 ..., the weight that xk is corresponding.Ratio shared by initial data is fixing.

But considering that same commodity user marks this kind of factor of quantity owing to weighting is simply simple, entirely without the impact on it of the renewals iteration of reference commodity, user is very difficult therefrom obtains more real score information.

Summary of the invention

The embodiment of the present invention provides a kind of data processing method, device and server apparatus, for realizing field feedback is more accurately analyzed.

A kind of data processing method, the method includes:

Record the Times of Feedback of the numerical value that the feedback data of each version of product is corresponding and feedback data；

Obtain the weighted value that each version of product is corresponding；

The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version.

In this programme, consider the iterative of some products, by distributing different weighted values to the different spaces of a whole page of product, so, when calculating the final feedback data of this product, the feedback data impact for final feedback data of different editions product can be embodied so that the analysis of user feedback data is more accurate.

Preferably, include according to the final feedback data of the weighted value counting yield of numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version: the final feedback data according to below equation counting yield,

$S=\frac{\underset{j=1}{\overset{m}{\mathrm{\Σ}}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{C}_{\mathrm{ij}}{N}_{\mathrm{ij}}{W}_j}{\underset{j=1}{\overset{m}{\mathrm{\Σ}}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{N}_{\mathrm{ij}}{W}_j},$

Wherein, S is the final feedback data of this product；C_ijFor the numerical value that the i-th feedback data to described product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to described product jth version；W_jWeighted value for described product jth version；I=1,2,3......n, j=1,2,3......m, n is the number of feedback data, and m is the number of version.

In the present embodiment, by the new and old order according to product version, for each version right of distribution weight values, so, when calculating the final feedback data of described product, the feedback data impact for final feedback data of different editions product can be embodied so that the analysis of user feedback data is more accurate.

Preferably, the weighted value obtaining each version of product corresponding includes: calculate, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\mathrm{\Π}}}(1-t_{j-1}),$

Wherein, W_jFor the weighted value of described product jth version, j=1,2,3...m；M is described product version number；t_jFor the difference in version value between jth version and+1 version of jth, wherein t₀=0。

Preferably, described difference in version value is calculated by below equation:

Work as z_j<y_jTime, described difference in version value

Work as z_j≥y_jTime, described difference in version value

Described z_jFor the code update increment obtained according to the difference between described product jth version and+1 version of jth；y_jSize of code for described product jth version.

In this programme, by the weighted value that each version of code update incremental computations between the size of code according to each version of product and adjacent version is corresponding, the weighted value making each version embodies the difference between adjacent version, the feedback data impact for final feedback data of different editions product can be embodied more exactly so that the analysis of user feedback data is more accurate.

Preferably, the weighted value that each version of described product is corresponding is arithmetic progression or Geometric Sequence, or weighted value corresponding to each version of described product obtains according to the table lookup pre-set.

In this programme, by the new and old order according to product version, the weighted value successively decreased is distributed for each version, so, when calculating the final feedback data of described product, it is possible to embodying the feedback data impact for final feedback data of new and old edition product, namely final feedback data is had the greatest impact by the feedback data of latest edition product, and more the feedback data of legacy version product is more little on the impact of final feedback data so that the analysis of user feedback data is more accurate.

Preferably, the weighted value obtaining each version of product corresponding includes:

Detect product version corresponding to described feedback data whether record；

When the product version that described feedback data is corresponding records, obtain the weighted value that the described product version recorded is corresponding；

When the product version that described feedback data is corresponding does not record, recalculate the weighted value that the product version recorded is corresponding with Unrecorded product version.

In the present embodiment, by judging that whether feedback data is for redaction product, when feedback data is for redaction product, namely product updates, need to adjust each weighted value of product, the weighted value making latest edition corresponding is maximum, and the weighted value that more legacy version is corresponding is more little so that the analysis of user feedback data is more accurate.When product does not update, then obtain the weighted value that existing version is corresponding, be analyzed user feedback data calculating.

Preferably, described method also includes:

The ID of described feedback data submitted in record；

Detect described user according to described ID and whether the same version of identical product is had been filed on feedback data；

When the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product is deleted by described user, the feedback data submitted to after being retained in.

In this programme, it is possible to avoid same user that described product carries out malice feedback repeatedly, it is also possible to upgrade in time the user's up-to-date feedback data to described product so that the analysis of user feedback data is more accurate.

Preferably, described method also includes:

The ID of described feedback data submitted in record；

Detect the number of times of the feedback data that described ID one of them version to the number of times of the feedback data that described product is submitted to or to described product is submitted to；

When the number of times of the feedback data that the number of times to the feedback data that described product is submitted to or one of them version to described product are submitted to exceedes default first threshold, do not record the feedback data that described ID is submitted to.

In this programme, it is possible to avoid same user that described product carries out malice feedback repeatedly, it is also possible to upgrade in time the user's up-to-date feedback data to described product so that the analysis of user feedback data is more accurate.

Preferably, described method also includes:

The ID of described feedback data submitted in record；

Detect described ID in preset time period, submit the number of times of feedback data to；

When described ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, do not record the feedback data that described ID is submitted to.

In this programme, it is possible to avoid same user that described product carries out malice feedback continually, it is also possible to upgrade in time the user's up-to-date feedback data to described product so that the analysis of user feedback data is more accurate.

Preferably, described method also includes:

Described feedback data is audited, it is judged that whether described feedback data comprises information unauthorized；

When described feedback data comprises information unauthorized, delete described feedback data.

In this programme, it is possible to the feedback data containing junk information or harmful content is filtered, it is to avoid the impact of the malice feedback subsequent analysis of user, improve the degree of accuracy of the analysis to user feedback data.

A kind of data processing equipment, described device includes:

Logging modle, for recording the Times of Feedback of numerical value corresponding to the feedback data to each version of product and feedback data；

Acquisition module, for obtaining the weighted value that each version of product is corresponding；

Final feedback data computing module, for the final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version.

Preferably, described final feedback data computing module, for calculating the final feedback data of counting yield according to below equation,

$S=\frac{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C}_{\mathrm{ij}}{N}_{\mathrm{ij}}{W}_j}{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{N}_{\mathrm{ij}}{W}_j},$

Wherein, S is the final feedback data of described product；C_ijFor the numerical value that the i-th feedback data to described product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to described product jth version；W_jWeighted value for described product jth version；I=1,2,3......n, j=1,2,3......m, n is the number of feedback data, and m is the number of version.

Preferably, described acquisition module includes: weight calculation submodule, for calculating, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\mathrm{\&Pi;}}}(1-t_{j-1}),$

Wherein, W_jFor the weighted value of described product jth version, j=1,2,3...m；M is described product version number；t_jFor the difference in version value between jth version and+1 version of jth, wherein t₀=0。

Preferably, described weight calculation submodule, for calculating described difference in version value by below equation:

Work as z_j<y_jTime, described difference in version value

Work as z_j≥y_jTime, described difference in version value

Described z_jFor the code update increment obtained according to the difference between described product jth version and+1 version of jth；y_jSize of code for described product jth version.

Preferably, described acquisition module also includes:

Whether detection sub-module, for detecting product version corresponding to described feedback data record；

Weight Acquisition submodule, for when the product version that described feedback data is corresponding records, obtaining the weighted value that the described product version recorded is corresponding；

Described weight calculation submodule, when the product version that described feedback data is corresponding does not record, recalculates the weighted value that the product version recorded is corresponding with Unrecorded product version.

Preferably, the weighted value that each version of described product is corresponding is arithmetic progression or Geometric Sequence, or weighted value corresponding to each version of described product obtains according to the table lookup pre-set.

Preferably, described device also includes: first detection module,

Described logging modle, for recording the ID submitting described feedback data to；

Whether described first detection module, had been filed on feedback data to the same version of identical product for detecting described user according to described ID；

Described logging modle, for when the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product being deleted by described user, the feedback data submitted to after being retained in.

Preferably, described device also includes: the second detection module,

Described logging modle, for recording the ID submitting described feedback data to；

Described second detection module, for detecting the number of times of the feedback data that described ID one of them version to the number of times of the feedback data that described product is submitted to or to described product is submitted to；

Described logging modle, for when the number of times of the feedback data that the number of times to the feedback data that described product is submitted to or one of them version to described product are submitted to exceedes default first threshold, not recording the feedback data that described ID is submitted to.

Preferably, described device also includes: the 3rd detection module,

Described logging modle, for recording the ID submitting described feedback data to；

Described 3rd detection module, submits the number of times of feedback data to for detecting described ID in preset time period；

Described logging modle, for when described ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, not recording the feedback data that described ID is submitted to.

Preferably, described device also includes:

Auditing module, for auditing described feedback data, it is judged that whether comprise information unauthorized in described feedback data；

Described logging modle, for when comprising information unauthorized in described feedback data, deleting described feedback data.

A kind of server apparatus, described server apparatus includes memorizer, and one or more than one program, one of them or more than one program are stored in memorizer, and are configured to be performed one or more than one program package containing the instruction for carrying out following operation by one or more than one processor:

Record the Times of Feedback of the numerical value that the feedback data of each version of product is corresponding and feedback data；

Obtain the weighted value that each version of product is corresponding；

The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version.

Other features and advantages of the present invention will be set forth in the following description, and, partly become apparent from description, or understand by implementing the present invention.The purpose of the present invention and other advantages can be realized by structure specifically noted in the description write, claims and accompanying drawing and be obtained.

Below by drawings and Examples, technical scheme is described in further detail.

Accompanying drawing explanation

Accompanying drawing is for providing a further understanding of the present invention, and constitutes a part for description, is used for together with embodiments of the present invention explaining the present invention, is not intended that limitation of the present invention.In the accompanying drawings:

Fig. 1 is the schematic flow sheet of data processing method in the embodiment of the present invention；

Fig. 2 is another schematic flow sheet of data processing method in the embodiment of the present invention；

Fig. 3 is another schematic flow sheet of data processing method in the embodiment of the present invention；

Fig. 4 is the structural representation of data processing equipment in the embodiment of the present invention；

Fig. 5 is the structural representation of acquisition module in the embodiment of the present invention；

Fig. 6 is the structural representation of server apparatus in the embodiment of the present invention.

Detailed description of the invention

Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are illustrated, it will be appreciated that preferred embodiment described herein is merely to illustrate and explains the present invention, is not intended to limit the present invention.

As described in Figure 1, embodiments provide a kind of data processing method, comprise the following steps:

Step 102, numerical value that the feedback data of each version of record product is corresponding and the Times of Feedback of feedback data；

Step 104, obtains the weighted value that each version of product is corresponding；

Step 106, the final feedback data according to the weighted value counting yield of numerical value corresponding to feedback data, the Times of Feedback of feedback data and each version.

In the present embodiment, consider the iterative of some products, by distributing different weighted values to the different spaces of a whole page of product, so, when the final feedback data of counting yield, the feedback data impact for final feedback data of different editions product can be embodied so that the analysis of user feedback data is more accurate.

Preferably, in step 106, include according to the final feedback data of the weighted value counting yield of numerical value corresponding to feedback data, the Times of Feedback of feedback data and each version:

$S=\frac{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C}_{\mathrm{ij}}{N}_{\mathrm{ij}}{W}_j}{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{N}_{\mathrm{ij}}{W}_j}$ Formula (1)

Wherein, S is the final feedback data of product；C_ijFor the numerical value that the i-th feedback data to product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to product jth version；W_jWeighted value for product jth version；I=1,2,3......n, j=1,2,3......m, n is the number of feedback data, and m is the number of version.

Preferably, the weighted value that each version of product is corresponding is arithmetic progression or Geometric Sequence, or weighted value corresponding to each version of product obtains according to the table lookup pre-set.

Such as: the weighted value between version is arithmetic progression, such as 1,0.8,0.6,0.4.Weighted value between version is Geometric Sequence, such as 1,0.8,0.64,0.512.Or, weighted value between version is tabled look-up acquisition according to the form pre-set.Special weight can also be provided, such as difference two-stage between this version with adjacent version according to the renewal of certain emphasis version.Such as, certain product has just done once great renewal, latest edition with its before the weighted value of version be followed successively by: 1,0.6,0.4.

In the present embodiment, by the new and old order according to product version, the weighted value successively decreased is distributed for each version, so, when the final feedback data of counting yield, it is possible to embodying the feedback data impact for final feedback data of new and old edition product, namely final feedback data is had the greatest impact by the feedback data of latest edition product, and more the feedback data of legacy version product is more little on the impact of final feedback data so that the analysis of user feedback data is more accurate.

Preferably, in order to more reasonably show the edition upgrading impact for each version weight, in step 104, according to the weighted value that each version of code update incremental computations between the size of code of each version of product and adjacent version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\mathrm{\&Pi;}}}(1-t_{j-1})$ Formula (2)

Wherein, W_jFor the weighted value of product jth version, j=1,2,3...m；M is product version number；t_jFor the difference in version value between jth version and+1 version of jth, wherein t₀=0.In described formula, according to product version by newly to the weighted value of old correspondence respectively W₁、W₂、W₃...W_m.It should be pointed out that, the number order of weighted value and product version new and old between corresponding relation, be only a kind of artificial setting, have no effect on protection scope of the present invention and limit.

Difference in version value is calculated by below equation:

Work as z_j<y_jTime, difference in version value

$t_j=\frac{z_j}{y_j};$ Formula (3)

Work as z_j≥y_jTime, difference in version value

$t_j=\frac{z_j}{z_j+y_j};$ Formula (4)

z_jFor the code update increment obtained according to the difference between product jth version and+1 version of jth；y_jSize of code for product jth version.

In reality, binary system difference (binarydiff can be adopted, bsdiff) difference (diff) file that the difference between new and old two versions of product is generated by program pin, size of code and the size of code of each version Android installation kit (AndroidPackage, apk) file according to diff file calculate the weighted value that each version is corresponding.Such as: certain product one has 5 versions, product is by being newly followed successively by 1,2,3,4,5 to legacy version number.According to above-mentioned formula (2), calculate the weighted value obtaining each version and be followed successively by:

W₁=1;

$W_2=1-t_1=1-\frac{z_1}{y_1},z_1<y_1;$

$W_3=(1-t_1)(1-t_2)=(1-\frac{z_1}{y_1})(1-\frac{z_2}{y_2}),z_2<y_2;$

$W_4=(1-t_1)(1-t_2)(1-t_3)=-(1-\frac{z_1}{y_1})(1-\frac{z_2}{y_2})(1-\frac{z_3}{y_3}),z_3<y_3;$

$W_5=(1-t_1)(1-t_2)(1-t_3)(1-t_4)=(1-\frac{z_1}{y_1})(1-\frac{z_2}{y_2})(1-\frac{z_3}{y_3})(1-\frac{z_4}{y_4}),z_4<y_4.$

Such as " search dog input method " is upgraded to 1.0 from version 2 .0, the apk file 10M of version 2 .0, the apk file 12M of version 1.0, the diff file 5M generated according to algorithm between these 2 versions, then after edition upgrading, the weighted value of version 1.0 correspondence is 1, and weighted value corresponding for version 2 .0 is 1-5/12=0.58.

In the present embodiment, by the weighted value that each version of code update incremental computations between the size of code according to each version of product and adjacent version is corresponding, the weighted value making each version embodies the difference between adjacent version, the feedback data impact for final feedback data of different editions product can be embodied more exactly so that the analysis of user feedback data is more accurate.

Preferably, in the present embodiment, step 104 includes:

The product version whether record that detection feedback data is corresponding；

When the product version that feedback data is corresponding records, obtain the weighted value that the product version recorded is corresponding；

When the product version that feedback data is corresponding does not record, recalculate the weighted value that the product version recorded is corresponding with Unrecorded product version.

In the present embodiment, by judging that whether feedback data is for redaction product, when feedback data is for redaction product, namely product updates, need to adjust each weighted value of product, the weighted value making latest edition corresponding is maximum, and the weighted value that more legacy version is corresponding is more little so that the analysis of user feedback data is more accurate.When product does not update, then obtain the weighted value that existing version is corresponding, be analyzed user feedback data calculating.

Preferably, the method also includes: also the ID of feedback data submitted in record in a step 102；Before step 104, detect user according to ID and whether the same version of identical product was had been filed on feedback data；When the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product is deleted by user, the feedback data submitted to after being retained in.This way it is possible to avoid product is carried out malice feedback by same user repeatedly, it is also possible to upgrade in time the user's up-to-date feedback data to product so that the analysis of user feedback data is more accurate.

Preferably, the method also includes: also the ID of feedback data submitted in record in a step 102；Before step 104, the number of times of the feedback data that the detection ID number of times to the feedback data that product is submitted to or one of them version to product are submitted to；When the number of times of the feedback data that the number of times to the feedback data that product is submitted to or one of them version to product are submitted to exceedes default first threshold, do not record the feedback data that ID is submitted to.This way it is possible to avoid product is carried out malice feedback by same user repeatedly, it is also possible to upgrade in time the user's up-to-date feedback data to product so that the analysis of user feedback data is more accurate.

Preferably, the method also includes: also the ID of feedback data submitted in record in a step 102；Before step 104, detection ID submits the number of times of feedback data in preset time period；When ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, do not record the feedback data that ID is submitted to.This way it is possible to avoid product is carried out malice feedback by same user continually, it is also possible to upgrade in time the user's up-to-date feedback data to product so that the analysis of user feedback data is more accurate.

Preferably, the method also includes: before step 104, and feedback data is audited, it is judged that whether comprise information unauthorized in feedback data；When feedback data comprises information unauthorized, delete feedback data.As such, it is possible to the feedback data containing junk information or harmful content is filtered, it is to avoid the impact of the malice feedback subsequent analysis of user, improve the degree of accuracy of the analysis to user feedback data.

It is described in detail for user's different editions software marked method that the embodiment of the present invention is provided below.

As in figure 2 it is shown, when there being user to submit new scoring to, the method that the embodiment of the present invention provides comprises the following steps:

Step 202, the score information record this user submitted to is in data base；Wherein score information at least includes: fractional value and for software version information；

Step 204, is updated the scoring number of times of this fractional value when recording score information in data base；

Step 206, detects whether this new version information submitting to scoring targeted is the scoring to software version existing in data base, if it is, perform step 208, if it does not, perform step 210；

Step 208, directly utilizes the final scoring of the data software for calculation of record in data base；

Step 210, after obtaining the weighted value of software version, the final scoring of software for calculation.

In the present embodiment, the calculating of software scoring is more reasonable.The developer of software can see the hobby of user's software for newly reaching the standard grade or submit to faster.When software difference is commented more by user, it is possible to timely from the rapid reacting condition of scoring out, it is simple to developer submits redaction in time to.Poor software of marking in a series of personalized recommendations such as the calculating that software is marked is more accurate, and the recommendation for information such as the seniority among brothers and sisters of software scoring impact, fine work is more reasonable, is ranking, fine work is not recommended.The software of same developer can be contrasted, it is recommended that mark higher software to user to this developer of user.

As it is shown on figure 3, the method that the embodiment of the present invention provides comprises the following steps:

Step 302, the score information record this user submitted to, in data base, wherein at least includes in score information: fractional value, for software version information and ID；

Step 304, is updated the scoring number of times of this fractional value when data-base recording score information；

Step 306, detects this user according to ID and whether the same version of this software was had been filed on feedback data, if it does not, perform step 308, if it is, perform step 310；

Step 308, directly utilizes the final scoring of the data software for calculation of record in data base；

Step 310, deletes the scoring record that this user is original in data base, adds new scoring record；

Step 312, the final scoring according to the data base's software for calculation after updating.

This way it is possible to avoid this software is carried out malice scoring by same user repeatedly, it is also possible to upgrade in time the user's up-to-date evaluation to this software.

In order to embody user's fancy grade to software, adopting and currently a popular comment star mode, 1-5 star represents user's fancy grade to software respectively, and 1 star is least liked, and 5 stars like best.

The same version of same software is marked, adopts the final scoring of weighting scheme software for calculation.When user's first time scoring, corresponding star value adds 1, and scoring sum adds 1.User's second time and later scoring, corresponding star value scoring number of times adds 1, and star value scoring number of times originally subtracts 1.

Such as, appId be 10 software version 1 mark at present the number respectively 1,2,3,2,1 of 1,2,3,4,5 star, then comment the 1*1+2*2+3*3+2*4+1*5=27 that adds up to of star, scoring total number of persons is 1+2+3+2+1=9 people, and software scoring is 27/9=3.When some user comments 3 star first time, commenting the number of 3 stars to add 1, comment the sum of star to add 3, software scoring is 30/10=3 star.This user's second time scoring is 5 points, then 3 original star numbers subtract 1, and the number of 5 stars adds 1.Now, the number commenting 1,2,3,4,5 star is 1 respectively, 2,3,2,2, comment star sum 30-3+5=32, and software scoring is 32/10=3.2.

But after software release upgrade, commenting star number also to change therewith accordingly, appId is the software of 10 is example (scoring 1,2,3,4, the number of 5 stars respectively 1,2,3,2,1), when application upgrade to version 2, comment the value that star number is all multiplied by between a coefficient 0.8(or other 0-1 accordingly), comment 1,2,3, the number of 4,5 stars becomes 0.8,1.6,2.4,1.6,0.8, the total number of persons of scoring becomes 0.8+1.6+2.4+1.6+0.8=7.2, commenting star sum is 27*0.8=21.6, and software scoring is 21.6/7.2=3.Visible, after edition upgrading, if marked but without people, software scoring is to maintain constant.

When there being a user (user never marked before being no matter or the user marked) to be 1 timesharing to the scoring of software, this is to comment 1,2,3, the number of 4,5 stars is 1.8,1.6,2.4,1.6,0.8, scoring total number of persons becomes 8.2, scoring sum becomes 22.6, then software scoring is 22.6/8.2=2.756.A visible user comments (1 star) to directly results in software scoring for the difference of redaction and falls below 2.75 from 3, and also remains the scoring impact for software scoring of early version simultaneously.

When the user of some malice is in order to improve favorable comment number, issuing some advertisements by program or artificial mode, during yellow information, the calculating of software scoring can be influenced by very big impact, it is possible to has following 2 kinds of modes to solve:

1) first passing through examination & verification, rear deletion comments on scoring.

With appId above for 10, the software of (number of scoring 1,2,3,4,5 star respectively 1,2,3,2,1) is for example, and some user has delivered a yellow advertising commentary, and scoring is 5 points, and this news commentary star number becomes 1, and 2,3,2,2, software scoring is 3.2.But this scoring should not be accumulated in software score calculation, when deleting, first detect the latest edition whether version corresponding to this comment scoring record be app, if, show that version is but without upgrading, at this moment can simply by for star number subtract 1, overall score subtracts 1, then software scoring is for (1*1+2*2+3*3+4*2+5*(2-1))/(1+2+3+2+2-1)=3.If not the latest edition of app, then need to detect the promoted how many times of app, it is assumed that upgraded 1 time, software scoring should be just (1*1+2*2+3*3+4*2+5*(2-1*0.8))/(1+2+3+2+2-1*0.8)=3.04.

2) being judged as comment spam either directly through program, the calculating of average mark is not affected by its scoring

By some machine learning with based on the comment spam detection mode of regularity, it is possible to find waste advertisements timely, its state is set to only oneself is visible, the comment scoring that so he sends he mobile phone terminal it can be seen that.

Such as, the recognition method of comment spam is as follows: A) number of times that same user recommends can be limited；B) app for finding out, searches its scoring in Commentary Systems, and scoring is less than 2, it is believed that it is the application of a rubbish, shows in various lists；C) each account (such as a day) within a period of time not can exceed that 10 comments repeated, each No. ime within a period of time (such as one day) not can exceed that 20 identical comments.

Based on same inventive concept, the embodiment of the present invention also provides for a kind of data processing equipment, and as shown in Figure 4, this device includes:

Logging modle 41, for recording the Times of Feedback of numerical value corresponding to the feedback data to each version of product and feedback data；

Acquisition module 42, for obtaining the weighted value that each version of product is corresponding；

Final feedback data computing module 43, for the final feedback data of the weighted value counting yield according to numerical value corresponding to feedback data, the Times of Feedback of feedback data and each version.

Preferably, final feedback data computing module 43 calculates the final feedback data of counting yield according to below equation,

$S=\frac{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C}_{\mathrm{ij}}{N}_{\mathrm{ij}}{W}_j}{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{N}_{\mathrm{ij}}{W}_j}$ Formula (1)

Wherein, S is the final feedback data of this product；C_ijFor the numerical value that the i-th feedback data to this product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to this product jth version；W_jWeighted value for this product jth version；I=1,2,3......n, j=1,2,3......m, n is the number of feedback data, and m is the number of version.

Preferably, as it is shown in figure 5, acquisition module 42 includes: weight calculation submodule 421, for calculating, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\mathrm{\&Pi;}}}(1-t_{j-1})$ Formula (2)

Wherein, W_jFor the weighted value of product jth version, j=1,2,3...m；M is product version number；t_jFor the difference in version value between jth version and+1 version of jth, wherein t₀=0。

Preferably, weight calculation submodule, for by below equation calculated version difference value:

Work as z_j<y_jTime, difference in version value

$t_j=\frac{z_j}{y_j};$ Formula (3)

Work as z_j≥y_jTime, difference in version value

$t_j=\frac{z_j}{z_j+y_j};$ Formula (4)

z_jFor the code update increment obtained according to the difference between product jth version and+1 version of jth；y_jSize of code for product jth version.

Preferably, as it is shown in figure 5, acquisition module 42 also includes:

Whether detection sub-module 422, for detecting product version corresponding to feedback data record；

Weight Acquisition submodule 423, for when the product version that feedback data is corresponding records, obtaining the weighted value that the product version recorded is corresponding；

Weight calculation submodule 421, when the product version that feedback data is corresponding does not record, recalculates the weighted value that the product version recorded is corresponding with Unrecorded product version.

As shown in Figure 4, it is preferable that this device also includes: first detection module 44,

Logging modle 41, for recording the ID submitting feedback data to；

Whether first detection module 44, had been filed on feedback data to the same version of identical product for detecting this user according to ID；

Logging modle 41, for when the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product being deleted by this user, the feedback data submitted to after being retained in.

Preferably, this device also includes: the second detection module 45,

Logging modle 41, for recording the ID submitting feedback data to；

Second detection module 45, for detecting the number of times of the feedback data of the ID number of times to the feedback data that product is submitted to or the submission of one of them version to product；

Logging modle 41, for when the number of times of the feedback data that the number of times to the feedback data that product is submitted to or one of them version to product are submitted to exceedes default first threshold, not recording the feedback data that ID is submitted to..

Preferably, this device also includes: the 3rd detection module 46,

Logging modle 41, for recording the ID submitting feedback data to；

3rd detection module 46, submits the number of times of feedback data to for detecting ID in preset time period；

Logging modle 41, for when ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, not recording the feedback data that ID is submitted to.

Preferably, this device also includes:

Auditing module 47, for auditing feedback data, it is judged that whether comprise information unauthorized in feedback data；

Logging modle 41, for when comprising information unauthorized in feedback data, deleting feedback data.

Fig. 6 is a kind of server device topology schematic diagram that the embodiment of the present invention provides.As shown in Figure 6, this server apparatus may be used for implementing the data processing method of offer in above-described embodiment.Wherein, this server apparatus can be the high-performance computer in network environment or computer system etc., intercepts the service request that other computers (client computer) on network are submitted to, and provides corresponding service.Preferential:

This server apparatus 600 includes but not limited to following structure or kinetic energy.Preferentially, this server apparatus 600 at least include one or more central processing unit (CPU) 610, one or more internal memory 630, for storing one or more media 620(such as one or more mass storages of operating system 621, application program 622 or data).

These one or more internal memories 630 and media 620 could be arranged to interim or non-provisional.The program being stored in one or more media 620 can include one or more module.Each module can include the operational order collection of this server apparatus 600.Closer, CPU610 can be configured to communicate with media 620, perform instruction set and perform the operation on server apparatus 600.

This server apparatus can also include one or more power supply 660, one or more wired or wireless network interface 640, one or more keyboard one or more input and output (I/O) interface 650 and/or one or more operating system 621, such as WindowsServer^TM, MacOSX^TM, Unix^TM, Linux^TM, FreeBSD^TMEtc. operating system.

Specifically in the present embodiment, server apparatus includes memorizer, and one or more than one program, one of them or more than one program are stored in memorizer, and are configured to be performed one or more than one program package containing the instruction for carrying out following operation by one or more than one processor:

Numerical value that the feedback data of each version of record product is corresponding and the Times of Feedback of feedback data；

Obtain the weighted value that each version of product is corresponding；

The final feedback data of the weighted value counting yield according to numerical value corresponding to feedback data, the Times of Feedback of feedback data and each version.

Preferably, the instruction for carrying out following operation is also comprised: include according to the final feedback data of the weighted value counting yield of numerical value corresponding to feedback data, the Times of Feedback of feedback data and each version:

$S=\frac{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{C}_{\mathrm{ij}}{N}_{\mathrm{ij}}{W}_j}{\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{N}_{\mathrm{ij}}{W}_j}$ Formula (1)

Wherein, S is the final feedback data of this product；C_ijFor the numerical value that the i-th feedback data to this product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to this product jth version；W_jWeighted value for this product jth version；I=1,2,3......n, j=1,2,3......m, n is the number of feedback data, and m is the number of version.

Preferably, the instruction for carrying out following operation is also comprised: calculate, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\mathrm{\&Pi;}}}(1-t_{j-1}),$

Wherein, W_jFor the weighted value of product jth version, j=1,2,3 ... m；M is product version number；t_jFor the difference in version value between jth version and+1 version of jth, wherein t₀=0。

Preferably, the instruction for carrying out following operation is also comprised: difference in version value is calculated by below equation:

Work as z_j<y_jTime, difference in version value

$t_j=\frac{z_j}{y_j};$ Formula (3)

Work as z_j≥y_jTime, difference in version value

$t_j=\frac{z_j}{z_j+y_j};$ Formula (4)

z_jFor the code update increment obtained according to the difference between product jth version and+1 version of jth；y_jSize of code for product jth version.

Preferably, the instruction for carrying out following operation is also comprised: the product version whether record that detection feedback data is corresponding；When the product version that feedback data is corresponding records, obtain the weighted value that the product version recorded is corresponding；When the product version that feedback data is corresponding does not record, recalculate the weighted value that the product version recorded is corresponding with Unrecorded product version.

Preferably, the instruction for carrying out following operation is also comprised: the ID of feedback data submitted in record；Detect this user according to ID and whether the same version of identical product is had been filed on feedback data；When the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product is deleted by this user, the feedback data submitted to after being retained in.

Preferably, the instruction for carrying out following operation is also comprised: the ID of feedback data submitted in record；The number of times of the feedback data that the detection ID number of times to the feedback data that product is submitted to or one of them version to product are submitted to；When the number of times of the feedback data that the number of times to the feedback data that product is submitted to or one of them version to product are submitted to exceedes default first threshold, do not record the feedback data that ID is submitted to.

Preferably, the instruction for carrying out following operation is also comprised: the ID of feedback data submitted in record；Detection ID submits the number of times of feedback data in preset time period；When ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, do not record the feedback data that ID is submitted to.

Preferably, the instruction for carrying out following operation is also comprised: feedback data is audited, it is judged that whether feedback data comprises information unauthorized；When feedback data comprises information unauthorized, delete feedback data.

The data processing method of present device embodiment, device and server apparatus, consider the iterative of some products, by distributing different weighted values to the different spaces of a whole page of product, so, when calculating the final feedback data of this product, the feedback data impact for final feedback data of different editions product can be embodied so that the analysis of user feedback data is more accurate.

Those skilled in the art are it should be appreciated that embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, complete software implementation or the embodiment in conjunction with software and hardware aspect.And, the present invention can adopt the form at one or more upper computer programs implemented of computer-usable storage medium (including but not limited to disk memory and optical memory etc.) wherein including computer usable program code.

The present invention is that flow chart and/or block diagram with reference to method according to embodiments of the present invention, equipment (system) and computer program describe.It should be understood that can by the combination of the flow process in each flow process in computer program instructions flowchart and/or block diagram and/or square frame and flow chart and/or block diagram and/or square frame.These computer program instructions can be provided to produce a machine to the processor of general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device so that the instruction performed by the processor of computer or other programmable data processing device is produced for realizing the device of function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.

These computer program instructions may be alternatively stored in and can guide in the computer-readable memory that computer or other programmable data processing device work in a specific way, the instruction making to be stored in this computer-readable memory produces to include the manufacture of command device, and this command device realizes the function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.

These computer program instructions also can be loaded in computer or other programmable data processing device, make on computer or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computer or other programmable devices provides for realizing the step of function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.

Obviously, the present invention can be carried out various change and modification without deviating from the spirit and scope of the present invention by those skilled in the art.So, if these amendments of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.

Claims (15)

1. a data processing method, it is characterised in that described method includes:

Record the Times of Feedback of the numerical value that the feedback data of each version of product is corresponding and feedback data；

Obtain the weighted value that each version of product is corresponding；

The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version；

The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version, including: the final feedback data according to below equation counting yield,

$S=\frac{\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{n}{\&Sigma;}}{C}_{ij}{N}_{ij}{W}_j}{\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{n}{\&Sigma;}}{N}_{ij}{W}_j},$

Wherein, S is the final feedback data of this product；C_ijFor the numerical value that the i-th feedback data to described product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to described product jth version；W_jWeighted value for described product jth version；I=1,2,3 ... n, j=1,2,3 ... m, n are the number of numerical value corresponding to feedback data, and m is the version number of described product；

The weighted value obtaining each version of product corresponding includes: calculate, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\&Pi;}}(1-t_{j-1}),$

Wherein, W_jFor the weighted value of described product jth version, j=1,2,3 ... m；M is described product version number；t_jFor the difference in version value between jth version and+1 version of jth, t_j-1For the difference in version value between-1 version of jth and jth version, wherein t₀=0；

Described difference in version value is calculated by below equation:

Work as z_j<y_jTime, described difference in version value

Work as z_j≥y_jTime, described difference in version value

Described z_jFor the code update increment obtained according to the difference between described product jth version and+1 version of jth；y_jSize of code for described product jth version.

2. the method for claim 1, it is characterised in that the weighted value that each version of described product is corresponding is arithmetic progression or Geometric Sequence, or weighted value corresponding to each version of described product obtain according to the table lookup pre-set.

3. the method for claim 1, it is characterised in that the weighted value obtaining each version of product corresponding includes:

Detect product version corresponding to described feedback data whether record；

When the product version that described feedback data is corresponding records, obtain the weighted value that the described product version recorded is corresponding；

When the product version that described feedback data is corresponding does not record, recalculate the weighted value that the product version recorded is corresponding with Unrecorded product version.

4. the method for claim 1, it is characterised in that described method also includes:

The ID of described feedback data submitted in record；

Detect described user according to described ID and whether the same version of identical product is had been filed on feedback data；

When the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product is deleted by described user, the feedback data submitted to after being retained in.

5. the method for claim 1, it is characterised in that described method also includes:

The ID of described feedback data submitted in record；

Detect the number of times of the feedback data that described ID one of them version to the number of times of the feedback data that described product is submitted to or to described product is submitted to；

When the number of times of the feedback data that the number of times to the feedback data that described product is submitted to or one of them version to described product are submitted to exceedes default first threshold, do not record the feedback data that described ID is submitted to.

6. the method for claim 1, it is characterised in that described method also includes:

The ID of described feedback data submitted in record；

Detect described ID in preset time period, submit the number of times of feedback data to；

When described ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, do not record the feedback data that described ID is submitted to.

7. the method for claim 1, it is characterised in that described method also includes:

Described feedback data is audited, it is judged that whether described feedback data comprises information unauthorized；

When described feedback data comprises information unauthorized, delete described feedback data.

8. a data processing equipment, it is characterised in that described device includes:

Logging modle, for recording the Times of Feedback of numerical value corresponding to the feedback data to each version of product and feedback data；

Acquisition module, for obtaining the weighted value that each version of product is corresponding；

Final feedback data computing module, for the final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version；

Described final feedback data computing module, for calculating the final feedback data of counting yield according to below equation,

$S=\frac{\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{n}{\&Sigma;}}{C}_{ij}{N}_{ij}{W}_j}{\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{n}{\&Sigma;}}{N}_{ij}{W}_j},$

Wherein, S is the final feedback data of described product；C_ijFor the numerical value that the i-th feedback data to described product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to described product jth version；W_jWeighted value for described product jth version；I=1,2,3 ... n, j=1,2,3 ... m, n are the number of feedback data, m is the number of version；

Described acquisition module includes: weight calculation submodule, for calculating, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\&Pi;}}(1-t_{j-1}),$

Wherein, W_jFor the weighted value of described product jth version, j=1,2,3 ... m；M is described product version number；t_jFor the difference in version value between jth version and+1 version of jth, t_j-1For the difference in version value between-1 version of jth and jth version, wherein t₀=0；

Described weight calculation submodule, for calculating described difference in version value by below equation:

Work as z_j<y_jTime, described difference in version value

Work as z_j≥y_jTime, described difference in version value

Described z_jFor the code update increment obtained according to the difference between described product jth version and+1 version of jth；y_jSize of code for described product jth version.

9. device as claimed in claim 8, it is characterised in that the weighted value that each version of described product is corresponding be arithmetic progression or Geometric Sequence, or weighted value corresponding to each version of described product is according to the table lookup acquisition pre-set.

10. device as claimed in claim 8, it is characterised in that described acquisition module also includes:

Whether detection sub-module, for detecting product version corresponding to described feedback data record；

Weight Acquisition submodule, for when the product version that described feedback data is corresponding records, obtaining the weighted value that the described product version recorded is corresponding；

Described weight calculation submodule, when the product version that described feedback data is corresponding does not record, recalculates the weighted value that the product version recorded is corresponding with Unrecorded product version.

11. device as claimed in claim 8, it is characterised in that described device also includes: first detection module,

Described logging modle, for recording the ID submitting described feedback data to；

Whether described first detection module, had been filed on feedback data to the same version of identical product for detecting described user according to described ID；

Described logging modle, for when the feedback data having been filed on, the feedback data formerly submitted to of the same version of identical product being deleted by described user, the feedback data submitted to after being retained in.

12. device as claimed in claim 8, it is characterised in that described device also includes: the second detection module,

Described logging modle, for recording the ID submitting described feedback data to；

Described second detection module, for detecting the number of times of the feedback data that described ID one of them version to the number of times of the feedback data that described product is submitted to or to described product is submitted to；Described logging modle, for when the number of times of the feedback data that the number of times to the feedback data that described product is submitted to or one of them version to described product are submitted to exceedes default first threshold, not recording the feedback data that described ID is submitted to.

13. device as claimed in claim 8, it is characterised in that described device also includes: the 3rd detection module,

Described logging modle, for recording the ID submitting described feedback data to；

Described 3rd detection module, submits the number of times of feedback data to for detecting described ID in preset time period；

Described logging modle, for when described ID submits to the number of times of feedback data to exceed default Second Threshold in preset time period, not recording the feedback data that described ID is submitted to.

14. device as claimed in claim 8, it is characterised in that described device also includes:

Auditing module, for auditing described feedback data, it is judged that whether comprise information unauthorized in described feedback data；

Described logging modle, for when comprising information unauthorized in described feedback data, deleting described feedback data.

15. a server apparatus, it is characterized in that, described server apparatus includes memorizer, and one or more than one program, one of them or more than one program are stored in memorizer, and are configured to be performed one or more than one program package containing the instruction for carrying out following operation by one or more than one processor:

Record the Times of Feedback of the numerical value that the feedback data of each version of product is corresponding and feedback data；

Obtain the weighted value that each version of product is corresponding；

The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version；

The final feedback data of the weighted value counting yield according to numerical value corresponding to described feedback data, the Times of Feedback of feedback data and each version, including: the final feedback data according to below equation counting yield,

$S=\frac{\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{n}{\&Sigma;}}{C}_{ij}{N}_{ij}{W}_j}{\underset{j=1}{\overset{m}{\&Sigma;}}\underset{i=1}{\overset{n}{\&Sigma;}}{N}_{ij}{W}_j},$

Wherein, S is the final feedback data of this product；C_ijFor the numerical value that the i-th feedback data to described product jth version is corresponding；N_ijTimes of Feedback for the i-th feedback data to described product jth version；W_jWeighted value for described product jth version；I=1,2,3 ... n, j=1,2,3 ... m, n are the number of numerical value corresponding to feedback data, and m is the version number of described product；

The weighted value obtaining each version of product corresponding includes: calculate, by below equation, the weighted value that each version is corresponding:

$W_j=\underset{j=1}{\overset{j}{\&Pi;}}(1-t_{j-1}),$

Wherein, W_jFor the weighted value of described product jth version, j=1,2,3 ... m；M is described product version number；t_jFor the difference in version value between jth version and+1 version of jth, wherein t₀=0；

Described difference in version value is calculated by below equation:

Work as z_j<y_jTime, described difference in version value

Work as z_j≥y_jTime, described difference in version value

Described z_jFor the code update increment obtained according to the difference between described product jth version and+1 version of jth；y_jSize of code for described product jth version.