CN106294628A - Recommended determines method and device - Google Patents
Recommended determines method and device Download PDFInfo
- Publication number
- CN106294628A CN106294628A CN201610624763.2A CN201610624763A CN106294628A CN 106294628 A CN106294628 A CN 106294628A CN 201610624763 A CN201610624763 A CN 201610624763A CN 106294628 A CN106294628 A CN 106294628A
- Authority
- CN
- China
- Prior art keywords
- feature set
- fisrt feature
- similarity
- feature
- type
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0277—Online advertisement
Abstract
The embodiment of the present invention provides a kind of recommended to determine method and device, the method includes: obtaining the weighted value of each fisrt feature set that at least one fisrt feature set of existing object, at least one second feature set of object to be selected and user determine, object to be selected is any one object in library of object to be selected；Determine the similarity of each fisrt feature set and corresponding second feature set respectively；Weighted value according to each fisrt feature set and the similarity of the corresponding second feature set of each fisrt feature set, determine the similarity of existing object and object to be selected；According to existing object and the similarity of each object to be selected in library of object to be selected, library of object to be selected determines the recommended that existing object is corresponding.For improving the accuracy determining recommended.
Description
Technical field
The present embodiments relate to field of computer technology, particularly relate to a kind of recommended and determine method and device.
Background technology
Along with the development of Internet technology, during user's browse network page (webpage), in order to improve user's body
Test, in webpage, can recommend currently to browse to user the recommended that object is relevant.
In the prior art, during user browses webpage, determine and currently browse the existing object that webpage includes,
Obtain the characteristic information of preset kind of existing object, and according to the characteristic information of the preset kind of existing object in library of object
Coupling obtains recommended, and shows recommended in a network.Such as, when browsing the detail web page of automobile A, if presetting class
Type is price, then obtain the price of automobile A, and the automobile similar to the price of automobile A is defined as recommended；If presetting class
Type is brand, then obtain the brand of automobile A, and the automobile identical with the brand of automobile A is defined as recommended.
But, in the prior art, determine that the recommended obtained is more fixing single according to the characteristic information of preset kind
One, and determine that the recommended obtained is difficult to mate with the actual demand of user, cause the poor accuracy determining recommended.
Summary of the invention
The embodiment of the present invention provides a kind of recommended to determine method and device, determines the accurate of recommended for improving
Property.
First aspect, the embodiment of the present invention provides a kind of recommended to determine method, including:
Obtain at least one fisrt feature set of existing object, at least one second feature set of object to be selected, with
And the weighted value of each described fisrt feature set that user determines, described object to be selected is that any one in library of object to be selected is right
As；
Determine the similarity of each described fisrt feature set and corresponding second feature set respectively；
Weighted value according to each described fisrt feature set and corresponding second special of each described fisrt feature set
The similarity that collection is closed, determines the similarity of described existing object and described object to be selected；
According to described existing object and the similarity of each described object to be selected in described library of object to be selected, described to be selected right
The recommended corresponding as determining described existing object in storehouse.
Second aspect, the embodiment of the present invention provides a kind of recommended to determine device, including:
First acquisition module, for obtain at least one fisrt feature set of existing object, at least the one of object to be selected
The weighted value of each described fisrt feature set that individual second feature set and user determine, described object to be selected is to be selected right
As any one object in storehouse；
First determines module, for determining the similar of each described fisrt feature set and corresponding second feature set respectively
Degree；
Second determines module, for the weighted value according to each described fisrt feature set and each described fisrt feature collection
Close the similarity of corresponding second feature set, determine the similarity of described existing object and described object to be selected；
3rd determines module, for according to described existing object with described library of object to be selected the phase of each described object to be selected
Like degree, determine, in described library of object to be selected, the recommended that described existing object is corresponding.
The recommended that the embodiment of the present invention provides determines method and device, is determining that device needs to obtain existing object pair
During the recommended answered, determine device obtain at least one fisrt feature set of existing object, object to be selected at least one
The weighted value of each fisrt feature set that second feature set and user determine, object to be selected is appointing in library of object to be selected
Anticipate an object, determine the similarity of each fisrt feature set and corresponding second feature set respectively, according to each fisrt feature
The weighted value of set and the similarity of the corresponding second feature set of each fisrt feature set, determine existing object with
The similarity of object to be selected, and according to existing object and the similarity of each object to be selected in library of object to be selected, in library of object to be selected
The middle recommended determining that existing object is corresponding.In above process, user can determine each the first spy according to actual needs
The weighted value that collection is closed, so that determining that device can determine corresponding the pushing away of existing object according to the weighted value of fisrt feature set
Recommend object so that determine that the actual demand of recommended and the user obtained is relevant, and then raising determines the accurate of recommended
Property.
Accompanying drawing explanation
In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
In having technology to describe, the required accompanying drawing used is briefly described, it should be apparent that, the accompanying drawing in describing below is this
Some bright embodiments, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to
Other accompanying drawing is obtained according to these accompanying drawings.
Fig. 1 determines the application scenarios schematic diagram of method for the recommended that the present invention provides；
Fig. 2 determines the schematic flow sheet of method for the recommended that the present invention provides；
The schematic flow sheet one of the similarity based method of the determination characteristic set that Fig. 3 provides for the present invention；
The schematic flow sheet two of the similarity based method of the determination characteristic set that Fig. 4 provides for the present invention；
The schematic flow sheet of the determination characteristic similarity method that Fig. 5 provides for the present invention；
Fig. 6 determines the structural representation one of device for the recommended that the present invention provides；
Fig. 7 determines the structural representation two of device for the recommended that the present invention provides.
Detailed description of the invention
For making the purpose of the embodiment of the present invention, technical scheme and advantage clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
The a part of embodiment of the present invention rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under not making creative work premise, broadly falls into the scope of protection of the invention.
Fig. 1 determines the application scenarios schematic diagram of method for the recommended that the present invention provides, and refer to Fig. 1, including current
The page 101 and library of object to be selected 102.Wherein, the page that current page 101 is browsed by terminal unit for user, this terminal sets
For including but are not limited to the equipment such as computer, mobile phone, this current page 101 can be webpage.The page 101 includes region
M, region M for showing the detail information of existing object in current page, region Zhong Bao in addition to the M of region in the page 101
Including multiple object and show position, each object shows that position can show a recommended.In the technical scheme shown in the application
In, user can arrange the weighted value of multiple features of existing object, so that terminal unit can multiple according to existing object
The weighted value of feature, obtains the recommended relevant to existing object in library of object 102 to be selected, and opens up in object shows position
Show each recommended, and then make the recommended acquired more accurate.Below, by specific embodiment to the application
Shown technical scheme is described in detail.
It should be noted that these specific embodiments can be combined with each other, for same or analogous concept below
Or process may repeat no more in certain embodiments.
Fig. 2 determines the schematic flow sheet of method for the recommended that the present invention provides, and refers to Fig. 2, and the method can be wrapped
Include:
S201, acquisition at least one fisrt feature set of existing object, at least one second feature collection of object to be selected
The weighted value of each fisrt feature set that conjunction and user determine, object to be selected is any one object in library of object to be selected；
S202, determine the similarity of each fisrt feature set and corresponding second feature set respectively；
S203, according to the corresponding second feature of the weighted value of each fisrt feature set and each fisrt feature set
The similarity of set, determines the similarity of existing object and object to be selected；
S204, according to the similarity of each object to be selected in existing object and library of object to be selected, determine in library of object to be selected
The recommended that existing object is corresponding.
The executive agent of the present invention can be that recommended determines device (hereinafter referred determines device), and this determines that device can
To be realized by software and/or hardware.Optionally, this determines that device can be arranged on the number providing data, services to terminal unit
According in server.
In actual application, in the process that the detail information of existing object is browsed in current page by user
In, determining that device obtains at least one fisrt feature set of existing object, each fisrt feature set includes at least one
Individual feature.Such as, this existing object can be automobile, mobile phone etc..Optionally, this at least one fisrt feature set can be true
Determine what device acquired according to the page info of current page, it is also possible to for user's registration when issuing this current page.
It is, of course, also possible to obtain at least one fisrt feature set of existing object otherwise, this is not made specifically by the present invention
Limit.
Determine that device also obtains the weighted value of each fisrt feature set that user determines.Optionally, can be at terminal unit
The type of all fisrt feature set that middle displaying existing object is corresponding, so that the type of fisrt feature set can be entered by user
Row chooses operation, and chooses operation according to user to what the type of fisrt feature set inputted, determines each fisrt feature set
Weighted value.Such as, existing object correspondence fisrt feature set 1-fisrt feature set 10, and show in terminal unit this first
Characteristic set 1-fisrt feature set 10, it is assumed that fisrt feature set 1 and fisrt feature set 3 have been carried out choosing behaviour by user
Make, it is determined that the weighted value of fisrt feature set 1 and fisrt feature set 3 can be respectively set to 0.5 by device, by other the
The weighted value of one characteristic set is set to 0, certainly, determine device can also according to other feasible implementations determine each
The weighted value of one characteristic set, the present invention is to being not especially limited.
Determine that device also obtains at least one second feature set of each object to be selected in library of object to be selected, due to be selected right
As being the object of same type with existing object, therefore, the type of fisrt feature set and second feature set is same or similar,
For example, it is assumed that existing object is automobile A, the fisrt feature collection of automobile A is combined into basic parameter, electromotor etc., and object the most to be selected is also
For automobile, the second feature set of object to be selected can also be basic parameter, electromotor etc..
Each to be selected right determining that device acquires at least one fisrt feature set of existing object, library of object to be selected
After the weighted value of each fisrt feature set that at least one second feature set of elephant and user determine, for be selected right
As the object to be selected of each in storehouse, determine that device all determines the similar of each fisrt feature set and corresponding second feature set
Degree, wherein, the type of the second feature set that this fisrt feature set is corresponding is identical with the type of fisrt feature set.Such as,
Assume that existing object is automobile 1, corresponding three the fisrt feature set of automobile 1, respectively basic parameter, engine parameter, electronic
Machine parameter；Assume that library of object to be selected includes automobile 2-automobile 100, corresponding three the second feature set of automobile 2-automobile 100, point
Wei basic parameter, engine parameter, motor parameter, it is determined that device obtains basic parameter and the automobile 2-of automobile 1 respectively
The similarity of the basic parameter of automobile 100, obtains the engine parameter of automobile 1 and the engine parameter of automobile 2-automobile 100
Similarity, obtains the similarity of the motor parameter of automobile 1 and the motor parameter of automobile 2-automobile 100.
Determining that device acquires each fisrt feature set with after the similarity of corresponding second feature set, determine
Device is similar according to the corresponding second feature set of the weighted value of each fisrt feature set and each fisrt feature set
Degree, determines the similarity of existing object and object to be selected.Optionally, determine that device can determine according to equation below one the most right
Similarity d as with object to be selected:
Wherein, K is the number of fisrt feature set, αiFor the weighted value of i-th fisrt feature set, diFor i-th first
The similarity of the second feature set that characteristic set is corresponding with i-th fisrt feature set.
After determining that device acquires the existing object similarity with each object to be selected, determine that device is according to current
Object and the similarity of each object to be selected, determine the recommended that existing object is corresponding.Optionally, determine that device obtains current
The recommended that the page includes shows number N of position, determines N number of according to similarity order from high to low in recommended
Recommended, and in recommended shows position, recommended is shown.
Below, by concrete example, the method shown in Fig. 2 embodiment is described in detail.
Exemplary, it is assumed that existing object is automobile 1, and library of object to be selected includes automobile 2-automobile 100.Assume again that automobile
1 corresponding three fisrt feature set, respectively basic parameter, engine parameter, motor parameter；Assume library of object to be selected is wrapped
Include automobile 2-automobile 100, corresponding three the second feature set of automobile 2-automobile 100, respectively basic parameter, engine parameter,
Motor parameter.
Assume again that the weighted value of each fisrt feature set that user determines is as shown in table 1:
Table 1
| Fisrt feature set | Weighted value |
| Basic parameter | 0.1 |
| Engine parameter | 0.8 |
| Motor parameter | 0.1 |
Determining that device acquires 3 second feature collection of 3 fisrt feature set of automobile 1, automobile 2-automobile 100
After the weighted value of the fisrt feature set described in conjunction and table 1, determine that device determines that each fisrt feature set is with corresponding respectively
The similarity of second feature set, concrete, as shown in table 2:
Table 2
The automobile 1 shown in table 2 and each individual features set similar in automobile 2-automobile 100 is obtained determining that device determines
After degree, determine device according to the similarity shown in the weighted value of each fisrt feature set of the automobile 1 shown in table 1 and table 2,
Determine the similarity of existing object and each object to be selected, concrete: automobile 1 with the similarity of automobile 2 is: 0.1*0.3+0.8*
0.2+0.1*0.6=0.25；Automobile 1 with the similarity of automobile 3 is: 0.1*0.5+0.8*0.8+0.1*1=0.79；Automobile 1 with
The similarity of automobile 4 is: 0.1*0.6+0.8*0.5+0.1*0.3=0.49 etc., automobile 1 and the similarity of automobile 2-automobile 100
As shown in table 3:
Table 3
| Object to be selected | Similarity |
| Automobile 1-automobile 2 | 0.25 |
| Automobile 1-automobile 3 | 0.79 |
| Automobile 1-automobile 4 | 0.49 |
| …… | …… |
After determining that device determines the attributes similarity obtaining the existing object shown in table 3 and each object to be selected, determine
Device, according to the similarity of existing object Yu each object to be selected, determines recommended in automobile 2-automobile 100.
The recommended that the embodiment of the present invention provides determines method, is determining that device needs to obtain corresponding the pushing away of existing object
When recommending object, determine that device obtains at least one second spy of at least one fisrt feature set of existing object, object to be selected
The weighted value of each fisrt feature set that collection is closed and user determines, object to be selected is any one in library of object to be selected
Object, determines the similarity of each fisrt feature set and corresponding second feature set respectively, according to each fisrt feature set
Weighted value and the similarity of the corresponding second feature set of each fisrt feature set, determine that existing object is right with to be selected
The similarity of elephant, and according to existing object and the similarity of each object to be selected in library of object to be selected, determine in library of object to be selected
The recommended that existing object is corresponding.In above process, user can determine each fisrt feature set according to actual needs
Weighted value so that determining according to the weighted value of fisrt feature set, device can determine that the recommendation that existing object is corresponding is right
As so that determine that the actual demand of recommended and the user obtained is relevant, and then improve the accuracy determining recommended.
On the basis of embodiment illustrated in fig. 2, for any one fisrt feature at least one fisrt feature set
Set, optionally, can determine each fisrt feature set and corresponding second feature by the implementation that the following two kinds is feasible
The similarity (S202 in embodiment illustrated in fig. 2) of set, concrete, refer in the embodiment shown in Fig. 3-Fig. 4.
The schematic flow sheet one of the similarity based method of the determination characteristic set that Fig. 3 provides for the present invention, refers to Fig. 3, should
Method may include that
S301, type according to fisrt feature set, determine and fisrt feature collection at least one second feature set
The identical same type second feature set of type closed；
S302, each feature obtained in fisrt feature set and characteristic of correspondence similar in same type second feature set
Degree；
S303, according to each feature in fisrt feature set and characteristic of correspondence similar in same type second feature set
Degree, determines fisrt feature set and the similarity with type second feature set.
In the embodiment shown in fig. 3, existing object at least one fisrt feature set corresponding, each fisrt feature collection
The type closed is different.Wherein, the method class of each fisrt feature set and the similarity of corresponding second feature set is obtained
Seemingly, below, as a example by any one characteristic set at least one characteristic set, to obtaining each fisrt feature set with corresponding
The method of similarity of second feature set be described in detail.
When determining that device needs to obtain fisrt feature set with the similarity of corresponding second feature set, obtain first
The type of characteristic set, and according to the type of fisrt feature set, at least one second feature set of object to be selected really
The fixed same type second feature set the most identical with the type of fisrt feature set, wherein, this is combined into the with type second feature collection
The second feature set that one characteristic set is corresponding.Such as, when fisrt feature collection is combined into engine parameter, then fisrt feature set
Corresponding same type second feature set is also engine parameter.
After determining that device determines and obtains the same type second feature set that fisrt feature set is corresponding, determine that device obtains
Take in fisrt feature set each feature with the similarity of characteristic of correspondence in type second feature set, wherein, fisrt feature
Feature in set is identical with the type of characteristic of correspondence in type second feature set.For example, it is assumed that fisrt feature set
Include respectively with the feature in second feature set: volume of cylinder, intake method, number of cylinders, then the vapour in fisrt feature set
Cylinder volume is corresponding with the cylinder volume in second feature set, in the intake method in fisrt feature set and second feature set
Intake method corresponding, the number of cylinders in fisrt feature set is corresponding with the number of cylinders in second feature set, it is determined that device
Need volume of cylinder and the similarity of volume of cylinder in second feature set, the fisrt feature collection obtaining in fisrt feature set
Number of cylinders in the similarity of the intake method in the intake method in conjunction and second feature set and fisrt feature set with
The similarity of the number of cylinders in second feature set.
Determining that device determines each feature obtaining in fisrt feature set and with each spy in type second feature set
After the similarity levied, determine that device is according to each feature in fisrt feature set and with each spy in type second feature set
The similarity levied, determines fisrt feature set and the similarity with type second feature set.
In above process, determine that device is right with same type second feature set according to each feature in characteristic set
The similarity of the feature answered, determine fisrt feature set with the similarity of type second feature set so that determine and obtain
Fisrt feature set is more accurate with the similarity of same type second feature set.
The schematic flow sheet two of the similarity based method of the determination characteristic set that Fig. 4 provides for the present invention, refers to Fig. 4, should
Method may include that
S401, type according to fisrt feature set, determine and fisrt feature collection at least one second feature set
The identical same type second feature set of type closed；
S402, the first essential feature obtaining fisrt feature set and the second essential feature of second feature set, first
Essential feature is identical with the type of the second essential feature；
S403, judge that the first essential feature is the most identical with the second essential feature；
The most then perform S404-S405；
If it is not, then perform S406；
S404, each feature obtained in fisrt feature set are similar to each feature in same type second feature set
Degree；
S405, similar to each feature in same type second feature set according to each feature in fisrt feature set
Degree, determines fisrt feature set and the similarity with type second feature set；
S406, determine fisrt feature set and be zero with the similarity of type second feature set.
It should be noted that S401 with S301 is identical, the most no longer repeat.
After determining that device determines and obtains the same type second feature set that fisrt feature set is corresponding, determine that device obtains
Take the first essential feature in fisrt feature set and the second essential feature in second feature set, this first essential feature and
The type of the second essential feature is identical.Optionally, can be by user or determine that device pre-sets in fisrt feature set
The type of the first essential feature, it is assumed for example that all include following feature in fisrt feature set and second feature set: generate factory
Business, production status and manufacturers instructions valency, then can be defined as the first essential feature of fisrt feature set, accordingly by production firm
, production firm is also the second essential feature of second feature set.
After determining that device determines and obtains the first essential feature and the second essential feature, determine that device judges that first is necessary
Feature is the most identical with the second essential feature: the most then by above-mentioned S404-S405 determine fisrt feature set with type the
The similarity of two characteristic sets.It should be noted that S404-S405 with S302-S303 is identical, the most no longer repeat.If
No, it is determined that fisrt feature set be zero with the similarity of type second feature set.
Exemplary, it is assumed that fisrt feature set and second feature set all include following feature: generate manufacturer, production
State and manufacturers instructions valency, the first essential feature and the second essential feature are production firm.
When determining the similarity that device determines fisrt feature set and second feature set, first judge fisrt feature set
In the first essential feature (production firm) the most identical with the second essential feature (production firm) in second feature set: if
It is then to obtain the production firm in fisrt feature set and the similarity of production firm in second feature set, fisrt feature
The similarity of production status in production status in set and second feature set, the manufacturers instructions valency in fisrt feature set
With the similarity of the manufacturers instructions valency in second feature set, and determine that first is special according to above-mentioned three similarities acquired
Collection is closed and the similarity of second feature set.If not, it is determined that the similarity of fisrt feature set and second feature set is
Zero.
In above process, by arranging essential feature in fisrt feature set, this essential feature can be user's root
According to being actually needed setting so that determine that device preferentially can determine fisrt feature set and with type second according to essential feature
The similarity of characteristic set, and then improve the motility of the similarity determining fisrt feature set and second feature set.
On the basis of Fig. 3-embodiment illustrated in fig. 4, optionally, first can be determined by the most feasible implementation
Each feature in characteristic set with the similarity of characteristic of correspondence in type second feature set (in embodiment illustrated in fig. 3
S404 in S302 and embodiment illustrated in fig. 4).Due to obtain in fisrt feature set each feature with type second feature collection
The method of the similarity of second feature corresponding in conjunction is similar to, below, to obtain the fisrt feature in fisrt feature set with same
Illustrate as a example by the similarity of second feature corresponding in type second feature set, concrete, shown in Figure 5 reality
Execute example.
The schematic flow sheet of the determination characteristic similarity method that Fig. 5 provides for the present invention, refers to Fig. 5, and the method is permissible
Including:
S501, determine according to the type of fisrt feature, device can determine that in same type second feature set first is special
Levying the second feature of correspondence, fisrt feature is identical with the type of second feature；
S502, judge whether fisrt feature is numeric type:
The most then perform S503-S506；
If it is not, then perform S507-S509；
S503, acquisition fisrt feature and the ratio of second feature；
S504, judge that ratio is whether more than 1；
The most then perform S505；
If it is not, then perform S506；
S505, the inverse of ratio is defined as the similarity of fisrt feature and second feature；
S506, ratio is defined as the similarity of fisrt feature and second feature；
S507, judge that fisrt feature is the most identical with second feature；
The most then perform S508；
If it is not, then perform S509；
S508, the similarity determining fisrt feature and second feature are 1；
S509, the similarity determining fisrt feature and second feature are 0.
In implementing shown in Fig. 5, determine that device can be first according to the type of fisrt feature, in same type second feature set
The middle second feature determining that fisrt feature is corresponding, this fisrt feature is identical with the type of second feature.Determine that device judges first
Whether feature is numeric type, owing to fisrt feature is identical with the type of second feature, therefore, when fisrt feature is numeric type
Time, second feature is also numeric type.
When fisrt feature is numeric type, second feature is also numeric type, determines acquisition fisrt feature and the second spy
The ratio levied, and judge that the inverse of this ratio whether more than 1, is the most then defined as fisrt feature and second feature by this ratio
Similarity, if it is not, then this ratio to be defined as the similarity of fisrt feature and second feature.For example, it is assumed that fisrt feature and
Second feature all represents number of cylinders, it is further assumed that fisrt feature is 6, and second feature is 4, then obtain fisrt feature and second feature
Ratio 6/4, owing to 6/4 more than 1, is then defined as fisrt feature by the 4/6=2/3 reciprocal of this ratio similar with second feature
Degree.
When fisrt feature is not data type, second feature is not numeric type, determines that device judges fisrt feature
The most identical with second feature, if, it is determined that the similarity of fisrt feature and second feature is 1, if not, it is determined that first is special
The similarity of second feature of seeking peace is 0.For example, it is assumed that fisrt feature and second feature all represent intake method, it is further assumed that first
Being characterized as natural aspiration, second feature is turbocharging, determines that device judges that fisrt feature and second feature differ, it is determined that
The similarity of fisrt feature and second feature is 0.
On the basis of embodiment illustrated in fig. 5, accordingly, determine that device can be by the most feasible implementation, root
According to each feature in fisrt feature set and the similarity with each feature in type second feature set, determine fisrt feature collection
Close with the similarity with type second feature set (in S303 in embodiment illustrated in fig. 3 and embodiment illustrated in fig. 4
S405), concrete:
Determine that device obtains each feature in fisrt feature set similar to each feature in same type second feature set
Degree sum, obtains fisrt feature set and with the non-repeating features number in type second feature set, and according to similarity it
With non-repeating features number, determine fisrt feature set and the similarity with type second feature set.Optionally, can be by phase
It is defined as fisrt feature set and the similarity with type second feature set like spending the sum ratio with non-repeating features number.
Exemplary, it is assumed that the feature that fisrt feature set and second feature set include is as shown in table 4:
Table 4
When determining that device obtains the similarity needing to obtain fisrt feature set and second feature set, first obtain first
Each feature and the similarity of characteristic of correspondence in second feature set in characteristic set, the most as shown in table 5:
Table 5
| Volume of cylinder | 3/4=0.75 |
| Intake method | 0 |
| Number of cylinders | 4/6=0.67 |
Determine that device, according to the similarity shown in table 5, determines that the similarity of fisrt feature set and second feature set is:
On the basis of any one embodiment above-mentioned, determining that device determines that in library of object to be selected existing object is corresponding
Recommended after, determine that each object that device also obtains in the current page at existing object place shows the priority of position,
Show the priority of position according to each recommended and the similarity of existing object and each object, determine recommended and object
Show the corresponding relation of position, and recommended and corresponding relation are pushed to current page, so that at the object of current page
Show and show each recommended according to corresponding relation on position.
Exemplary, it is assumed that current page includes that 3 objects show position, is designated as object respectively and shows that position 1-object is shown
Position 3, and these 3 objects show that the priority of positions is successively decreased successively, it is further assumed that determine that device acquires 3 recommendeds, respectively
It is designated as recommended 1-recommended 3, and the similarity of these 3 recommendeds and existing object is successively decreased successively, then pusher
Determine that these 3 objects show that the corresponding relation of position and these 3 recommendeds is as shown in table 6:
Table 6
| Object shows position | Recommended |
| Object shows position 1 | Recommended 1 |
| Object shows position 2 | Recommended 2 |
| Object shows position 3 | Recommended 3 |
After determining that device acquires the corresponding relation shown in recommended 1-recommended 3 and table 6, determine
Device pushes the corresponding relation shown in recommended 1-recommended 3 and table 6 to current page so that show position 1 at object
Upper display recommended 1, shows at object and shows recommended 2 on position 2, shows at object and shows recommended 3 on position 3.
Fig. 6 determines the structural representation one of device for the recommended that the present invention provides, and refers to Fig. 6, and this device is permissible
Including:
First acquisition module 601, for obtaining at least one fisrt feature set of existing object, object to be selected at least
The weighted value of each fisrt feature set that one second feature set and user determine, object to be selected is in library of object to be selected
Any one object；
First determines module 602, for determining the similar of each fisrt feature set and corresponding second feature set respectively
Degree；
Second determines module 603, for according to the weighted value of each fisrt feature set and each fisrt feature set and its
The similarity of corresponding second feature set, determines the similarity of existing object and object to be selected；
3rd determines module 604, for according to existing object and the similarity of each object to be selected in library of object to be selected, treating
Select the recommended determining in library of object that existing object is corresponding.
Recommended shown in the embodiment of the present invention determines that device can perform the technical side shown in said method embodiment
Case, it realizes principle and beneficial effect is similar to, and the most no longer repeats.
Fig. 7 determines the structural representation two of device for the recommended that the present invention provides, on the basis of embodiment illustrated in fig. 6
On, refer to Fig. 7, first determines that module 602 includes that first determines that unit the 6021, first acquiring unit 6022 and second determines list
Unit 6023, wherein,
First determine unit 6021 for, according to the type of fisrt feature set, at least one second feature set
Determine the same type second feature set identical with the type of fisrt feature set；
First acquiring unit 6022 is used for, and obtains each feature in fisrt feature set and with in type second feature set
The similarity of characteristic of correspondence；
Second determine unit 6023 for, according to each feature in fisrt feature set with in type second feature set
The similarity of characteristic of correspondence, determines fisrt feature set and the similarity with type second feature set.
Further, first determines that module 602 can also include second acquisition unit 6024 and judging unit 6025, its
In,
First determine unit 6021 for, according to the type of fisrt feature set, at least one second feature set
Determine the same type second feature set identical with the type of fisrt feature set；
Second acquisition unit 6024 is used for, and obtains the of the first essential feature of fisrt feature set and second feature set
Two essential feature, the first essential feature is identical with the type of the second essential feature；
Judging unit 6025 is used for, it is judged that the first essential feature is the most identical with the second essential feature；
First acquiring unit 6022 is used for, and judges that the first essential feature is identical with the second essential feature at judging unit 6025
Time, obtain each feature in fisrt feature set and with the similarity of characteristic of correspondence in type second feature set, and second
Determine unit 6023 for, according to each feature in fisrt feature set with characteristic of correspondence in type second feature set
Similarity, determines fisrt feature set and the similarity with type second feature set；
Second determines that unit 6023 is additionally operable to, and judges that the first essential feature and the second essential feature differ at judging unit
Time, determine that fisrt feature set is zero with the similarity of corresponding second feature set.
In a kind of possible embodiment, the first acquiring unit 6022 specifically for:
According to the type of fisrt feature, determine, in same type second feature set, the second feature that fisrt feature is corresponding,
Fisrt feature is identical with the type of second feature, and fisrt feature is any one feature in fisrt feature set；
Judge whether fisrt feature is numeric type；
The most then obtain fisrt feature and the ratio of second feature, it is judged that whether ratio is more than 1, the most then by ratio
Inverse is defined as the similarity of fisrt feature and second feature, if it is not, ratio is then defined as fisrt feature and second feature
Similarity；
If it is not, then judge that fisrt feature is the most identical with second feature, if, it is determined that fisrt feature and second feature
Similarity is 1, if not, it is determined that the similarity of fisrt feature and second feature is 0.
In alternatively possible embodiment, second determine unit 6023 specifically for:
Obtain each feature and the similarity sum with each feature in type second feature set in fisrt feature set；
Obtain fisrt feature set and with the non-repeating features number in type second feature set；
According to similarity sum non-repeating features number, determine the phase of fisrt feature set and same type second feature set
Like degree.
In alternatively possible embodiment, second determine unit 6023 specifically for:
The ratio of similarity sum Yu non-repeating features number is defined as fisrt feature set and with type second feature
The similarity of set.
In alternatively possible embodiment, second determine module 6023 specifically for:
Similarity d of existing object and object to be selected is determined according to equation below one:
Wherein, K is the number of fisrt feature set, αiFor the weighted value of i-th fisrt feature set, diFor i-th first
The similarity of the second feature set that characteristic set is corresponding with i-th fisrt feature set.
In alternatively possible embodiment, the first acquisition module 601 specifically for:
Show the type of all fisrt feature set that existing object is corresponding；
Choose operation according to user to what the type of fisrt feature set inputted, determine the weight of each fisrt feature set
Value.
Further, this device can also include that the second acquisition module the 605, the 4th determines module 606 and pushing module
607, wherein,
Second acquisition module 605 is used for, and determines that module is each to be selected right with library of object to be selected according to existing object the 3rd
The similarity of elephant, after determining the recommended that existing object is corresponding, obtains working as of existing object place in library of object to be selected
Each object in the front page shows the priority of position；
4th determine module 606 for, similarity and each object according to each recommended and existing object show position
Priority, determine that recommended and object show the corresponding relation of position；
Pushing module 607 is used for, and recommended and corresponding relation are pushed to current page, so that at current page
Object displaying shows each recommended according to corresponding relation on position.
Recommended shown in the embodiment of the present invention determines that device can perform the technical side shown in said method embodiment
Case, it realizes principle and beneficial effect is similar to, and the most no longer repeats.
One of ordinary skill in the art will appreciate that: all or part of step realizing above-mentioned each method embodiment can be led to
The hardware crossing programmed instruction relevant completes.Aforesaid program can be stored in a computer read/write memory medium.This journey
Sequence upon execution, performs to include the step of above-mentioned each method embodiment；And aforesaid storage medium includes: ROM, RAM, magnetic disc or
The various media that can store program code such as person's CD.
Last it is noted that various embodiments above is only in order to illustrate technical scheme, it is not intended to limit；To the greatest extent
The present invention has been described in detail by pipe with reference to foregoing embodiments, it will be understood by those within the art that: it depends on
So the technical scheme described in foregoing embodiments can be modified, or the most some or all of technical characteristic is entered
Row equivalent；And these amendments or replacement, do not make the essence of appropriate technical solution depart from various embodiments of the present invention technology
The scope of scheme.
Claims (18)
1. a recommended determines method, it is characterised in that including:
Obtain at least one fisrt feature set of existing object, at least one second feature set, Yi Jiyong of object to be selected
The weighted value of each described fisrt feature set that family determines, described object to be selected is any one object in library of object to be selected；
Determine the similarity of each described fisrt feature set and corresponding second feature set respectively；
Weighted value according to each described fisrt feature set and the corresponding second feature collection of each described fisrt feature set
The similarity closed, determines the similarity of described existing object and described object to be selected；
According to described existing object and the similarity of each described object to be selected in described library of object to be selected, in described library of object to be selected
The middle recommended determining that described existing object is corresponding.
Method the most according to claim 1, it is characterised in that any at least one fisrt feature set described
One fisrt feature set, the described similarity determining each described fisrt feature set and corresponding second feature set respectively,
Including:
According to the type of described fisrt feature set, determine and described fisrt feature at least one second feature set described
The identical same type second feature set of type of set；
Obtain each feature in described fisrt feature set and characteristic of correspondence similar in described same type second feature set
Degree；
According to each feature in described fisrt feature set and characteristic of correspondence similar in described same type second feature set
Degree, determines the similarity of described fisrt feature set and described same type second feature set.
Method the most according to claim 1, it is characterised in that any at least one fisrt feature set described
One fisrt feature set, the described similarity determining each described fisrt feature set and corresponding second feature set respectively,
Including:
According to the type of described fisrt feature set, determine and described fisrt feature at least one second feature set described
The identical same type second feature set of type of set；
Obtain the first essential feature and second essential feature of described second feature set of described fisrt feature set, described
One essential feature is identical with the type of described second essential feature；
Judge that described first essential feature is the most identical with described second essential feature；
The most then obtain each feature in described fisrt feature set and characteristic of correspondence in described same type second feature set
Similarity, and according to characteristic of correspondence in each feature in described fisrt feature set and described same type second feature set
Similarity, determine the similarity of described fisrt feature set and described same type second feature set；
If not, it is determined that described fisrt feature set is zero with the similarity of corresponding second feature set.
The most according to the method in claim 2 or 3, it is characterised in that for any one in described fisrt feature set
Fisrt feature, obtains each feature in described fisrt feature set and characteristic of correspondence in described same type second feature set
Similarity, including:
According to the type of described fisrt feature, determine in described same type second feature set that described fisrt feature is corresponding
Two features, described fisrt feature is identical with the type of described second feature；
Judge whether described fisrt feature is numeric type；
The most then obtain described fisrt feature and the ratio of described second feature, it is judged that whether described ratio is more than 1, the most then
The inverse of described ratio is defined as described fisrt feature and the similarity of described second feature, if it is not, then by true for described ratio
It is set to described fisrt feature and the similarity of described second feature；
If it is not, then judge that described fisrt feature is the most identical with described second feature, if, it is determined that described fisrt feature and institute
The similarity stating second feature is 1, if not, it is determined that the similarity of described fisrt feature and described second feature is 0.
Method the most according to claim 4, it is characterised in that according to each feature in described fisrt feature set with described
With the similarity of each feature in type second feature set, determine described fisrt feature set and described same type second feature
The similarity of set, including:
The similarity of each feature obtained in described fisrt feature set in each feature and described same type second feature set it
With；
Obtain the non-repeating features number in described fisrt feature set and described same type second feature set；
According to non-repeating features number described in described similarity sum, determine described fisrt feature set and described same type second
The similarity of characteristic set.
Method the most according to claim 5, it is characterised in that according to non-repeating features described in described similarity sum
Number, determines the similarity of described fisrt feature set and described same type second feature set, including:
The ratio of described similarity sum Yu described non-repeating features number is defined as described fisrt feature set same with described
The similarity of type second feature set.
7. according to the method described in any one of claim 1-3, it is characterised in that according to the weight of each described fisrt feature set
Value and the similarity of the corresponding second feature set of each described fisrt feature set, determine described existing object and institute
State the similarity of object to be selected, including:
Similarity d of described existing object and described object to be selected is determined according to equation below one:
Wherein, described K is the number of fisrt feature set, described αiFor the weighted value of i-th fisrt feature set, described diFor
The similarity of the second feature set that i-th fisrt feature set is corresponding with described i-th fisrt feature set.
8. according to the method described in any one of claim 1-3, it is characterised in that obtain the weight of each described fisrt feature set
Value, including:
Show the type of all fisrt feature set that described existing object is corresponding；
Choose operation according to user to what the type of described fisrt feature set inputted, determine the power of each described fisrt feature set
Weight values.
9. according to the method described in any one of claim 1-3, it is characterised in that to be selected with described according to described existing object
In library of object, the similarity of each described object to be selected, determines that the recommendation that described existing object is corresponding is right in described library of object to be selected
As afterwards, also include:
Obtain each object in the current page at described existing object place and show the priority of position；
The priority of position is shown, really according to each described recommended and the similarity of described existing object and each described object
Fixed described recommended and described object show the corresponding relation of position；
Described recommended and described corresponding relation are pushed to described current page, so that at the object of described current page
Show and show each described recommended according to described corresponding relation on position.
10. a recommended determines device, it is characterised in that including:
First acquisition module, for obtain at least one fisrt feature set of existing object, object to be selected at least one the
The weighted value of each described fisrt feature set that two characteristic sets and user determine, described object to be selected is library of object to be selected
In any one object；
First determines module, for determining the similarity of each described fisrt feature set and corresponding second feature set respectively；
Second determines module, for according to the weighted value of each described fisrt feature set and each described fisrt feature set with
The similarity of the second feature set of its correspondence, determines the similarity of described existing object and described object to be selected；
3rd determines module, similar for according to described existing object described to be selected object each to described library of object to be selected
Degree, determines, in described library of object to be selected, the recommended that described existing object is corresponding.
11. devices according to claim 10, it is characterised in that described first determine module include first determine unit,
First acquiring unit and second determines unit, wherein,
Described first determine unit for, according to the type of described fisrt feature set, at least one second feature collection described
Conjunction determines the same type second feature set identical with the type of described fisrt feature set；
Described first acquiring unit is used for, and obtains each feature in described fisrt feature set and described same type second feature collection
The similarity of characteristic of correspondence in conjunction；
Described second determine unit for, according to each feature in described fisrt feature set and described same type second feature collection
The similarity of characteristic of correspondence in conjunction, determines the similarity of described fisrt feature set and described same type second feature set.
12. devices according to claim 11, it is characterised in that described first determines that module also includes second acquisition unit
And judging unit, wherein,
Described first determine unit for, according to the type of described fisrt feature set, at least one second feature collection described
Conjunction determines the same type second feature set identical with the type of described fisrt feature set；
Described second acquisition unit is used for, and obtains the first essential feature of described fisrt feature set and described second feature set
The second essential feature, described first essential feature is identical with the type of described second essential feature；
Described judging unit is used for, it is judged that described first essential feature is the most identical with described second essential feature；
Described first acquiring unit is used for, and judges described first essential feature and described second essential feature at described judging unit
Time identical, obtain each feature in described fisrt feature set and the phase of characteristic of correspondence in described same type second feature set
Like degree, and described second determine unit for, special with described same type second according to each feature in described fisrt feature set
In collection conjunction, the similarity of characteristic of correspondence, determines that described fisrt feature set is similar to described same type second feature set
Degree；
Described second determines that unit is additionally operable to, and judges described first essential feature and the described second necessary spy at described judging unit
Levy when differing, determine that described fisrt feature set is zero with the similarity of corresponding second feature set.
13. according to the device described in claim 11 or 12, it is characterised in that described first acquiring unit specifically for:
According to the type of described fisrt feature, determine in described same type second feature set that described fisrt feature is corresponding
Two features, described fisrt feature is identical with the type of described second feature, and fisrt feature is appointing in described fisrt feature set
Anticipate a feature；
Judge whether described fisrt feature is numeric type；
The most then obtain described fisrt feature and the ratio of described second feature, it is judged that whether described ratio is more than 1, the most then
The inverse of described ratio is defined as described fisrt feature and the similarity of described second feature, if it is not, then by true for described ratio
It is set to described fisrt feature and the similarity of described second feature；
If it is not, then judge that described fisrt feature is the most identical with described second feature, if, it is determined that described fisrt feature and institute
The similarity stating second feature is 1, if not, it is determined that the similarity of described fisrt feature and described second feature is 0.
14. devices according to claim 13, it is characterised in that described second determine unit specifically for:
The similarity of each feature obtained in described fisrt feature set in each feature and described same type second feature set it
With；
Obtain the non-repeating features number in described fisrt feature set and described same type second feature set；
According to non-repeating features number described in described similarity sum, determine described fisrt feature set and described same type second
The similarity of characteristic set.
15. devices according to claim 14, it is characterised in that described second determine unit specifically for:
The ratio of described similarity sum Yu described non-repeating features number is defined as described fisrt feature set same with described
The similarity of type second feature set.
16. according to the device described in any one of claim 10-12, it is characterised in that described second determine module specifically for:
Similarity d of described existing object and described object to be selected is determined according to equation below one:
Wherein, described K is the number of fisrt feature set, described αiFor the weighted value of i-th fisrt feature set, described diFor
The similarity of the second feature set that i-th fisrt feature set is corresponding with described i-th fisrt feature set.
17. according to the device described in any one of claim 10-12, it is characterised in that described first acquisition module specifically for:
Show the type of all fisrt feature set that described existing object is corresponding；
Choose operation according to user to what the type of described fisrt feature set inputted, determine the power of each described fisrt feature set
Weight values.
18. according to the device described in any one of claim 10-12, it is characterised in that described device also includes the second acquisition mould
Block, the 4th determine module and pushing module, wherein,
Described second acquisition module is used for, and determines that module is according in described existing object and described library of object to be selected the described 3rd
The similarity of each described object to be selected, after determining, in described library of object to be selected, the recommended that described existing object is corresponding,
Obtain each object in the current page at described existing object place and show the priority of position；
Described 4th determine module for, according to the similarity of each described recommended and described existing object and each described
Object shows the priority of position, determines that described recommended shows the corresponding relation of position with described object；
Described pushing module is used for, and described recommended and described corresponding relation are pushed to described current page, so that
Each described recommended is shown according to described corresponding relation on the object displaying position of described current page.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610624763.2A CN106294628A (en) | 2016-07-28 | 2016-07-28 | Recommended determines method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610624763.2A CN106294628A (en) | 2016-07-28 | 2016-07-28 | Recommended determines method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106294628A true CN106294628A (en) | 2017-01-04 |
Family
ID=57664033
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610624763.2A Pending CN106294628A (en) | 2016-07-28 | 2016-07-28 | Recommended determines method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106294628A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107193932A (en) * | 2017-05-18 | 2017-09-22 | 北京京东尚科信息技术有限公司 | Information-pushing method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110153598A1 (en) * | 2009-12-22 | 2011-06-23 | Kamimaeda Naoki | Information Processing Apparatus and Method |
| CN105760544A (en) * | 2016-03-16 | 2016-07-13 | 合网络技术（北京）有限公司 | Video recommendation method and device |
| CN105787055A (en) * | 2016-02-26 | 2016-07-20 | 合网络技术（北京）有限公司 | Information recommendation method and device |
| CN105812937A (en) * | 2014-12-30 | 2016-07-27 | Tcl集团股份有限公司 | Television program recommending method and television program recommending device |
-
2016
- 2016-07-28 CN CN201610624763.2A patent/CN106294628A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110153598A1 (en) * | 2009-12-22 | 2011-06-23 | Kamimaeda Naoki | Information Processing Apparatus and Method |
| CN105812937A (en) * | 2014-12-30 | 2016-07-27 | Tcl集团股份有限公司 | Television program recommending method and television program recommending device |
| CN105787055A (en) * | 2016-02-26 | 2016-07-20 | 合网络技术（北京）有限公司 | Information recommendation method and device |
| CN105760544A (en) * | 2016-03-16 | 2016-07-13 | 合网络技术（北京）有限公司 | Video recommendation method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107193932A (en) * | 2017-05-18 | 2017-09-22 | 北京京东尚科信息技术有限公司 | Information-pushing method and device |
| CN107193932B (en) * | 2017-05-18 | 2020-06-30 | 北京京东尚科信息技术有限公司 | Information pushing method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105808227B (en) | A kind of fine arts resource file detection method and device | |
| CN104572668B (en) | Method and apparatus based on multiple pattern file generated Merge Styles files | |
| CN104794116B (en) | The layout method and device of element in a kind of page | |
| CN104407779B (en) | The arrangement display methods and device of graphical interfaces | |
| CA3139243A1 (en) | Information collection method, device, computer equipment and storage medium | |
| CN106445971A (en) | Application recommendation method and system | |
| CN107590252A (en) | Method and device for information exchange | |
| CN109828811A (en) | A kind of methods of exhibiting and device of card object | |
| CN105488125A (en) | Page access method and apparatus | |
| CN108038135A (en) | Electronic device, the method for multilist correlation inquiry and storage medium | |
| CN107908553A (en) | Correlation testing method, application server and computer-readable recording medium | |
| CN106446027A (en) | Information recommendation method and apparatus | |
| CN108388509A (en) | A kind of method for testing software, computer readable storage medium and terminal device | |
| CN105574041A (en) | Data reconstruction method and device | |
| CN105550923A (en) | Method and apparatus for displaying network products on product shelf | |
| CN105320430A (en) | Page information control method and device | |
| US20170199912A1 (en) | Behavior topic grids | |
| CN106156132A (en) | Picture loading display method and device | |
| CN110324352A (en) | Identify the method and device of batch registration account group | |
| CN106294628A (en) | Recommended determines method and device | |
| CN100357952C (en) | Binary data access method | |
| CN110297957A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN106547658A (en) | A kind of automated testing method and device | |
| CN106598829A (en) | Webpage automation test method and apparatus | |
| CN112559866A (en) | College book reading recommendation method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170104 |