CN105224646A - Object relation analysis method and device and electronic equipment - Google Patents
Object relation analysis method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses an object relationship analysis method, an object relationship analysis device and electronic equipment, wherein the object relationship analysis method comprises the following steps: acquiring a behavior log of each object in an object set to be analyzed; respectively carrying out correlation analysis on the behavior log of each object and each feature in a preset behavior feature set to obtain correlation information of each object and each feature; and determining the relationship strength type of each two objects under each characteristic according to the correlation information of each two objects and each characteristic in the object set to be analyzed so as to obtain the object relationship in the object set to be analyzed. The object relation analysis method provided by the embodiment of the invention can be used for analyzing the relation among the objects according to different characteristics and obtaining the relation strength type, so that the obtained object relation is more accurate and refined.
Description
Technical field
The present invention relates to field of computer technology, particularly relate to a kind of object relationship analytical approach, device and electronic equipment.
Background technology
Along with the fast development of internet, being applied in people's life of internet serves more and more important effect.And the prosperity of Internet market, also open large data age.Under large data background, data volume is more and more huger, and the relation between data becomes increasingly complex.To application service, there is directive function by excavating the relation obtained between object to large data.For example, if excavate between user A and user B relevant, then content user A can paid close attention to or interested commending contents to B, thus are provided personalized service for user.
At present, the incidence relation between digging user mainly contain following methods:
(1) incidence relation between user is determined based on social networks data such as associated person informations.
(2) excavate two degree of human connections based on social platform, such as, all users paying close attention to same user are carried out association analysis, and be associated relation.
But the incidence relation determined by said method is comparatively single, relational hierarchy is meticulous not, thus is difficult to according to the incidence relation that said method is determined the application service providing accurate refinement.
Summary of the invention
The present invention is intended to solve one of technical matters in correlation technique at least to a certain extent.For this reason, the object of first aspect present invention is to propose a kind of object relationship analytical approach, can obtain object relationship that is more accurate, refinement.
The object of a second aspect of the present invention is to propose a kind of object relationship analytical equipment.
3rd object of the present invention is to propose a kind of electronic equipment.
4th object of the present invention is to propose a kind of storage medium.
5th object of the present invention is to propose a kind of application program.
For reaching above-mentioned purpose, embodiment proposes a kind of object relationship analytical approach according to a first aspect of the present invention, comprising: the user behaviors log obtaining each object in object set to be analyzed; Respectively correlation analysis is carried out, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set; The relationship strength type of described every two objects under each feature is determined, to obtain the object relationship in described object set to be analyzed according to the correlation information of every two objects and each feature in described object set to be analyzed.
In one embodiment of the invention, described correlation information is weight information, the relationship strength type of described every two objects under each feature is determined according to the correlation information of every two objects and each feature in described object set to be analyzed, comprise: the relevance weight determining the first object and feature i in described every two objects is the first relevance weight, in described every two objects, the relevance weight of the second object and described feature i is the second relevance weight, wherein, i=1, N, for positive integer, N is the quantity of feature in described default behavioural characteristic set, calculate the similarity of described first relevance weight and the second relevance weight, the relationship strength type of described every two objects under described feature i is determined according to described similarity.
In one embodiment of the invention, the method also comprises: the user behaviors log gathering object in multiple regional extent; Respectively feature extraction is carried out to the user behaviors log of object in each regional extent, to obtain subcharacter set corresponding to each regional extent; Subcharacter set corresponding for described multiple regional extent is merged, to obtain described default behavioural characteristic set.
In one embodiment of the invention, the method also comprises: upgrade described default behavioural characteristic set according to the first predetermined period; Again obtain the correlation information of each object and each feature according to the default behavioural characteristic set after renewal, and redefine the relationship strength type of every two objects under each feature, to upgrade the object relationship in described object set to be analyzed.
In one embodiment of the invention, the method also comprises: verify according to the accuracy of the second predetermined period to the object relationship in described object set to be analyzed.
In one embodiment of the invention, described checking according to the accuracy of the second predetermined period to the object relationship in described object set to be analyzed specifically comprises: in described object set to be analyzed, randomly draw the first object set; Obtain the second object set according to the object relationship in described object set to be analyzed, wherein, described second object set is the set that the object being the first kind with the relationship strength type of described first object set under fisrt feature is formed; Again the relationship strength type of two objects under described fisrt feature is obtained according to the user behaviors log of described first object set and the user behaviors log of described second object set; If there is the relationship strength type of two objects under described fisrt feature again obtained not conform to the described first kind, then judge that the object relationship in described object set to be analyzed is accurate, otherwise judge that the object relationship in described object set to be analyzed is inaccurate.
In one embodiment of the invention, described default behavioural characteristic set comprises behavioural characteristic and application scenarios feature.
For achieving the above object, the embodiment of a second aspect of the present invention provides a kind of object relationship analytical equipment, comprising: acquisition module, for obtaining the user behaviors log of each object in object set to be analyzed; Analysis module, for carrying out correlation analysis respectively, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set; Determination module, for determining the relationship strength type of described every two objects under each feature according to the correlation information of every two objects and each feature in described object set to be analyzed, to obtain the object relationship in described object set to be analyzed.
In one embodiment of the invention, described correlation information is weight information, described determination module specifically for: the relevance weight determining the first object and feature i in described every two objects is the first relevance weight, in described every two objects, the relevance weight of the second object and described feature i is the second relevance weight, wherein, and i=1, N is positive integer, and N is the quantity of feature in described default behavioural characteristic set; Calculate the similarity of described first relevance weight and the second relevance weight; The relationship strength type of described every two objects under described feature i is determined according to described similarity.
In one embodiment of the invention, this device also comprises: acquisition module, for gathering the user behaviors log of object in multiple regional extent; Characteristic extracting module, for carrying out feature extraction respectively to the user behaviors log of object in each regional extent, to obtain subcharacter set corresponding to each regional extent; Merge module, for subcharacter set corresponding for described multiple regional extent being merged, to obtain described default behavioural characteristic set.
In one embodiment of the invention, this device also comprises update module, for: according to the first predetermined period, described default behavioural characteristic set is upgraded; Again obtain the correlation information of each object and each feature according to the default behavioural characteristic set after renewal, and redefine the relationship strength type of every two objects under each feature, to upgrade the object relationship in described object set to be analyzed.
In one embodiment of the invention, this device also comprises: authentication module, for verifying according to the accuracy of the second predetermined period to the object relationship in described object set to be analyzed.
In one embodiment of the invention, described authentication module specifically for: in described object set to be analyzed, randomly draw the first object set; Obtain the second object set according to the object relationship in described object set to be analyzed, wherein, described second object set is the set that the object being the first kind with the relationship strength type of described first object set under fisrt feature is formed; Again the relationship strength type of two objects under described fisrt feature is obtained according to the user behaviors log of described first object set and the user behaviors log of described second object set; If there is the relationship strength type of two objects under described fisrt feature again obtained not conform to the described first kind, then judge that the object relationship in described object set to be analyzed is accurate, otherwise judge that the object relationship in described object set to be analyzed is inaccurate.
In one embodiment of the invention, described default behavioural characteristic set comprises behavioural characteristic and application scenarios feature.
For achieving the above object, third aspect present invention embodiment proposes a kind of electronic equipment, and this electronic equipment comprises processor, storer, communication interface and bus; Described processor, described storer and described communication interface are connected by described bus and complete mutual communication; Described storer stores executable programs code; Described processor runs the program corresponding with described executable program code by reading the executable program code stored in described storer, for execution following steps: the user behaviors log obtaining each object in object set to be analyzed; Respectively correlation analysis is carried out, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set; The relationship strength type of described every two objects under each feature is determined, to obtain the object relationship in described object set to be analyzed according to the correlation information of every two objects and each feature in described object set to be analyzed.
For achieving the above object, fourth aspect present invention embodiment proposes a kind of storage medium, and wherein, described storage medium is for storing application program, and described application program is used for operationally performing a kind of object relationship analytical approach of the present invention.
For achieving the above object, fifth aspect present invention embodiment proposes a kind of application program, and wherein, described application program is used for operationally performing a kind of object relationship analytical approach of the present invention.
Embodiments of the invention, by carrying out correlation analysis to the user behaviors log of each object to be analyzed and each default feature, obtain the correlativity of each object and each feature, and determine the relationship strength type of these two objects under this feature according to the similarity of the correlativity of two objects and same feature, can analyze the relation between object for different features, and obtain relationship strength type, the object relationship obtained is more accurate, refinement, can effectively excavate potential data value, so that provide corresponding service for different relationship strength types, improve efficiency and the accuracy of service.
Accompanying drawing explanation
Above-mentioned and/or additional aspect of the present invention and advantage will become obvious and easy understand from accompanying drawing below combining to the description of embodiment, wherein:
Fig. 1 is the process flow diagram of object relationship analytical approach according to an embodiment of the invention;
Fig. 2 is the process flow diagram of object relationship analytical approach in accordance with another embodiment of the present invention;
Fig. 3 is the schematic diagram of the object relationship analytical approach according to the present invention's specific embodiment;
Fig. 4 is the structural representation of object relationship analytical equipment according to an embodiment of the invention;
Fig. 5 is the structural representation of object relationship analytical equipment in accordance with another embodiment of the present invention;
Fig. 6 is the structural representation of the object relationship analytical equipment according to another embodiment of the present invention;
Fig. 7 is the structural representation of electronic equipment according to an embodiment of the invention.
Embodiment
Be described below in detail embodiments of the invention, the example of described embodiment is shown in the drawings, and wherein same or similar label represents same or similar element or has element that is identical or similar functions from start to finish.Be exemplary below by the embodiment be described with reference to the drawings, be intended to for explaining the present invention, and can not limitation of the present invention be interpreted as.
Because the defining method of current object relationship is comparatively single, relational hierarchy is meticulous not, and the relation between causing being difficult to according to object provides the application server of accurate refinement.Such as, for example, user A and the interested book subject matter of user B are similar, but the concern in film is but not identical.According to the method in correlation technique, because user A and the interested book subject matter of user B are similar, therefore, the incidence relation between user A and user B can be set up, and the film that user A pays close attention to is recommended user B, but in fact, user B is to such film and lose interest in, this just cause recommend inaccurate.
In order to solve the problem, the present invention proposes a kind of object relationship analytical approach, device and electronic equipment.Below with reference to the accompanying drawings object relationship analytical approach according to the embodiment of the present invention, device and electronic equipment are described.
Fig. 1 is the process flow diagram of object relationship analytical approach according to an embodiment of the invention, and the method is applied to server.
As shown in Figure 1, this object relationship analytical approach comprises the following steps.
S101, obtains the user behaviors log of each object in object set to be analyzed.
Wherein, user behaviors log can be the log file that the object behavior in a period of time occurs.
S102, carries out correlation analysis, to obtain the correlation information of each object and each feature respectively to each feature in the user behaviors log of each object and default behavioural characteristic set.
Wherein, preset behavioural characteristic set and comprise behavioural characteristic and application scenarios feature.The behavior characteristic of correspondence of behavioural characteristic and object, such as, if whether user's download pictures behavior characteristic of correspondence to be user interested in image information.The scene of the behavior transmission of application scenarios feature and object, such as, time, place, network type etc. that behavior occurs.
For example, suppose that one of them behavioural characteristic is the Interest Measure (referred to as feature p) to picture, can according to history picture number last time of object or the interior uploading pictures quantity of Preset Time (such as weekly).Interest Measure can be divided into three relevancy type: loseing interest in, (corresponding object is that the picture number that history is uploaded is no more than 30, or uploading pictures number of times is no more than 2 times weekly), it is interested that ((corresponding object is that the picture number uploaded of history is between 30 and 100, or weekly uploading pictures number of times between 2 ~ 4 times), very interested (corresponding object be the picture number uploaded of history more than 100, or weekly uploading pictures number of times more than 4 times).Thus the user behaviors log by each object analyzes quantity and the time point information of the uploading pictures of this object, thus determine this object feature p is belonged to lose interest in, interested or very interested relevancy type.
S103, determines the relationship strength type of every two objects under each feature, to obtain the object relationship in object set to be analyzed according to the correlation information of every two objects and each feature in object set to be analyzed.
In one embodiment of the invention, the correlation information obtained in S102 can be carried out being quantified as relevance weight, such as, by corresponding for above-mentioned feature p loseing interest in, the relevance weight of interested still very interested relevancy type can be set to 0 respectively, 0.5,1.Thus the correlativity of feature in object and default behavioural characteristic set can be embodied more intuitively, and be convenient to successor relationship analysis.
Particularly, first can determine that the relevance weight of the first object and feature i in every two objects is the first relevance weight, in every two objects, the relevance weight of the second object and feature i is the second relevance weight, wherein, i=1 ... N is positive integer, and N is the quantity of feature in default behavioural characteristic set, wherein, i=1 ..., N, for positive integer, N is the quantity of feature in default behavioural characteristic set.Then the similarity of the first relevance weight and the second relevance weight is calculated.Wherein, the similarity of relevance weight obtains by the difference calculated between relevance weight, and between relevance weight, difference is less, and the similarity of relevance weight is larger.
Afterwards, the relationship strength type of every two objects under feature i is determined according to described similarity.Particularly, the similarity threshold scope that different relationship strength type is corresponding can be set, thus the similarity threshold scope that can fall into according to described similarity determines the relationship strength type of every two objects under feature i.Thus the relation of any two objects obtained in object set to be analyzed under a certain feature and intensity thereof.Further, by setting up the relational database of object relationship in object set to be analyzed, to be used for storing above-mentioned object relationship.
The object relationship analytical approach of the embodiment of the present invention, by carrying out correlation analysis to the user behaviors log of each object to be analyzed and each default feature, obtain the correlativity of each object and each feature, and determine the relationship strength type of these two objects under this feature according to the similarity of the correlativity of two objects and same feature, can analyze the relation between object for different features, and obtain relationship strength type, the object relationship obtained is more accurate, refinement, can effectively excavate potential data value, so that provide corresponding service for different relationship strength types, improve efficiency and the accuracy of service.
Should be appreciated that the default behavioural characteristic set in above-described embodiment is carry out feature extraction to the behavior of a large amount of object in advance to obtain.Fig. 2 is the process flow diagram of object relationship analytical approach in accordance with another embodiment of the present invention.In this embodiment, the process of establishing of default behavioural characteristic set is comprised.Specifically as shown in Figure 2, the object relationship analytical approach of the embodiment of the present invention, comprises the following steps:
S201, gathers the user behaviors log of object in multiple regional extent.
In an embodiment of the present invention, over a period to come the user behaviors log of the object in multiple regional extent is gathered, and the behavior of the object of some in each region is gathered, can improve quantity and the range of object behavior, thus the behavioural characteristic finally obtained can be more comprehensive.
S202, carries out feature extraction respectively to the user behaviors log of object in each regional extent, to obtain subcharacter set corresponding to each regional extent.
Wherein, filtration can be carried out to user behaviors log as required and go forward side by side formatting lines in the process of carrying out feature extraction, thus filter out invalid information, and obtain the journal file of consolidation form.
In the process of feature extraction, if the behavior meeting feature i in user behaviors log has multiple, then this multiple behavior can be merged, only need record the quantity of the behavior meeting feature i, thus can reduced data amount.
S203, merges subcharacter set corresponding for multiple regional extent, to obtain default behavioural characteristic set.
For example, if acquire m regional extent/object behavior daily record, obtain m user behaviors log set respectively.Feature extraction can be carried out to each user behaviors log set, obtain m behavioural characteristic set M
1..., M
m.Then to M
1..., M
mmerge, namely obtain M
1..., M
munion M, be the default behavioural characteristic set that will build.
S204, obtains the user behaviors log of each object in object set to be analyzed.
S205, carries out correlation analysis, to obtain the correlation information of each object and each feature respectively to each feature in the user behaviors log of each object and default behavioural characteristic set.
S206, determines the relationship strength type of every two objects under each feature according to the correlation information of every two objects and each feature in object set to be analyzed.
Step S204-S206 is identical with middle S101-S103 embodiment illustrated in fig. 1, does not repeat them here.
Fig. 3 is the schematic diagram of the object relationship analytical approach according to the present invention's specific embodiment.As shown in Figure 3, characteristic set M1 comprises feature a and c, and characteristic set M2 comprises feature b, c, d and e, then preset behavioural characteristic set and comprise a, b, c, d and e five features.For object set to be analyzed, comprising UserA, UserB, UserC and UserD, the object relationship finally obtained is:
UserAUserBaw:1；
UserAUserBcw:2；
UserAUserCdw:2；
UserBUserDew:3；
Wherein, w represents relationship strength type.
Alternatively, in an example of the present invention, can upgrade default behavioural characteristic set according to the first predetermined period; According to the default behavioural characteristic set after upgrading, the relationship strength type of every two objects under each feature is upgraded.
Particularly, more incremental analysis can be done according to the user behaviors log of object newly-increased in the first predetermined period, analyze together with user behaviors log used by user behaviors log newly-increased in above-mentioned multiple regional extent and last time and carry out feature extraction, and the extraction result of multiple regional extent is merged, to obtain the default behavioural characteristic storehouse upgraded.Then S203-S206 is repeated according to the default behavioural characteristic storehouse after renewal, namely the correlation information of each object and each feature is again obtained, and redefine the relationship strength type of every two objects under each feature, upgrade with the object relationship treated in analytic target set.Wherein, in S204, the user behaviors log of object to be analyzed may also be the user behaviors log comprising and increasing newly in the first predetermined period.Period 1 can set as required, such as, can be one day.
Thus can data source be constantly updated so that with the timely adjustment of object relationship and optimization.
Alternatively, in an example of the present invention, the accuracy of the object relationship can treated in analytic target set according to the second predetermined period is verified.
Particularly, the accuracy treating the object relationship in analytic target set according to the second predetermined period is carried out checking and can specifically be comprised:
S301, randomly draws the first object set in object set to be analyzed;
S302, obtains the second object set according to the object relationship in object set to be analyzed, wherein, and the set that the object of the second object set to be relationship strength type with the first object set under fisrt feature be first kind is formed;
Particularly, for each object l in the first object set, can obtain with the relationship strength type of object l under fisrt feature according to the object relationship in fixed object set to be analyzed and be all objects of the first kind, obtain the second object set thus.
S303, obtains the relationship strength type of two objects under fisrt feature again according to the user behaviors log of the first object set and the user behaviors log of the second object set;
Square ratio juris is identical with S102 with S102 particularly, no longer carefully states at this.
S304, does not conform to the first kind if there is the relationship strength type of two objects under fisrt feature again obtained, then judge that the object relationship in object set to be analyzed is accurate, otherwise judge that the object relationship in object set to be analyzed is inaccurate.
Step S102 and S103 can be repeated according to the user behaviors log of current collection if inaccurate, upgrade with the object relationship treated in analytic target set.
In one embodiment of the invention, second predetermined period can be identical with the first predetermined period, namely each upgrade the relationship strength type of every two objects under each feature time, carry out Accuracy Verification, thus while renewal, the accuracy upgrading result can be ensured.
In order to realize above-described embodiment, the present invention also proposes a kind of object relationship analytical equipment.
Fig. 4 is the structural representation of object relationship analytical equipment according to an embodiment of the invention, and this device can be applicable in server.
As shown in Figure 4, this object relationship analytical equipment, comprising: acquisition module 10, analysis module 20 and determination module 30.
Particularly, acquisition module 10 is for obtaining the user behaviors log of each object in object set to be analyzed.
Wherein, user behaviors log can be the log file that the object behavior in a period of time occurs.
Analysis module 20 is for carrying out correlation analysis respectively, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set.
Wherein, preset behavioural characteristic set and comprise behavioural characteristic and application scenarios feature.The behavior characteristic of correspondence of behavioural characteristic and object, such as, if whether user's download pictures behavior characteristic of correspondence to be user interested in image information.The scene of the behavior transmission of application scenarios feature and object, such as, time, place, network type etc. that behavior occurs.
For example, suppose that one of them behavioural characteristic is the Interest Measure (referred to as feature p) to picture, can according to history picture number last time of object or the interior uploading pictures quantity of Preset Time (such as weekly).Interest Measure can be divided into three relevancy type: loseing interest in, (corresponding object is that the picture number that history is uploaded is no more than 30, or uploading pictures number of times is no more than 2 times weekly), it is interested that ((corresponding object is that the picture number uploaded of history is between 30 and 100, or weekly uploading pictures number of times between 2 ~ 4 times), very interested (corresponding object be the picture number uploaded of history more than 100, or weekly uploading pictures number of times more than 4 times).Thus analysis module 20 analyzes quantity and the time point information of the uploading pictures of this object by the user behaviors log of each object, thus determine this object feature p is belonged to lose interest in, interested or very interested relevancy type.
Determination module 30 is for determining the relationship strength type of every two objects under each feature, to obtain the object relationship in object set to be analyzed according to the correlation information of every two objects and each feature in object set to be analyzed.
In one embodiment of the invention, the correlation information that analysis module 20 can be obtained carries out being quantified as relevance weight, and such as, by corresponding for above-mentioned feature p loseing interest in, the relevance weight of interested still very interested relevancy type can be set to 0 respectively, 0.5,1.Thus the correlativity of feature in object and default behavioural characteristic set can be embodied more intuitively, and be convenient to successor relationship analysis.
Particularly, first determination module 30 can determine that the relevance weight of the first object and feature i in every two objects is the first relevance weight, and in every two objects, the relevance weight of the second object and feature i is the second relevance weight, wherein, i=1 ... N is positive integer, and N is the quantity of feature in default behavioural characteristic set, wherein, i=1 ..., N, for positive integer, N is the quantity of feature in default behavioural characteristic set.Then the similarity of the first relevance weight and the second relevance weight is calculated.Wherein, the similarity of relevance weight obtains by the difference calculated between relevance weight, and between relevance weight, difference is less, and the similarity of relevance weight is larger.
Afterwards, determination module 30 determines the relationship strength type of every two objects under feature i according to described similarity.Particularly, determination module 30 can set similarity threshold scope corresponding to different relationship strength type, thus the similarity threshold scope that can fall into according to described similarity determines the relationship strength type of every two objects under feature i.Thus the relation of any two objects obtained in object set to be analyzed under a certain feature and intensity thereof.Further, by setting up the relational database of object relationship in object set to be analyzed, to be used for storing above-mentioned object relationship.
The object relationship analytical equipment of the embodiment of the present invention, by carrying out correlation analysis to the user behaviors log of each object to be analyzed and each default feature, obtain the correlativity of each object and each feature, and determine the relationship strength type of these two objects under this feature according to the similarity of the correlativity of two objects and same feature, can analyze the relation between object for different features, and obtain relationship strength type, the object relationship obtained is more accurate, refinement, can effectively excavate potential data value, so that provide corresponding service for different relationship strength types, improve efficiency and the accuracy of service.
Should be appreciated that the default behavioural characteristic set in above-described embodiment is carry out feature extraction to the behavior of a large amount of object in advance to obtain.Fig. 5 is the structural representation of object relationship analytical equipment in accordance with another embodiment of the present invention.As shown in Figure 5, this object relationship analytical equipment, comprising: acquisition module 10, analysis module 20, determination module 30, acquisition module 40, characteristic extracting module 50 and merging module 60.
Acquisition module 40 is for gathering the user behaviors log of object in multiple regional extent.
In an embodiment of the present invention, acquisition module 40 gathers the user behaviors log of the object in multiple regional extent over a period to come, and the behavior of the object of some in each region is gathered, can improve quantity and the range of object behavior, thus the behavioural characteristic finally obtained can be more comprehensive.
Characteristic extracting module 50 for carrying out feature extraction respectively to the user behaviors log of object in each regional extent, to obtain subcharacter set corresponding to each regional extent.
Wherein, characteristic extracting module 50 can be carried out filtration to user behaviors log as required and to be gone forward side by side formatting lines in the process of carrying out feature extraction, thus filters out invalid information, and obtains the journal file of consolidation form.
In the process of feature extraction, if the behavior meeting feature i in user behaviors log has multiple, then this multiple behavior can be merged, only need record the quantity of the behavior meeting feature i, thus can reduced data amount.
Merge module 60 for subcharacter set corresponding for multiple regional extent being merged, to obtain default behavioural characteristic set.
For example, if acquire m regional extent/object behavior daily record, obtain m user behaviors log set respectively.Feature extraction can be carried out to each user behaviors log set, obtain m behavioural characteristic set M
1..., M
m.Then to M
1..., M
mmerge, namely obtain M
1..., M
munion M, be the default behavioural characteristic set that will build.
Fig. 3 is the schematic diagram of the object relationship analytical approach according to the present invention's specific embodiment.As shown in Figure 3, characteristic set M1 comprises feature a and c, and characteristic set M2 comprises feature b, c, d and e, then preset behavioural characteristic set and comprise a, b, c, d and e five features.For object set to be analyzed, comprising UserA, UserB, UserC and UserD, the object relationship finally obtained is:
UserAUserBaw:1；
UserAUserBcw:2；
UserAUserCdw:2；
UserBUserDew:3；
Wherein, w represents relationship strength type.
Fig. 6 is the structural representation of the object relationship analytical equipment according to another embodiment of the present invention.As shown in Figure 6, this object relationship analytical equipment, comprising: acquisition module 10, analysis module 20, determination module 30, acquisition module 40, characteristic extracting module 50, merging module 60, update module 70 and authentication module 80.
Wherein, update module 70 is for upgrading default behavioural characteristic set according to the first predetermined period; Again obtain the correlation information of each object and each feature according to the default behavioural characteristic set after renewal, and redefine the relationship strength type of every two objects under each feature, upgrade with the object relationship treated in analytic target set.
Particularly, update module 70 more can do incremental analysis according to the user behaviors log of object newly-increased in the first predetermined period, analyze together with user behaviors log used by user behaviors log newly-increased in above-mentioned multiple regional extent and last time and carry out feature extraction, and the extraction result of multiple regional extent is merged, to obtain the default behavioural characteristic storehouse upgraded.Then again obtain the correlation information of each object and each feature according to the default behavioural characteristic storehouse after renewal, and redefine the relationship strength type of every two objects under each feature, upgrade with the object relationship treated in analytic target set.Wherein, the user behaviors log of object to be analyzed may also be and comprises user behaviors log newly-increased in the first predetermined period.Period 1 can set as required, such as, can be one day.Thus can data source be constantly updated so that with the timely adjustment of object relationship and optimization.
Authentication module 80 is verified for the accuracy of the object relationship treated in analytic target set according to the second predetermined period.
Particularly, authentication module 80 for randomly drawing the first object set in object set to be analyzed; The second object set is obtained according to the object relationship in object set to be analyzed, wherein, the set that the object of the second object set to be relationship strength type with the first object set under fisrt feature be first kind is formed; Again the relationship strength type of two objects under fisrt feature is obtained according to the user behaviors log of the first object set and the user behaviors log of the second object set; If there is the relationship strength type of two objects under fisrt feature again obtained not conform to the first kind, then judge that the object relationship in object set to be analyzed is accurate, otherwise judge that the object relationship in object set to be analyzed is inaccurate.
If the inaccurate object relationship can treated in analytic target set according to the user behaviors log of current collection upgrades.
In one embodiment of the invention, second predetermined period can be identical with the first predetermined period, namely each upgrade the relationship strength type of every two objects under each feature time, carry out Accuracy Verification, thus while renewal, the accuracy upgrading result can be ensured.
In order to realize above-described embodiment, the present invention also proposes a kind of electronic equipment.
Fig. 7 is the structural representation of electronic equipment according to an embodiment of the invention.
As shown in Figure 7, this electronic equipment comprises processor 71, storer 72, communication interface 73 and bus 74, wherein: processor 71, storer 72 and communication interface 73 are connected by bus 74 and complete mutual communication; Storer 72 stores executable programs code; Processor 71 runs the program corresponding with executable program code by reading the executable program code stored in storer 72, for performing the step shown in Fig. 1.Detailed process can refer to the explanation of the object relationship analytical approach part shown in Fig. 1 of the present invention, does not repeat them here.
The electronic equipment of the embodiment of the present invention, by carrying out correlation analysis to the user behaviors log of each object to be analyzed and each default feature, obtain the correlativity of each object and each feature, and determine the relationship strength type of these two objects under this feature according to the similarity of the correlativity of two objects and same feature, can analyze the relation between object for different features, and obtain relationship strength type, the object relationship obtained is more accurate, refinement, can effectively excavate potential data value, so that provide corresponding service for different relationship strength types, improve efficiency and the accuracy of service.
The electronic equipment of the embodiment of the present invention exists in a variety of forms, includes but not limited to:
(1) mobile communication equipment: the feature of this kind equipment possesses mobile communication function, and to provide speech, data communication for main target.This Terminal Type comprises: smart mobile phone (such as iPhone), multimedia handset, functional mobile phone, and low-end mobile phone etc.
(2) super mobile personal computer equipment: this kind equipment belongs to the category of personal computer, has calculating and processing capacity, generally also possesses mobile Internet access characteristic.This Terminal Type comprises: PDA, MID and UMPC equipment etc., such as iPad.
(3) portable entertainment device: this kind equipment can show and play multimedia content.This kind equipment comprises: audio frequency, video player (such as iPod), handheld device, e-book, and intelligent toy and portable car-mounted navigator.
(4) server: the equipment that calculation services is provided, the formation of server comprises processor, hard disk, internal memory, system bus etc., server and general computer architecture similar, but owing to needing to provide highly reliable service, therefore require higher in processing power, stability, reliability, security, extensibility, manageability etc.
(5) other have the electronic installation of data interaction function.
For achieving the above object, the present invention also proposes a kind of storage medium, and wherein, described storage medium is for storing application program, and described application program is used for operationally performing a kind of object relationship analytical approach of the present invention.
For achieving the above object, the present invention also proposes a kind of application program, and wherein, described application program is used for operationally performing a kind of object relationship analytical approach of the present invention.
In describing the invention, it will be appreciated that, term " " center ", " longitudinal direction ", " transverse direction ", " length ", " width ", " thickness ", " on ", D score, " front ", " afterwards ", " left side ", " right side ", " vertically ", " level ", " top ", " end " " interior ", " outward ", " clockwise ", " counterclockwise ", " axis ", " radial direction ", orientation or the position relationship of the instruction such as " circumference " are based on orientation shown in the drawings or position relationship, only the present invention for convenience of description and simplified characterization, instead of indicate or imply that the device of indication or element must have specific orientation, with specific azimuth configuration and operation, therefore limitation of the present invention can not be interpreted as.
In addition, term " first ", " second " only for describing object, and can not be interpreted as instruction or hint relative importance or imply the quantity indicating indicated technical characteristic.Thus, be limited with " first ", the feature of " second " can express or impliedly comprise at least one this feature.In describing the invention, the implication of " multiple " is two or more, such as two, three etc., unless otherwise expressly limited specifically.
In the present invention, unless otherwise clearly defined and limited, the term such as term " installation ", " being connected ", " connection ", " fixing " should be interpreted broadly, and such as, can be fixedly connected with, also can be removably connect, or integral; Can be mechanical connection, also can be electrical connection; Can be directly be connected, also indirectly can be connected by intermediary, can be the connection of two element internals or the interaction relationship of two elements, unless otherwise clear and definite restriction.For the ordinary skill in the art, above-mentioned term concrete meaning in the present invention can be understood as the case may be.
In the present invention, unless otherwise clearly defined and limited, fisrt feature second feature " on " or D score can be that the first and second features directly contact, or the first and second features are by intermediary indirect contact.And, fisrt feature second feature " on ", " top " and " above " but fisrt feature directly over second feature or oblique upper, or only represent that fisrt feature level height is higher than second feature.Fisrt feature second feature " under ", " below " and " below " can be fisrt feature immediately below second feature or tiltedly below, or only represent that fisrt feature level height is less than second feature.
In the description of this instructions, specific features, structure, material or feature that the description of reference term " embodiment ", " some embodiments ", " example ", " concrete example " or " some examples " etc. means to describe in conjunction with this embodiment or example are contained at least one embodiment of the present invention or example.In this manual, to the schematic representation of above-mentioned term not must for be identical embodiment or example.And the specific features of description, structure, material or feature can combine in one or more embodiment in office or example in an appropriate manner.In addition, when not conflicting, the feature of the different embodiment described in this instructions or example and different embodiment or example can carry out combining and combining by those skilled in the art.
Although illustrate and describe embodiments of the invention above, be understandable that, above-described embodiment is exemplary, can not be interpreted as limitation of the present invention, and those of ordinary skill in the art can change above-described embodiment within the scope of the invention, revises, replace and modification.
Claims (10)
1. an object relationship analytical approach, is characterized in that, comprising:
Obtain the user behaviors log of each object in object set to be analyzed;
Respectively correlation analysis is carried out, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set;
The relationship strength type of described every two objects under each feature is determined, to obtain the object relationship in described object set to be analyzed according to the correlation information of every two objects and each feature in described object set to be analyzed.
2. object relationship analytical approach as claimed in claim 1, it is characterized in that, described correlation information is weight information, determines the relationship strength type of described every two objects under each feature, comprising according to the correlation information of every two objects and each feature in described object set to be analyzed:
The relevance weight determining the first object and feature i in described every two objects is the first relevance weight, in described every two objects, the relevance weight of the second object and described feature i is the second relevance weight, wherein, i=1, N is positive integer, and N is the quantity of feature in described default behavioural characteristic set;
Calculate the similarity of described first relevance weight and the second relevance weight;
The relationship strength type of described every two objects under described feature i is determined according to described similarity.
3. object relationship analytical approach as claimed in claim 1, is characterized in that, also comprise:
Gather the user behaviors log of object in multiple regional extent;
Respectively feature extraction is carried out to the user behaviors log of object in each regional extent, to obtain subcharacter set corresponding to each regional extent;
Subcharacter set corresponding for described multiple regional extent is merged, to obtain described default behavioural characteristic set.
4. the object relationship analytical approach as described in claim 1-3, is characterized in that, also comprise:
According to the first predetermined period, described default behavioural characteristic set is upgraded;
Again obtain the correlation information of each object and each feature according to the default behavioural characteristic set after renewal, and redefine the relationship strength type of every two objects under each feature, to upgrade the object relationship in described object set to be analyzed.
5. object relationship analytical approach as claimed in claim 4, is characterized in that, also comprise:
Verify according to the accuracy of the second predetermined period to the object relationship in described object set to be analyzed.
6. the object relationship analytical approach as described in claim 1 or 5, is characterized in that, described checking according to the accuracy of the second predetermined period to the object relationship in described object set to be analyzed specifically comprises:
The first object set is randomly drawed in described object set to be analyzed;
Obtain the second object set according to the object relationship in described object set to be analyzed, wherein, described second object set is the set that the object being the first kind with the relationship strength type of described first object set under fisrt feature is formed;
Again the relationship strength type of two objects under described fisrt feature is obtained according to the user behaviors log of described first object set and the user behaviors log of described second object set;
If there is the relationship strength type of two objects under described fisrt feature again obtained not conform to the described first kind, then judge that the object relationship in described object set to be analyzed is accurate, otherwise judge that the object relationship in described object set to be analyzed is inaccurate.
7. object relationship analytical approach as claimed in claim 6, it is characterized in that, wherein, described default behavioural characteristic set comprises behavioural characteristic and application scenarios feature.
8. an object relationship analytical equipment, is characterized in that, comprising:
Acquisition module, for obtaining the user behaviors log of each object in object set to be analyzed;
Analysis module, for carrying out correlation analysis respectively, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set;
Determination module, for determining the relationship strength type of described every two objects under each feature according to the correlation information of every two objects and each feature in described object set to be analyzed, to obtain the object relationship in described object set to be analyzed.
9. object relationship analytical equipment as claimed in claim 8, it is characterized in that, described correlation information is weight information, described determination module specifically for:
The relevance weight determining the first object and feature i in described every two objects is the first relevance weight, in described every two objects, the relevance weight of the second object and described feature i is the second relevance weight, wherein, i=1, N is positive integer, and N is the quantity of feature in described default behavioural characteristic set;
Calculate the similarity of described first relevance weight and the second relevance weight;
The relationship strength type of described every two objects under described feature i is determined according to described similarity.
10. an electronic equipment, is characterized in that, comprising: processor, storer, communication interface and bus;
Described processor, described storer and described communication interface are connected by described bus and complete mutual communication;
Described storer stores executable programs code;
Described processor runs the program corresponding with described executable program code by reading the executable program code stored in described storer, for execution following steps:
Obtain the user behaviors log of each object in object set to be analyzed;
Respectively correlation analysis is carried out, to obtain the correlation information of each object and each feature to each feature in the user behaviors log of each object and default behavioural characteristic set;
The relationship strength type of described every two objects under each feature is determined, to obtain the object relationship in described object set to be analyzed according to the correlation information of every two objects and each feature in described object set to be analyzed.
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