CN108921413A - A kind of social networks degree of belief calculation method based on user intention - Google Patents
A kind of social networks degree of belief calculation method based on user intention Download PDFInfo
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Abstract
A kind of social networks degree of belief calculation method based on user intention including the establishment user's cluster successively carried out and chooses training set, interval number set is obtained according to training set, interval number distance based on attribute is calculated for any user to be determined, trust-factor is obtained according to interval number distance and is combined into and trusts evidence, carry out evidence fusion to all trusts evidences and calculates optimal weights collection, by interval number set and optimal weights collection composition user intention, social network-i i-platform foundation user intention progress degree of belief calculating.The present invention provides a kind of social networks degree of belief calculation method based on user intention, can embody personalization when user intention and user's progress trust decisions, to be conducive to provide more accurately tactful foundation for every application study of social networks.
Description
Technical field
The present invention relates to social networks technical field, specifically a kind of social networks degree of belief based on user intention
Calculation method.
Background technique
Social media (SocialMedia) is that occur under permeation effects of the internet to reality background growing day by day
New Media is a kind of new model that social interaction is carried out by ubiquitous media of communication.Especially smart phone etc. moves
The in-depth penetration of dynamic terminal and the rapid development of 4G/5G network infrastructure, so that " being created " anywhere or anytime with " propagation " more
Add convenience.The mobile network user for occupying netizen's sum 95.1% is forming rapidly " the wireless sensor of a human society
Network " acquires and shares with the digital content progress social circle for generating various formats, to express consciousness, emotion and experience.Society
Hand over media be exactly this fusion virtually with the essential carrier of the social ecosystem of informationization and tool of reality, but simultaneously,
Also causing the morals and legal issue to emerge one after another.The data issued in social media platform include numerous sensitivities
Personal information, they may be by the illegal Collection utilization of external entity to seek private interests." Hollywood Nude Picture Scandal ", LinkedIn and
The world-famous social network sites user data such as MySpace, which is disclosed, the various privacy violation events such as to be sold and rings alarm bell to us：
The various private datas and original digital content that people are actively or passively presented in social media, just suffering from data theft,
Serious puzzlement, the safety and trust problem of network social intercourse such as information fraud, privacy snooping and infringement of copyright unprecedentedly intensify, relationship
To happy and peace, social stability.
People decide whether to share in social activity personal resource depending on " trust " to other people, the intension of the trust
Belong to sociology and category of psychology, be an abstract cognitive process of psychology, by people to the subjectivity of other people social performances
Cognition, the social similitude to the in-mind anticipations of other people behaviors, both sides, contacts cohesion, certain sharing contents context with
And people influence many factors such as the personal preference of privacy, have ambiguity, dynamic and background correlation, it is difficult to quasi-
It is also difficult point that really quantization, which is the research hotspot in access control and secret protection field,.For this purpose, many scholars propose that degree of belief calculates
This concept, social network-i i-platform can help social user carrying out individual digital content share according to degree of belief calculated result
When, correct decision is made, to carry out effective access control, uncertain bring risk is avoided, promotes user more
It is actively participating in normal social activity.In addition to this, degree of belief is also frequently applied to social recommendation system, makes user
More accurately obtain the hot spot message and user's focus in social networks.
In the prior art, degree of belief calculating can substantially be classified as the calculating of the degree of belief based on customer relationship, base between user
Three categories are calculated in the degree of belief that the degree of belief of user behavior is calculated and recommended based on trust chain.But these methods all have one
Fixed shortcoming is mainly reflected in the subjectivity and personalization for having ignored trust.It is general in current degree of belief calculating process
Time way be by the user social contact attribute sample data unified Modeling of all pending trust evaluations, unified calculation, do not excavate
Privacy and preference of the user in trust decisions is analyzed, the subjectivity and individual difference of users to trust impression, degree of belief can not be embodied
It is larger to calculate error.
Summary of the invention
In order to solve deficiency in the prior art, the present invention is provided based on a kind of social networks degree of belief by user intention
Calculation method can embody personalization preferences when user carries out trust decisions, to be conducive to be based in field of social network
Every application of trust evaluation provides more accurately tactful foundation.
To achieve the goals above, the concrete scheme that the present invention uses for：A kind of social networks letter based on user intention
Appoint degree calculation method, including training stage and application stage；
The training stage includes the following steps：
S1.1, several users' composition user's clusters with interdependence are chosen, each user has several attributes, often
A attribute corresponds to an attribute value v；
S1.2, selected part user forms training set in proportion from user's cluster；
S1.3, for any one attribute f, user u will be removed in training setiOuter all users are divided into set1And set2Two groups, wherein
set1In use per family by uiTrust, set2In use per family not by uiTrust；
S1.4, according to the attribute value v sequence from small to large of attribute f respectively to set1And set2Interior user is ranked up, and obtains
To two interval number sco1And sco2, sco1=[min (set1),max(set1)], sco2=[min (set2),max(set2)]；
S1.5, according to sco1And sco2Generate sco3,
Obtain uiFor the section manifold of attribute f
Close scof=< sco1,sco2,sco3>；
S1.6, for any user u to be determinedjAttribute f, generate interval number scov=[v, v], calculates separately scovWith sco1、
sco2And sco3Distance；
S1.7, three distances obtained based on S1.1, obtain three trust-factors based on attribute f；
S1.8, three trust-factors are combined into the trust evidence based on attribute f；
S1.9, evidence fusion is carried out to the trust evidence of all properties, and calculates each evidence most using gradient descent algorithm
Excellent weightObtain optimal weights collection
S1.10, by optimal weights collectionWith interval number set scof=< sco1,sco2,sco3> forms user
Wish ξ；The application stage includes：
S2, social network-i i-platform carry out degree of belief calculating according to its user intention ξ.
In S1.6, sco is calculatedvWith sco1、sco2And sco3The specific method of distance be：
Wherein a and b is interval number, a respectively1It is the lower limit of interval number a, a2It is the upper limit of interval number a, b1It is under interval number b
Limit, b2It is the upper limit of interval number b, k is proportionality coefficient, and has k=1.
In S1.7, the calculation method of three trust-factors is：
Wherein indicate mf(Trust) trust possible degree, mf(Distrust) distrust degree, m are indicatedf(Ambiguity) it indicates
Uncertainty degree.
In S1.8, the trust evidence of attribute f is expressed as：
mf:{mf(Trust),mf(Distrust),mf(Ambiguity)}。
Beneficial effect：The present invention is by investigating user intention, so that obtaining user is carrying out trust decisions when institute
The user property focused particularly on, and then the individual demand of users to trust decisions is embodied, preferably embody the subjectivity trusted and calculated
Property, it is the application studies based on users to trust such as access control mechanisms, method for secret protection, the content recommendation system of social networks
More accurately tactful foundation is provided.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, Fig. 1 is flow chart of the invention.
A kind of social networks degree of belief calculation method based on user intention, including training stage and application stage.
Training stage includes S1.1 to S1.10.
S1.1, several users' composition user's clusters with interdependence are chosen, each user is with several categories
Property, each attribute corresponds to an attribute value v.Interdependence in user's cluster refers to that there are certain passes between user
It is, such as to unifying the concern of things, delivering evaluation to same thing or having concern relation etc. from each other, herein basis
On, a perfect social networks can be constituted.
S1.2, selected part user forms training set in proportion from user's cluster, and ratio of choosing can be 80%, remaining
User as user to be determined.
S1.3, for any one attribute f, user u will be removed in training setiOuter all users are divided into set1And set2Two groups,
Wherein set1In use per family by uiTrust, set2In use per family not by uiTrust.
S1.4, according to the attribute value v sequence from small to large of attribute f respectively to set1And set2Interior user is ranked up,
Obtain two interval number sco1And sco2, sco1=[min (set1),max(set1)], sco2=[min (set2),max
(set2)]。
S1.5, according to sco1And sco2Generate sco3,
, obtain uiFor the interval number set sco of attribute ff=< sco1,sco2,sco3>.
The meaning of determination section number is to measure each user when making trust decisions, to the sensitivity of attribute f.If active user uiWhen making trust decisions
It is concerned about very much attribute f, then by user uiThe trust user of differentiation and the f attribute of distrust user will have very big difference.Instead
It, if user uiWhen making trust decisions, it is indifferent to the numerical value of attribute f, then either by uiThe user of trust still not by
uiThe user of trust, the numeric distribution of f attribute are all random.For any one user u to be calculatedj, can be belonged to
Property f numerical value v be based on interval number sco1,sco2,sco3It measures.If numerical stability, which is distributed in, trusts section sco1In, then generation
Table active user is likely to trust user ujIf same numerical score is distrusting section sco2In, then represent active user very
It may be because user ujF attribute it is lower, to ujMistrustful decision is generated, and if the numerical value is in indeterminacy section sco3
In, then it represents active user and is indifferent to attribute f when making trust decisions.
S1.6, for any user u to be determinedjAttribute f, generate interval number scov=[v, v], v refers to user u herej
Attribute f corresponding to attribute value, calculate separately scovWith sco1、sco2And sco3Distance.Specifically calculation method is：
Wherein a and b is interval number, a respectively1It is the lower limit of interval number a, a2It is the upper limit of interval number a, b1It is under interval number b
Limit, b2It is the upper limit of interval number b, k is proportionality coefficient, and has k=1.
S1.7, three distances obtained based on S1.1, obtain three trust-factors based on attribute f, three trust-factors
Calculation method be：
Wherein indicate mf(Trust) trust possible degree, mf(Distrust) distrust degree, m are indicatedf(Ambiguity) it indicates
Uncertainty degree.
S1.8, three trust-factors are combined into the trust evidence based on attribute f, the trust evidence based on attribute f indicates
For：mf:{mf(Trust),mf(Distrust),mf(Ambiguity)}。
For the set F of whole attributes composition, by u all in training setiThe trust decisions made are as label, and by F
In attribute be converted into evidence, can obtain the trust decisions accuracy rate g (F) based on attribute F, and g (F) is made to obtain extreme value
Optimal solutionIt is exactly the weight portion of user intention ξ.
S1.9, evidence fusion is carried out to the trust evidence of all properties according to the evidence fusion method of D-S evidence theory, and
The optimal weights of each evidence are calculated using gradient descent algorithmObtain optimal weights collectionIn order to find
Optimal solutionIt needs to find from all possible user property to the best attribute set of active user's decision and each
The weight of attribute.Assuming that have the possible attribute of k kind, if traversing each attribute to obtain optimal decision attribute set,
Complexity is higher.In order to optimize calculating process, the trust focus for being best suitable for active user is obtained using gradient descent algorithm.
S1.10, by optimal weights collectionWith interval number set scof=< sco1,sco2,sco3> composition
User intention ξ.
Application stage includes S2.
S2, social network-i i-platform according to its user intention ξ carry out degree of belief calculating, i.e., social network-i i-platform calculate user it
Between degree of belief, calculating process can carry out by the server of social network-i i-platform.
The present invention is by investigating user intention, to obtain what user was focused particularly on when carrying out trust decisions
User property, and then the individual demand of users to trust decisions is embodied, the subjectivity trusted and calculated preferably is embodied, is social network
Access control mechanisms, method for secret protection, content recommendation system of network etc. are provided more acurrate based on the application study of users to trust
Tactful foundation.
It should also be noted that, herein, relational terms such as first and second and the like are used merely to one
Entity or operation are distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation
There are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant are intended to contain
Lid non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (4)
1. a kind of social networks degree of belief calculation method based on user intention, it is characterised in that：Including training stage and application
Stage；
The training stage includes the following steps：
S1.1, several users' composition user's clusters with interdependence are chosen, each user has several attributes, often
A attribute corresponds to an attribute value v；
S1.2, selected part user forms training set in proportion from user's cluster；
S1.3, for any one attribute f, user u will be removed in training setiOuter all users are divided into set1And set2Two groups, wherein
set1In use per family by uiTrust, set2In use per family not by uiTrust；
S1.4, according to the attribute value v sequence from small to large of attribute f respectively to set1And set2Interior user is ranked up, and obtains
Two interval number sco1And sco2, sco1=[min (set1),max(set1)], sco2=[min (set2),max(set2)]；
S1.5, according to sco1And sco2Generate sco3,
Obtain uiFor the section manifold of attribute f
Close scof=< sco1,sco2,sco3>；
S1.6, for any user u to be determinedjAttribute f, generate interval number scov=[v, v], calculates separately scovWith sco1、
sco2And sco3Distance；
S1.7, three distances obtained based on S1.6, obtain three trust-factors based on attribute f；
S1.8, three trust-factors are combined into the trust evidence based on attribute f；
S1.9, evidence fusion is carried out to the trust evidence of all properties, and calculates each evidence most using gradient descent algorithm
Excellent weightObtain optimal weights collection
S1.10, by optimal weights collectionWith interval number set scof=< sco1,sco2,sco3> forms user
Wish ξ；
The application stage includes：
S2, social network-i i-platform carry out degree of belief calculating according to its user intention ξ.
2. a kind of social networks degree of belief calculation method based on user intention as described in claim 1, it is characterised in that：
In S1.6, sco is calculatedvWith sco1、sco2And sco3The specific method of distance be：
Wherein a and b is interval number, a respectively1It is the lower limit of interval number a, a2It is the upper limit of interval number a, b1It is under interval number b
Limit, b2It is the upper limit of interval number b, k is proportionality coefficient, and has k=1.
3. a kind of social networks degree of belief calculation method based on user intention as claimed in claim 2, it is characterised in that：
In S1.7, the calculation method of three trust-factors is：
Wherein mf(Trust) trusting degree, m are indicatedf(Distrust) distrust degree, m are indicatedf(Ambiguity) indicate not true
Determine degree.
4. a kind of social networks degree of belief calculation method based on user intention as claimed in claim 3, it is characterised in that：
In S1.8, the trust evidence of attribute f is expressed as：mf:{mf(Trust),mf(Distrust),mf(Ambiguity)}。
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