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T C A R T S B
A
d n a m e d d e z il a n o s r e p e h
T of usersin socia lnetworksis an importan tfactor o f r
e s u e h
t saitsfaciton . Integrated user microblog conten t and user interaciton n
o it a m r o f n
i ,introduce the concep tof interes tsimliartiy and interaciton trust. This m h ti r o g l a n o it a d n e m m o c e r g n i r e tl i f e v it a r o b a ll o c d e v o r p m i n a s e s o p o r p r e p a
p .
Compuitng user interes tsimliartiy and user interaciton trust ,linear ftiitng of the 2 .
y ti r a li m i s b u
s I tcan calculate the tota lsimliartiy between the users fo micro-blog r
d e z il a n o s r e
p ecommendaiton . The experimenta l resul t shows tha t the new f o n o it c a f s it a s r e s u d n a y ti l a u q , y c a r u c c a e h t e v o r p m i y l e v it c e f f e n a c m h ti r o g l a
. s k r o w t e n l a i c o s n i m e t s y s n o it a d n e m m o c e r
N O I T C U D O R T N I
, r e tt i w T ( a i d e m l a i c o
S Microblog , c.et ) is especially rapid development ,has d i p a r e h t h ti w . a i d e m t e n r e t n I g n i g r e m e e h t n i e c i v r e s t n a t r o p m i f o d n i k a e m o c e b
o r c i m f o e s a e r c n
i -blog, to query the mos trelevant topic in the mass of conten tis t
s y s n o it a d n e m m o c e r e h t d n a , tl u c i f f i d y r e v e m o c e
b em can recommend the mos t
d e t s e r e t n
i ininformaiton,t oenhancet heusersaitsfaciton.
r e h t , k r o w t e n l a i c o s g n i g g o l b o r c i m e h t n i , t n e s e r p t
A e are three main
s y a w d n e m m o c e
r :First ,according to the registered informaiton ,choose a higher c
e r e e r g e d g n i h c t a
m ommended ,butt hisapproacht endst oproduceal o tofresutlst o
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
L n u
D i,Xiaop iZheng ,Xingijn Zhang ,Zhiyun Zheng ,Schoo lo fInformaiton Engineeirng , ,
1 0 0 0 5 4 u o h z g n e h Z , y ti s r e v i n U u o h z g n e h
e l b a n u r e s u e h t o t d a e l , n r u t e
r tochoose;t hesecondi sbasedont hepopulartiyoft he t o h a d n e m m o c e r , c i p o
t microbloggingtopic ,bu tahott opicforal lmicro-blogusers , e c n e u l f n i e h t n o d e s a b s i d r i h t e h t ;t s e r e t n i s' r e s u h c a e f o s d e e n e h t o t d r a g e r t u o h ti w e m o s d n e m m o c e r , s r e s u f
o high populartiyusers ,these popular friends is through " s n a f " f o r e b m u n e h
t ,transmission raito, the leve lof acitvtiy to calculate ,Users t c e p x e t o n o d s d n e i r f d e d n e m m o c e r f o r e b m u n e g r a l a e v i e c e
r .Therefore ,the
o r c i m g n it s i x
e -blog recommendedapproachhascertain ilmtiaitons ,howto digou t r e s u e h t f o s t n e t n o c e h
t interes tfromthemassivemicro-blogdataandpersonailzed b s a h n o it a d n e m m o c e
r ecomeaho tspo tformicro-blogrecommendaiton. u n o d e s u c o f s d o h t e m d e d n e m m o c e r g n i g g o l b o r c i m l a n o it n e v n o
C ser interest ,
, s i s y l a n a s e r u t a e f d n a t n e t n o c g n i g g o l b o r c i
m ignoret hepotenita lilnkbetweensocia l o r c i m d e z il a n o s r e p f o e l o r e h t n i s r e s u k r o w t e
n -blog recommendaiton .In order to n o it c e n n o c f o d n i k s i h t y l p p
a reasonablyt othepersonailzedrecommendaiton ,Int he t s u r t f o e e r g e d e h t g n i r e d i s n o c e W , r e p a
p a ndtheuseri nteracitontrust ,ani mproved t s e r e t n i r e s u d e c u d o r t n i h c i h w , m h ti r o g l a n o it a d n e m m o c e r g n i r e tl i f e v it a r o b a ll o c e t a i v e ll a , t s u r t n o it c a r e t n i r e s u d n a y ti r a li m i
s data sparseness problem ,integrates . s n o it a d n e m m o c e r d e z il a n o s r e p n o it a m r o f n i n o it c a r e t n i d n a t n e t n o c g n i g g o l b o r c i m a d e z i n a g r o s i r e p a p e h t f o r e d n i a m e r e h
T s follows .Seciton 2 discusses g n i g g o l b o r c i m e h t s t n e s e r p 3 n o it c e S , k r o w d e t a l e r d e d n e m m o c e r g n i g g o l b o r c i m s l l a r e v o d e d n e m m o c e
r tructureandthedegree ofuserinteres tandtrus tcalculaiton f f e e h t d e i f i r e v t e s a t a d l a e r y b d n a s t e s a t a d e h t s e b i r c s e d 4 n o it c e S , p e t
s ec tof
d n e m m o c e r g n i g g o l b o r c i
m ,Finally ,wedrawt heconclusioni nSeciton5.
S K R O W D E T A L E R a w ] 1 [ m e t s y s d e d n e m m o c e
R s originally used in the field of e-commerce , n o it a d n e m m o c e r d e z il a n o s r e p , . c t e , s t s e r e t n i , s d e e n n o it a m r o f n i ' s r e s u o t g n i d r o c c a . r e s u e h t o t n o it a m r o f n i r e h t o d n a s t c u d o r p , n o it a m r o f n i d e t s e r e t n i l li w m e t s y s d e d n e m m o c e r e h t s i e r o c m e t s y s n o it a d n e m m o c e
R algortihm , the main
f o n o it a d n e m m o c e r e h t g n i r e tl i f e v it a r o b a ll o c : e d u l c n i s m h ti r o g l a n o it a d n e m m o c e r , s e l u r n o it a i c o s s a e h t n o d e s a b f o n o it a d n e m m o c e r e h t , t n e t n o c e h t n o d e s a b e v it a r o b a ll o c , m e h t g n o m A . c t e , s e i g o l o n h c e t g n i r e t s u l c d e s a b n o it a d n e m m o c e r i
f tlering algortihm [2] depend on tiems' score matrix of the targe tuser or simliar s i h t t u B . e u q i n h c e t r a l u p o p t s o m e h t s i , d n e m m o c e r o t s r e s u t e g r a t e h t f o s r e s u s s a l
c ic recommendaiton algortihm canno t be direclty used for user friends d e d n e m m o c e
r in socia lnetwork .In addiiton ,due to the data sparstiy of the socia l . l a e d i t o n s i g n i r e tl i f e v it a r o b a ll o c g n it s i x e f o s s e n e v it c e f f e e h t , k r o w t e n n i s m e t s y s d e d n e m m o c e r g n i g g o l b o r c i m , g n i g g o l b o r c i m f o e s u y l e d i w e h t h ti W t a e r g d e s u o r a s a h h c r a e s e r c i m e d a c a e h
t concern . Proposed learning to rank [
l e d o
m 3] ,selec tthe length of tweets ,qualtiy of the tweets ,influence of user three s t u p t u o h c i h w s t e e w t e h t k n a r o t d o h t e m a e s o p o r p r e p a p s i h T . r e tt i w T f o s e r u t a e f r i e h t n o d e s a b s t e e w t d e h c t a m e h
e h t r e d i s n o
c individua lneedsofusers .Chene tal[4] ,proposedamethodofmaking e d o m g n i k n a R e v it a r o b a ll o C n o d e s a b s n o it a d n e m m o c e r d e z il a n o s r e p t e e w
t l .
, s t e e w t f o s r o t c a f l e v e l c i p o t , s r e s u n e e w t e b s n o it a l e r d n a t n e t n o c r e tt i w t d e t a r g e t n I s r e s u e d i v o r p o t , k r o w t e n l a i c o s e h t n i e c n e u l f n i r e s u d n a s e r u t a e f t n e t n o c r e tt i w t r e s u d e z il it u y ll u f t o n d o h t e m e h T . tl u s e r s n o it a d n e m m o c e r d e z il a n o s r e p h ti w r e t n
i aciton informaiton , i t is recommended no t ideal . collaboraitve fitlering . d e c u d o r t n i s i y ti r a li m i s r e s u f o t p e c n o c e h t n o d e s a b m h ti r o g l a n o it a d n e m m o c e
r [5 ,]
y ti r a li m i s e v it c a r e t n i r e s u d n a y ti r a li m i s e t u b i r tt a r e s u e h t e t a l u c l a
C and ftiitng the
o s d n i k o w
t fsimliartiytogivetheulitmatesimliartiyoftheuser, recommendtarge t o r c i m n o d e s a b y l n i a m s i m h ti r o g l a e h t , s r e s u s t s e r e t n i r a li m i s h ti w s r e s
u -blog
e h T . h g u o n e e s i c e r p t o n s i y c a r u c c a n o it a d n e m m o c e r d n a y ti r a li m i s t n e t n o c r e s u d e s o p o r
p -based clustering algortihm[6] heterogeneous socia l network t n e t n o c , n o it c a r t x e e r u t a e f t n e t n o c g n i g g o l b o r c i m , n o it a d n e m m o c e
r -basedsimliartiy
s d n e i r f t n a v e l e r r o f s tl u s e r g n i r e t s u l c l a n i f e h t , p u o r g r e s u e r it n e e h t o t g n i r e t s u l c . d e d n e m m o c e
r To some extent ,the algorithm reduces the impac tof the matrix n o it a d n e m m o c e r f o y ti l a u q e h t n o s m e l b o r p t r a t s d l o c d n a y ti s r a p
s ,bu tthe users'
d e d n e m m o c e r r e w o l a t e s , g n i g n a h c y lt n a t s n o c e r a s t s e r e t n i d n a s e c n e r e f e r p , e g a r e v o
c Suchalgortihmshaveno tmadeaneffecitvewayt osolvet hisproblem. o r c i m g n it s i x
E -blog recommendaiton algortihm has achieved good resutls ,Bu t o r c i m f o e v it c e p s r e p e h t m o r f t r a t s y l n o m e h t f o t s o
m -blogconten toruserinterest , e
li h
w ignore the importanceof trus trelaitonship among therecommended objects . [7] proposed a nove lsocia lrecommended method and incorporate tiem relaitons , t n e t x e e m o s o t , r e s u t e g r a t e h t s a d e d n e m m o c e r x i r t a m p i h s n o it a l e r g n i s u g n it r a t s e s u l l u f e k a m t o n d i d t u b , m e l b o r p l a e d i t o n s i y c a r u c c a d e d n e m m o c e r e h t e t a i v e ll a c i m f
o roblogging users a variety of informaiton ,sparse matrix falied to solve the n e e w t e b p i h s n o it a l e r t s u r t d e d n e m m o c e r e h t m o r f e l c it r a s i h t , e r o f e r e h T . m e l b o r p o r c i m f o e c n e u l f n i e h t y d u t s o t , w e i v f o t n i o p t s e r e t n i r e s u d n a s t c e j b
o -blog
. s tl u s e r d e d n e m m o c e r R E T L I F E V I T A R O B A L L O
C INGALGORITHMBASEDONI NTEREST T S U R T D N A f o r e b m u n e h t , m e t s y s n o it a d n e m m o c e r d e z il a n o s r e p g n i g g o l b o r c i M s i s r e s u f o r e b m u n e h T . d e t a d p u y lt n e u q e r f o s l a t u b , e v i s s a m s i g n i g g o l b o r c i m r e s u , e l b a t s y l e v it a l e
r -based collaboraitvefitleringrecommendaiton ,relaitvelyeasy [ , r e p a p s i h t n I . s r e s u n e e w t e b y ti r a li m i s e t a l u c l a c o
t 8-9] analyze the users'
e t a d i d n a c t n a v e l e r t s o m e h t d n e m m o c e r s r e s u r o f n o it a t n e i r o s t s e r e t n i d e z il a n o s r e p g n it a r r e s u t i c il p x e o n s i e r e h t e s u a c e B . r e w o ll o
f data eb tweenmicro-blogplatform , o r c i m f o e s u e h
t -blogconten tand user interaciton informaiton ,the users interested r e s u t c u r t s n o c o t x i r t a m f o a t a d n o it a z il a it i n i s a e e r g e
d - topic feature matrix ,the
t a m a t a d n o it a z il a it i n i s a s n o it c a r e t n i r e s u a t a d t i c il p m
i rix ,buliduser- socia lfeature o r c i m n o d e s a b s r e s u n e e w t e b n o it a l e r r o c e h T , x i r t a
m -blog conten tand socia l
l a n a s i p i h s n o it a l e
s r e s u t e g r a t e h t t e g n e h t d n a , s r e s u f o y ti r a li m i s e v i s n e h e r p m o
c of neighbor set .
p o T e h t o t g n i d r o c c
A - Nsoritngalgortihmsproducerecommendaitonsfort heuser.
o r c i
M -blogU rs Ie nterest Calculaiton
r e s u e h t f o n o it c e l f e r e k il , s t n e m m o c , g n i d r a w r o f , k r o w t e n l a i c o s a n
I interest .
a f o n g i s e d e h t , n o it a m r o f n i s i h t h ti
W reasonableuseri nteres tmodelt ogett het opic o
r c i m f o y r o g e t a
c -blogusers,t ofurtheri nfert heusers'i nterestst endencies. 1
[ l e d o m c i p o
T 0] LDA as a kind of unsupervised tex t topic generaiton o
t d e s u e b n a c , d e i d u t s y l e d i w n e e b s a h l e d o m y ti li b a b o r
p idenitfylarge-scalese tof
1 [ n o it a m r o f n i c i p o t g n i y l r e d n u n i s u p r o c r o n o it c e ll o c t n e m u c o
d 1-12] .Int hispaper ,
e h t r e f n i d n a , n o it u b i r t s i d l a it n e t o p r e s u g n i n i m o t l e d o m c i p o t A D L d e v o r p m i e w
o r c i m f o n o it u b i r t s i d e h t h g u o r h t n o it a t n e i r o t s e r e t n i s' r e s
u -blogs'topic .In order to
e h t d n i f y l e t a r u c c
a interes ttopic of users ,this paper constructs microblogging o
r c i m d e t s e r e t n i l l a o t g n i d r o c c a t n e m u c o
d -blogs ,a user corresponding to the I
m e t s y s l a c i x e l d r o w e s e n i h C e h t e s u n e h t d n a , t n e m u c o d a f o s t n e t n o
c CTCLAS
e h t t e g o t ) m e t s y S s i s y l a n A l a c i x e L e s e n i h C , y g o l o n h c e T g n it u p m o C f o e t u ti t s n I (
e r o m s i r e s u e h t t a h t s n a e m t i , r e h g i h s i d r o w y e k f o y c n e u q e r f e h t , s d r o w y e k
e s n e s n o n , s d r o w p o t s , s n o c it o m e g n i g g o l b o r c i m e v o m e R . m r e t e h t n i d e t s e r e t n i
A D L e s u , s d r o
w topic mode lmining topic of each document ,ge tthe user-topic t e s s c i p o t , c i p o t f o n o it c e ll o c T t e s e r p a s t n e s e r p e r C t e L . x i r t a m
=
C � �C ,C� � …� C� � ,ti samicro-blog ,P� � �C |t indicatest hatt hemicro-blog t c
i p o t i C f o y ti li b a b o r p r o i r e t s o p e h t o t g n o l e
b ,The greater the value of P� �C |t) , e
h t t a h t s e t a c i d n
i possiblitiyofmicro-blog tbelongt ot het hemeofCii shigher. t
d e t s o p r e s u a e s o p p u
S he number of microblogging is d , make
� � � � �t t …� t� � represents microblogging colleciton of certain users ,the topic T
a e l b a li a v a s' r e s u e h t f o r o t c e v e r u t a e
f -dimensiona l vector
=
Z � � � � �v v …� vT� isdescribed ,asfollows:
v� = ( 1)
o r c i m y n a d e t s o p t o n s a h r e s u e h t n e h
W -blog ,the feature vectoriszero vector . .i
C c i p o t e h t n i d e t s e r e t n i s i r e s u e h t e r o m e h t ,i v t n e n o p m o c e h t r e h g i h e h T
s e r e t n I . 1 n o it i n if e
D tdegre e between th euse .r Le tthe se tof the user m
i S e n i s o c , } n u , … , 2 u , 1 u { =
U Ibetween two vectors defines as the degree of w
t e b y ti r a li m i s t s e r e t n
i eentheuseru iandthe userum. Theformulas arepresented .
s w o ll o f s a
m i
S I= = ( 2)
s t n e s e r p e r m Z , i Z , s r e s u o w t n e e w t e b y ti r a li m i s t s e r e t n i d e s s e r p x e I m i S e r e h W
c i
M or -blogU rs Te rus t Calculaiton
o l b o r c i
M ggingi nteracitoni nformaiton ,includingcomments ,forwarded ,ilkeand l a i c o s e h t f o h t g n e r t s e h t s t c e l f e r y lt c e r i d r o i v a h e b n o it c a r e t n I . s n o it c a r e h t o
e r o m e h t , s r e s u n e e w t e b t s u r t f o e e r g e d e h t , s i t a h t , s r e s u n e e w t e b s p i h s n o it a l e r
e r
f quenti nteracitonshowst hatt hemorei nitmaterelaitonship .
e e r g e d t s u r t e h t e z y l a n a o t s e r u t a e f l a i c o s f o s d n i k r u o f r e d i s n o c e w , r e p a p s i h t n I
. s p i h s n o it a l e r l a i c o s n o d e s a b
) 1
( Thedegreeofconcernoft heuser .Theformulasarepresentedasfollows.
Wf(uo)= ( 3)
f o r e b m u n e h t d n a s r e w o ll o f f o r e b m u n e h t t a h t o it a r e h t s e t o n e d ) o u ( f W e r e h W
, o u r e s u f o s e e w o ll o
f followers (uo) represents the number of fans of user uo , .
r e h s il b u p t s e r e t n i s r e s u f o r e b m u n e h t s t n e s e r p e r ) o u ( s e e w o ll o f
2( ) The number of users uo interes tin the followers of the user ut .The .
s w o ll o f s a d e t n e s e r p e r a s a l u m r o f
Wo(uo)=followees(uo) followers(ut |) ( 4)
b e h t , e r e H . m s i n a h c e m d a e r p s t s e r e t n i e h t s i a e d i c i s a b e h t ) 4 ( n o it a u q
E asic
s i e l p o e p g n o m a r o i v a h e b n r e c n o c e h t t a h t n o it p m u s s a e h t s i m s i n a h c e m d a e r p s
e h t w o ll o f a r e s u e h t f I . d a e r p s n a c e r u t a n n i t s e r e t n i s i h t d n a , t s e r e t n i e h t n o d e s a b
b r e s u e h t n e h t , c r e s u w o ll o f b r e s u e h t f i ; b r e s u n i d e t s e r e t n i a r e s u t a h t b r e s
u plays
e d u l c n o c o t e l b a d n a , c r e s u d n a a r e s u n e e w t e b r o t a c i n u m m o c t s e r e t n i f o e l o r e h t
e h t s a h c u s t s e r e t n i f o r e b m u n e h t e r o m e h t , s r e s u o w t r o F . c r e s u n i d e t s e r e t n i a r e s u
. c r e s u r o f a r e s u e h t d e t s e r e t n i e r o m e h t n e h t , s r o t a c i n u m m o c b r e s u
u n e h T ) 3
( mberofcommonfolloweeofuseru tanduseruo ,calculatedas(5)
Wt(uo)=followees(ut) followees(uo) | ( 5)
s e e w o ll o f , r e h s il b u p n i t s e r e t n i o u f o r e b m u n e h t s e t o n e d ) o u ( s e e w o ll o f e r e h W
e r e t n i t u r e b m u n e h t s e t o n e d ) t u
( s tinpubilsher .Obviously ,thenumberofcommon .
p i h s n o it a l e r e t a m it n i e r o m e h t , o u r e s u e h t d n a o u r e s u e h t f o s d n e i r f
e h t r e h g i h e h t , n r e c n o c f o e e r g e d e h t r e h g i h e h t , r e s u e h t f o e c n e u l f n i e h T ) 4 (
, d e d r a w r o f s a w g n i g g o l b o r c i
m comments ,ilke .Asformula )( 6
u ( c
W o)=CountR(uo)+CountC(uo)+CountL(uo) ( 6)
h t s e t o n e d ) o u ( R t n u o C e r e h
W e number ofcommentsof user uo ,CountC(uo) ) o u ( L t n u o C , g n i g g o l b o r c i m o u r e s u d e d r a w r o f y b f o r e b m u n e h t t a h t s e t a c i d n i
. e k il y b o u r e s u e h t f o r e b m u n e h t s e t o n e d
o it i n if e
D n 2. Interacitve trus tbetween users Le tthe user se tU = {u1 ,u2 ,... , t
u r e s u n o d e s a b t s u r t e v it c a r e t n i o u r e s U } n
m i
S R(uo)=wfWf(uo) w+ oWo(uo) w+ tWt(uo) 1+ -w( f-wo-wt)Wc(uo) ( 7)
n r e c n o c f o e e r g e d e h t s e t a c i d n i ) o u ( f W e r e h
W ,Wo(uo)representsthenumber o u r e s u t u r e s u e h t t a h t s n a e m ) o u ( t W , t s e r e t n i f o t c e j b o o u r e s u n a f t u s r e s u f o
f o e c n e u l f n i e h t s t n e s e r p e r ) o u ( c W , s d n e i r f f o r e b m u n n r e c n o c n o m m o
c featuresof
. r e s u e h t
e h t d n a a r e s u n e e w t e b s d n e i r f w o ll o f n o m m o c n e t e r a e r e h t f
I userb ,bu tonlya
, c r e s u d n a a r e s u n e e w t e b d n e i r f w o ll o f n o m m o
c obviously ,user a is more ilkely b
r e s
u than user c. Visible ,The number of common interests between users can .
s r e s u n e e w t e b n o it a l e r r o c t s e r e t n i y f it n a u q
o w , f w s t h g i e w e h t ) 7 ( n o it a u q
E and wr direclty affec tthe recommendaiton m
it s e y l e t a r u c c a e r o m o t r e d r o n I . n o it a l u c l a c e r o c
s atethevaluesofwf ,woandwr , e l b a i r a v t n e d n e p e d e h t f o n o i s s e r g e r r a e n il , l e d o m n o i s s e r g e r r a e n il a s a ) 7 ( a l u m r o f
a s e l b a i r a v t n e d n e p e d n i r u o f , ) o u ( R m i S =
y rex1=Wf(uo) ,x2=Wo(uo) ,x3=w t e
r e w s t n e i c i f f e o c g n i d n o p s e r r o c t n e m u g r a e h t , ) o u ( c W = 4 x d n a ) o u
( = wf , =
, o
w = w tand = - f1w - ow -wt .Suppose there are n sets of observaitons can be d
e t n e s e r p e
r by the formula (7) to derive an expression for the ilnear regression :
) 8 ( n o it a u q e
( 8)
s s o l g n i c u d o r t n i d n a l e d o m n o i s s e r g e r r a e n il e h t o t n i s n o it a v r e s b o f o t e s h c a E
o c e u l a v m u m i n i m g n it t e g n o it c n u f s s o l e h t g n i v l o s n e h w , ) 9 ( n o it c n u
f rresponding
e h t o
t � � dan 4 .
y ( = ) ) 4 x , 3 x , 2 x , 1 x ( f , y (
L -f(x1,x2,x3,x4))2 ( 9)
d e t a r g e t n
I Interes tandTrus tRecommendaitonAlgortihm
e f f a s n o it a d n e m m o c e r r e s u , s e c i v r e s g n i k r o w t e n l a i c o s g n i g g o l b o r c i m r o
F c t
r e s u d n a n o it a m r o f n i t n e t n o c s' r e s u e h t : n o it a m r o f n i f o s e p y t o w t y l n i a m e r a s tl u s e r
f o y ti r a li m i s e h t n e v i g e r a s r o t c a f o w t e s e h t r e d i s n o C . n o it a m r o f n i n o it c a r e t n i
: s w o ll o f s a ) 0 1 ( n o it a u q e n i l e d o m n o it a l u c l a c r e s u e h t n e e w t e b s t s e r e t n i
= m i
S (10)
m i S e h t e r e h
W indicates users comprehensive simliartiy ,integrated simliartiy t
n e t n o c : s t r a p o w t f o s t s i s n o
c -based impac tfactorSim Iandtheimpac tfactor SimR s
a b ) o u
( edonsocia lrelaitonships,t heweigh toft hei mpac tfactori s .Togeneratea d
e d n e m m o c e r f o t e s d o o
g , the algortihms should be combined wtih the rea l .
m h ti r o g l a e h t , n o i s s u c s i d e v o b a e h t m o r
F I&R_CF (Interes tAnd Relaitonship e
v it a r o b a ll o
C -Fitlering)gives the procedureofgeneraitngtherecommendedse tfor .
o u r e s u e h t
) N t n i , U , o u ( F C _ R & I 1 m h ti r o g l A
. N r e b m u n d e d n e m m o c e r e t a r e n e g , U t e s r e s u e t a n r e tl a , o u r e s U : t u p n I
o c e r o u s r e s U : t u p t u
O mmendaitonse tC. n
i g e B
� 1� Simliartiycalculaiton 1
( -1)Theuseri nteres tcalculaiton.
LDAtopic mode lminingtopicofeachdocumen taccordingtothemicro-blog r
e s u e h t t e g , t e
s -topicmatrix .Wecalculatetheuserfeaturevectoraccordingtouser e
r e t n
i s tsimliartiySim.I 1
( -2)Userst rus tcalculaiton.
① esitmated tha tweights wf ,wo ,and wr ,randomly selected n users from the t
e s a t a
d ,use t hehistoricali nteracitondataasat rainingse tforeachuseraccordingt o .
) 7 ( a l u m r o f
② usingequaiton(3) ,(4) ,(5) ,(6)tocalculatetheweightsofWf(uo) ,Wo(uo) , .
) o u ( c W d n a ) o u ( t W
③ usingthe ilnearregressionmodelt hrough tieraitveweightedvalueofwf ,wo .
r w d n a
④ basedonuserinteracitoninformaiton ,calculatefoursocia lfeaturesWf(uo) , o
u ( o
W ) ,Wt(uo)andWc(uo) inthemicrobloggingsocia lnetwork .The four kinds t s u r t s' r e s u e h t e t a l u c l a c o t g n it ti f r a e n il y b d e t a l u c l a c e r e w s e u l a v e r u t a e f l a i c o s f o
, g n i k n a r y ti r a li m i s e v i s n e h e r p m o c s' r e s u e h t o t g n i d r o c c
A g et the fina l
, L t e s n o it a d n e m m o c e
r In accordance wtih the Top-N sor talgortihm to generate .
s r e s u r o f s n o it a d n e m m o c e r
. C t e s n o it a d n e m m o c e r o u s r e s U : t u p t u O
d n E
o t m h ti r o g l a n o it a d n e m m o c e r e e r g e d t s u r t d n a t s e r e t n i r e s u e h t n o d e s a B
tl u s e r e h T . y ti r a li m i s e v i s n e h e r p m o c s' r e s u e h t e t a l u c l a
c softhetwo comprehensive
r e s u N p o t e h t e k a t n e h T . s tl u s e r n o it a d n e m m o c e r l a n i f e h t s a g n i k n a r
. s r e s u t e g r a t e h t o t s n o it a d n e m m o c e r
I S Y L A N A L A T N E M I R E P X
E S
l a t n e m i r e p x
E Environmen tandT eh Dataset
c e l e s , d o h t e m d e s o p o r p e h t f o s s e n e v it c e f f e e h t y f i r e v o
T ttherea lexperimenta l
l a it n e u l f n i t s o m e h t s i g n i g g o l b o r c i m a n i S . m r o f t a l p g n i k r o w t e n l a i c o s m o r f t e s a t a d
e h t , r e p a p s i h t n I . n o o s d n a s d n e i r f w o ll o f , s t n e m m o c n a c s r e s u , m r o f t a l p e c i v r e s
c i s a b s' r e s u t e g e r a w t f o s r e l w a r c g n i g g o l b o r c i m e s u l a t n e m i r e p x
e informaiton ,users
o r c i
h t d a e r b e h t g n i s
u -firs talgortihm ,collecitng20,000userdata .Inordert oi mprovet he e
t n i e h t , g n i n a e l c y b , m h ti r o g l a t s e t e h t f o y c a r u c c
a graitonofdatasets ,obtained5424 o
r c i m 7 2 6 6 7 2 g n i d u l c n i , s r e s
u -blogs, 115583i nteracitoni nformaiton .Int hispaper , s
s o r
c -overstudy ,thedataseti sdividedi nto80%t rainingsetsand20%tes tsetsand .
m h ti r o g l a e h t e t a d il a v o t s t n e m i r e p x e f o s t e s e l p it l u m y b
o v o n e L e h t n o g n i n n u r e d o c t n e m i r e p x e e h T . a v a J d n a n o h t y P n i e d o C
. 8 . 1 n o i s r e v k d j , 1 . 5 . 3 n o h t y P n o i s r e v n o h t y P , 0 6 4 E d a P k n i h T
: m h ti r o g l a e c n e r e f e r l a t n e m i r e p x
E
p o T n o e s a
B -K simliar collaboraitve fitlering recommendaiton algortihm e
v it a r o b a ll o C
( -Fitlering ,CF)tes talgortihm basedon opensource reference ilbrary t
u o h a M e h c a p
A machine learning implementaiton .CF based on user ,the user it
a l e r r o c s' n o s r a e P g n i s u e t a l u c l a c y ti r a li m i
s oncoefficient ,thefina lresul toftheuse p
o T n o d e s a b d e d n e m m o c e r f
o -Krecommended.
n o it a u l a v
E Metrics
t e s u e w , r e p a p s i h t n
I hreepopular evaluaitonmetrics :Precision ,Recal land F .
e r u s a e m
t a d n e m m o c e r e v it c e f f e n i s tl u s e r d e d n e m m o c e r a s i n o i s i c e r
P ion accountedfor
e h t f o s s e n e v it c e f f e e h t t c e l f e r n a c , d e d n e m m o c e r l l a f o n o it r o p o r p e h t
: s w o ll o f s a ) 1 1 ( a l u m r o f n o it a l u c l a c e h t , m h ti r o g l a n o it a d n e m m o c e r
n o i s i c e r
P = (11)
e r e h
W Nrs is the number of correc trecommended in recommended ilst ,Nr is .
s tl u s e r d e d n e m m o c e r f o r e b m u n e h t
n o it r o p o r p e h t r o f d e t n u o c c a s d n e i r f n i d e t s e r e t n i s r e s u d e d n e m m o c e r a s i ll a c e R
: s w o ll o f s a ) 2 1 ( a l u m r o f n o it a l u c l a c e h t , s d n e i r f s i h f o l l a f o
= ll a c e
R (12)
N e r e h
W r s is the number of correc trecommended in recommended ilst ,Nr is .
r e s u e h t f o s d n e i r f l a t o t f o r e b m u n e h t
h c i h w , e t a r l l a c e r d n a n o i s i c e r p f o n a e m c i n o m r a h d e t h g i e w a s i e r u s a e m F
r e tt e b e h t e r u s a e m F r e h g i h e h T . s e t a r l l a c e r d n a n o i s i c e r p f o s tl u s e r e h t s e n i b m o c
e r d e d n e m m o c e r e h
t sutls.
F measure= (13)
p o T f o r e b m u n d e d n e m m o c e r e h t , t n e m i r e p x e s i h t n
I -N ofN = 2,4,6,8,10 ,five
l a t n e m i r e p x
E Resutl sandAnalyssi
f o s e u l a v g n i h g i e w e h t e n i m r e t e d o T . 1 n
I order to calculate the weigh tα ,according to rea ltes tdatase tleas tsquares t s e b e h t n i a t b o o t α f o e u l a v e h t t s u j d a o t e u n it n o c , g n i m m a r g o r p r a e n il d n a g n it ti f
F 1 e r u g i F n i n w o h s s A . s tl u s e
r measure curve comparison ,when the maximum ,
4 . 0 s i α f o e u l a
v F measuret aket heopitma lpersonailzedrecommendaitonresul.t
g i
F u 1. re algortihm andFmeasure.
e r u g i
F 2 .algortihmprecisioncomparisonchart. Figure3 .algortihmcontras tfigurerecal lrate.
i s e v i s n e h e r p m o c h ti w e c n a d r o c c a n i n o it a d n e m m o c e r l a e r n
I mliartiy
f o e u l a v e h t , e r o f e r e h T . tl u s e r d e d n e m m o c e r s a N t s r i f e h t t c e l e s , r e d r o g n i d n e c s e d
. ll a c e r d n a n o i s i c e r p d n e m m o c e r s d n e i r f g n i g g o l b o r c i m p i h s n o it a l e r t c e r i d a s a h N
n e v e d n a , g n i g g o l b o r c i m f o r e b m u n t n e r e f f i d a e v i e c e r n a c r e s u h c a e e s u a c e b d n A
wli lbeverydifferent ,tii sno tpossiblesimplyt oNt oagivenvalue. N
n e h
w ≤4 ,wtih the increase o f the length of the recommendaiton ilst ,the s
i n o i s i c e r
p increasing, thispaperalgortihmwtihhighe raccuracythanthebasedon n
e m m o c e r g n i r e tl i f l a i c o
e s u a c e b s i s i h T . e n il c e d o t e u n it n o c e t a r y c a r u c c a e h t , s e s a e r c n i t s il d e d n e m m o c e r
n e c s e d y ti r a li m i s e v i s n e h e r p m o c e h
t ding order, there wli lbe some lesser interes t .
e t a r y c a r u c c a d e c u d e r o t d a e l ,t s il n o it a d n e m m o c e r e h t o t n i g n i g g o l b o r c i m
e h t n a h t ll a c e r r e h g i h s i d o h t e m d e s o p o r p e h t , e t a r l l a c e r e h t t a h t s w o h s 3 e r u g i F
r e p a p s i h t , 6 = N n e h w , m h ti r o g l a g n i r e tl i f l a i c o s n o d e s a
b algortihmto achievethe
s d o h t e m h t o b h g u o h tl a , 3 d n a 2 s e r u g i F m o r f e e s n a c e w s A . % 3 . 5 6 f o l l a c e r t s e h g i h
f o r e b m u n e h t e s a e r c n i o t n o it a d n e m m o c e r d o o g a h ti w e t a r l l a c e r d n a n o i s i c e r p e h t
n o i s i c e r p e h t n i d o h t e m d e s o p o r p e h t t u b , e m a s e h t y l h g u o r s d n e r
t and recal lrate
r e tt e b a s a h d o h t e m s i h t s w o h s h c i h w , m h ti r o g l a F C e h t n a h t r e tt e b y lt n a c i f i n g i s s a w
. e c n a m r o f r e p
d e s o p o r p e h t t a h t d e d n e m m o c e r g n i g g o l b o r c i m t a h t w o h s s tl u s e r , ll a r e v O
p m i , t n e t x e n i a t r e c a o t , t c e f f e n o it a d n e m m o c e r r e tt e b a s a h m h ti r o g l
a rove the
. s m h ti r o g l a s d n e i r f d e d n e m m o c e r g n i g g o l b o r c i m e h t f o y ti l a u q
U T U F D N A S N O I S U L C N O
C REWORK
d n i f y li s a e o t s r e s u p l e h n a c m e t s y s d e d n e m m o c e R g n i g g o l b o r c i m d e z il a n o s r e P
o f n i e h
t rmaiton they are interested in . This paper considers the users' own r
e f e r
p ences and interests microblogging users interac twtih trust ,we pu tforward . g n i r e tl i f e v it a r o b a ll o c d e v o r p m i n o d e s a b d o h t e m d e d n e m m o c e R g n i g g o l b o r c i m
l a n o it i d a r t e h t o t y ti r a li m i s t s u r t d e d n e m m o c e r d n a t s e r e t n i s' r e s u e h t h ti w d e n i b m o C
p o T e h t h ti w e c n a d r o c c a n i ; d e v o r p m i s a w m h ti r o g l a g n i r e tl i f e v it a r o b a ll o
c - N
l A g n it r o
S gortihm for the user to generate a recommendaiton ilst .Selec tthe user g n i g g o l b o r c i m g n il b a n e , r e s u e h t o t d e d n e m m o c e r d n e i r f a t s e r e t n i o t y l e k il t s o m
d e z il a n o s r e p y f i r e v o t a t a d t s e t l a e r e h t , y ll a n i F . s n o it a d n e m m o c e r d e z il a n o s r e p
c a r u c c a n o it a d n e m m o c e
r y and efficiency of the method proposed in this paper . l
a e r g n i g g o l b o r c i m r e d i s n o c d l u o c k r o w e r u t u
F -itmepersonailzedrecommendaitons , .
y c n e i c i f f e e v o r p m i r e h t r u f d e d n e m m o c e r
S T N E M G D E L W O N K C A
y b d e t r o p p u s s i k r o w s i h
T the periodica lachievementsof the subjec tof pubilc (
y ti s r e v i n U u o h z g n e h Z n i a i d e m w e n f o n o it a c i n u m m o
c projec tXMTGGCBJSZ05) ,
( e c n i v o r P n a n e H f o t c e j o r p y g o l o n h c e t d n a e c n e i c
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s t c e j o r p h c r a e s e r y g o l o n h c e t d n a e c n e i c s y ti c u o h z g n e h
Z (projec t141PPTGG368).
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