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Interest-basedpersonalised real time
content recommendationin online socialcommunities
Nini P Suresh137511
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Problem
Identify unique and diverse
interests of users on real time
basis Existing Recommendation
system flaw: yield biased
decisions favoring popular
content
2
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Problem
3
U={ u1,u2,....,um} I={ i1,i2,....,in}
u1
u2
u3
u4
i1
i2
i3
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Problem
4
i1, i2,i3 should be grouped into
cluster based on the interest of
users optimally.Calculate rating for u1,u2,u3,u
!ind "hether recommendation
repair is re#uired
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$%&I'( )($ I*+%*%(-)-I( ! I(-%/%&-
0)&%$ /%) -I*% +%/&()I%$ &CI)
/%C**%($)-I( &&-%*
5
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6
Through Jaccard Similarity, fnd
distance beteen to items!istance"i,#$%1&JaccardSimilarity"i,#$
%ji
ji
UU
UU
1
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'ssign each item to cluster initially
(ased on distance, using ) meansclustering algorithm , fnd ) clustersPer*orm +nterest rou- .lustering on
these ) clusters
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/
Objective Function
wu,c : Fraction of items of cluster c viewedby the user u
Support(u, c) : robability of user belon!in! to
cluster c
= ),(1 , cuSupportwPE cuCcu"he probability of clusterin! error
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0
Objective Function
#ariance is !iven by
= 2)||
(1||
1
UPE
UVar u
$ur ob%ective is to minimi&e variance and
form the bi!!est cluster
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1
'nput
1(user id,itemid,"ime spent,)value
2('tem id, post time
3( user id, new online time,previous time
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11
+nterest rou- contains seto* items, set o* users andcentre .g.entre o* an +nterest grou-is the item ith smallestaerage distance'll users in the set ill li)eero or more item in the seto* item o* +nterest grou-
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4 *istin!uishin! operation
+utation
rossover+er!e*ivide
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13
*U-)-I(
+utation
i1 i2
!-i3
i4i.
i/
i0
i
i
i1
i11
i12
i13
i14
i1.i1/
i10
i1
i1
i2
i21
i22
i23
i24
i2.
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C/&&%/
i1
i2
i3
i4
i.
c-i/
i0
ii1
-i
i11
i12
i13
i14
i1.
i1/
i10
i1
i1i2
i21
i22
i23
i24
i2.
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16
C/&&%/
i0
i2
i3
i4
i.
c-i/
i1
ii1
c-i
i11
i12
i13
i14
i1.
i1/
i10
i1
i1i2
i21
i22
i23
i24
i2.
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*%/'%
i0
i2
i3
i4
i.
c-i/
i1
ii1
c-i
i11
i12
i13
i14
i1.
i1/
i10
i1
i1i2
i21
i22
i23
i24
i2.
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1/
*%/'%
i0
i2
c-i3
i4
i.
i/
i1
ii1
i
i11
i12
i13
i14
i1.
i1/
i10
i1
i1i2
i21
i22
i23
i24
i2.
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10
i0
i2
c- i3
i4
i.
i/i1
ii1
i
i11
i12
i13
i14
i1.
i1/
i10
i1
i1i2
i21
i22
i23
i24
i2.
$II$%
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2
$II$%
i0
i2
c- i3
i4
i.
i/i1
ii1
i
i11
i12
i13
i14
i1.
i1/
i10
i1
i1i2
i21
i22
i23
-i24
i2.
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21
)lgorithm 4: 'nterestroupluster()
/e#uire: several clusters as the output of 5 means clusterin!
1 'nitialise all clusters into a set 2 smallest6ob%-
3 do
4 fori-1 to si&e71 where si&e is si&e of
. for%-i81 to si&e
/ 'nitialise 9 to null
0 add to 9
remove ith !roup and %th !roup from 9 'nitialise union to null
1 add ith and %th !roup to union
11 add union to 9
12 if $b%ective(9) $b%ective()
13 ;-9
14 smallest6ob%-$b%ective(9)
1. end if
1/ end for
10 end for
1 if smallest6ob% < $b%ective(9)
1 -;
2 end if
21 "hilesmallest6ob% = $b%ective()
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22
)lgorithm 4: 'nterestroupluster()22 round-
23 for round $>S"?>"24 9- @andomly do either crossover() or mer!e() or *ivide()
2. if $b%ective(9) $b%ective()
2/ -9
20 end if
2 if doesnot chan!e for 1 rounds
2 -+utation()
3 end if
31 round88
32 end for
33 @eturn as the bi!!est cluster with minimum variance
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23
Real Time
recommendationsIssues
1alse negaties2eal time neighborselection
3!ierent leels o* +nterest
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)lgorithm 5: >ei!hbor Select(,item)
Require: set of interest group C, post time of item, users latest time t1 and previous time t2
1. Map item with post time
2. Map user with t2-t13. forall users u
. for all items i
!. if "(u,i)# $
%. CMu,i
&1
'. else
. ift2-t1 of user u 1$$$$$
*. +1&11$. end if
11. else
12. +1&$
13. end else
1. ifpost time t1
1!. +2&1
1%. end if
1'. else
1. +2&$
1*. end else
1%. Cmu,i
&+1+2
1'. end else
1. end for
1*. end for
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)lgorithm 5: >ei!hbor Select(,item)
2$. forall user u21. foreah interest group g in C
22. forall items i in g23. if Cm
u,i&1
2. mg,u
&1
2!. end if
2%. end for
2'. end for
2. end for2*. i&interest group of item
3$. for all user u
31. /0item,u
&CMu,item
Mu,i
32. Map olumn id of CM with list of row indees with non-ero entr4 in updated CM
33. ifli# null where l
iis list orresponding to inde of i
3. for eah u in li
do
3!. if/0item,u
# 1
3%. /0item,u
&Mi,u
3'. end if
3. end for
3*. end if
$. 5eturn /0item
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8eighbor Selection"item$1.reate a hashma- *or userconte9t matri9 ith item inde9 as)ey and list o* users li)ed asalue
2:btain the inde9 c o* interestgrou- ith the item3Ta)e list ith c as )ey
4+* list is not em-ty, *or all userschec) 8(;i
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27
>ser Time'da-tion
+=
2
)(1
$
),(6 utiuweighttime if t 7 ! if t 8 !
if u posts9omments
on i
=
|)(|2
)(
$
)(
utt
uttt
avg
avguwhere
if [ ])(2),(!.$ ututt avgavg
otherwise
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2/
||
||),(:r
g
gu
I
IIguecision
=
||
||),(5e
u
gu
I
IIgucall
=
),(5e),(:r
),(5e),(:r)1(),(
gucallguecision
gucallguecisionguweightage
+
+=
),(),(6),(6 guweightiuweighttimeiuweightFinal =
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20
'lgorithm 3? eal Timeecommender+n-ut?ne item@1un neighbour selectionalgorithm *or the ne item and
get the neighbours2u-date ne item rating ithin24hr
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3
3u-date neighbour list4i* neighbour list is not em-ty fnd the
distance o* the item ith centre o* all interestgrou-s5select interest grou- ith the smalldistance6*or each o* users in neighbour list,inaleight is added7emoe users *rom neighbour list i* userdoesnt li)e item/calculate local rating *or the item
0.alculate global rating *or each user1or a s-ecifc user fnd trust orthy usersand determine trust alues
useruserytrustworth
userytrustworth
IIIuserytrustworthuserTrusts
=
6
6)6,(
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11 .alculate eightage
12 .alculate user a--roal and disa--roal
13 +* >serAdisa--roalB userAa--roal ,then
recommendation re-air is reCuired
nRatingMaimum
userytrustworthuserTrustweightage
n
j
j
6
)6,(1==
weightageratingli!eavgapprovalUser 666 =weightagerating"isli!eavgal"issapprovUser 666 =
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esult
32
/o. of items/o. of lusters with o;
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esult
33
2 4 / 1 12 14 1/ 1
.
1
1.
2
2.
3luster distribution
>o of clusters with ob%ective in terms of probab ility
>o of clusters with ob%ective in terms of variance
>o of items
>o of clusters
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Than) Dou
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