distributed collaborative filtering for robust recommendation against shilling attacks distributed...
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DISTRIBUTED COLLABORATIVE DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST FILTERING FOR ROBUST
RECOMMENDATION RECOMMENDATION AGAINST SHILLING ATTACKSAGAINST SHILLING ATTACKS
AE-TTIE JI1, CHEOL YEON1, HEUNG-NAM KIM1, AND GEUN-SIK JO2
1 Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University
{aerry13, entireboy, nami}@eslab.inha.ac.kr
2 School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea 402-751
gsjo@inha.ac.kr
INTRODUCTION & BACKGROUNDSINTRODUCTION & BACKGROUNDS
A Robustness Analysis of Collaborative Filtering User profiles made by anonymous unauthenticated users Vulnerability to Profile Injection Attacks PocketLens - Distributed Personal Recommender It can partially improve the effects of PIA from system
providers.
Trust in Recommender Systems But, it is still not safe from anonymous attackers! “Trust” in Recommender systems
Automated attack detection schemes and robustness of recommendation algorithms.
Correlation between trust and user similarity
TCFMA ARCHITECTURETCFMA ARCHITECTURETRUST-BASED COLLABORATIVE FILTERING TRUST-BASED COLLABORATIVE FILTERING WITH MOBILE AGENTSWITH MOBILE AGENTS
Credibility of recommendations To achieve robustness against shilling attacks Distributed Personal Recommender Web of Trust
Trust Propagation To overcome sparseness of webs of trust The Advogato trust metric
Scalability To raise the efficiency of distributed computing Mobile Agent Framework
ARCHITECTUREARCHITECTURE
Owner’s Similarity Model
TrustList
ItemList
BlockList
Web of Trust
Action & Feedback
Recommendation
UpdateSimilarity
Dispatch
Creation
Dispatch
MobileAgent
MobileAgent
MobileAgent
Model Owner
Get Neighbors’ Ratings
Neighbors’Ratings
Find Migration Path
Owner’sTrust List
Neighbor’sAgent
Mobile Agent
Message
Neighbors’Trust List
User Agent
Fig. 1. Overview of trust-based collaborative filtering with mobile agents
THE MEANING OF NOTATIONS THE MEANING OF NOTATIONS
PX Arbitrary user included in web of trust
PO Target user, i.e. similarity model owner
PC Current user who PO’s mobile agent is visiting at the moment
{TRUSTPx} List of users who are trusted by PX
{BLOCKPx} List of users who are distrusted by PX
{ITEMSPx}List of <item, rating> pairs, i.e. items which PX already has
expressed his or her own opinion and these preference ratings.
{PATHPx} Migration path which PX’s mobile agent migrates along
AGENTPx Personal agent of PX
AGENTMPx Mobile agent of PX
Table 1. The meaning of notations
TRUST-BASED USER SELECTIONTRUST-BASED USER SELECTION
I. AGENTPo finds the migration path {PATHPo} that includes users trusted by PO for a mobile agent AGENTM
Po.
II. The neighbors of target user PO are chosen from the users included in {PATHPo}.
III. PO’s personal agent AGENTPo creates a mobile agent, AGENTMPo,
to find neighbors and build a similarity model based on them incrementally.
IV. AGENTMPo traces the path recursively until no users exist in
{PATHPo}∩{TRUSTPc}.
V. AGENTMPo is disposed of from the last node after visiting all users
in {PATHPo}.
TRUST-BASED USER SELECTIONTRUST-BASED USER SELECTION
The Advogato maximum flow algorithm Discover which users are trusted by credible
members of an online community and which are not.
The bottleneck property “the total trust quantity accorded to an s → t edge
is not significantly affected by changes to the successors of t”
The minimum number of profiles that make the attack succeed is not included in the process of collaborative filtering.
INCREMENTAL MODEL BUILDINGINCREMENTAL MODEL BUILDING
I. AGENTMPO identifies IOi and IPj that are
{ITEMSPO}∩{ITEMSPC} and {ITEMSPC} - {ITEMSPO} respectively, by communicating with a neighbor agent AGENTPC.
II. For each pair (IOi, IPj), AGENTMPO calculates values and sends
the values to its own user agent AGENTPO. (cosine and adjusted cosine similarity)
2,,
2,,
,,,
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icii
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IPPIPIP
IOPIOIO
IPPIOPIPIO
RatingW
RatingW
RatingRatingW
2,,
2,,
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cjcjj
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PIPPIPIP
PIOPIOIO
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AvgRatingRatingW
AvgRatingRatingW
AvgRatingRatingAvgRatingRatingW
INCREMENTAL MODEL BUILDINGINCREMENTAL MODEL BUILDING
III. AGENTPO adds up these values incrementally until AGENTMPO
sends values of all users in {PATHPO} except for those which don’t have IOi.
IV. AGENTPO calculates the similarity of item pair (IOi, IPj).
jj
ii
ji
IPIPDenomDenom
IOIODenomDenom
IPIONumerNumer
WWW
WWW
WWW
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,
21
),(DenomDenom
Numer
ji
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WW
W
IPIO
IPIOIPIOsim
AGENTS’ TASKS IN EACH CASEAGENTS’ TASKS IN EACH CASE
TrustList
ItemList
BlockList
TrustList
ItemList
BlockList
TrustList
ItemList
BlockList
Owner’sSimilarity Model
TrustList
ItemList
BlockList
Owner’sItem List
Owner’sItem List
Owner’sItem List
Owner’sItem List
Owner’sItem List
Owner’sItem List
Neighbor’sAgent
MobileAgent
Matched item rating
Request for matched item rating
MobileAgent
UserAgent
Reject
Request for matched item rating
MobileAgent
Pre-computed information
Request for matched item rating
Migration Path
Information for similarity
Migration PathInformation for similarity
Neighbor’sAgent
Neighbor’sAgent
Neighbor’sSimilarity Model
[Case 1]
[Case 2]
[Case 3]
Rejection Message
Migration Path
Fig. 2. Agents’ tasks in each case
RECOMMENDATIONS & FEEDBACKRECOMMENDATIONS & FEEDBACK Predictions
Feedback
IP1 ... IPk ... IPj
Delete
Add
Update
User Agent
Model Owner
IP4 ... IPk ... IPn
Trusted peers’Agent
IO1
IO2
IOi
IOk
IO7
IO2
IOm
.
.
.
.
.
.
IO7
IO4
UpdatePropagating
user feedback
Ratingfeedback
IPk
Recom-mending
IPk
i
i io
jo
IOAll ji
IOAll IOPji
IPPIPIOsim
RatingIPIOsimratingp
),(
}),({_
,
,
Fig. 3. Recommendations and propagation user’s feedback
DATASETS & EVALUATION DATASETS & EVALUATION METRICS METRICS
Datasets Crawling through epinions.com in May 2006
http://www.epinions.com Numeric rating of item is in the range of 1 to 5 Web of Trust among users
Users who had rated at least 5 item Users who had expressed trust opinion to at least 25
users Items that had been rated by at least 10 users
users trusts items rating
4,751 216,490 2,955 121,862
Table 1. Dataset for Experiment
DATASETS & EVALUATION METRICS DATASETS & EVALUATION METRICS
Evaluation Metrics
Mean absolute error (MAE)
Absolute Prediction Shift (APS)
M
ratingaratingpMAE
M
i ii
1|__|
M
ratingpratingpAPS
M
i ii
1
|__|
PERFORMANCE EVALUATIONPERFORMANCE EVALUATION
Prototype system implemented using IBM aglet Software with JDK 1.4.2
Benchmark system to compare the performanceRandom model building (in PocketLens)
- Miller, B., Konstan, J., Terveen, L., Riedl, J.: PocketLens: Towards a Personal Recommender System.
In ACM Transactions on Information Systems 22 (2004) 437-476
PERFORMANCE EVALUATIONPERFORMANCE EVALUATION Overall Performance of Prediction Quality
TCFMA + cosine-based scheme showed better prediction quality than the other two methods.
Even a small number of users can result in a relatively better model with our proposed methods
Table 2. Overall Performance of Prediction Quality
Neighbor peer size 10 30 50 70 100
Random 1.2866 1.2863 1.2859 1.2859 1.2859
TCFMA + cosine 1.2113 1.2114 1.2100 1.2101 1.2101
TCFMA + adjusted 1.2384 1.2480 1.2412 1.2415 1.2402
PERFORMANCEPERFORMANCE EVALUATIONEVALUATION
Positive Effect of Trust for Prediction
Datasets with users who have more than x trusted users.
The more trust opinions are included in each user, the better the prediction quality obtained.
Direct trust opinions have a positive influence on prediction quality.
Trust x Trust 5 Trust 10 Trust 15 Trust 25 Trust 45
TCFMA + cosine 1.4131 1.3338 1.3313 1.2867 1.1611
TCFMA + adjusted 1.5688 1.3028 1.2952 1.2512 1.2238
Table 3. Sensitivity of trust on MAE (neighbor peer size = 50)
PERFORMANCE EVALUATIONPERFORMANCE EVALUATION Robustness of the shilling problem
The set of manipulated users including arbitrary 50 ratings were inserted into the training dataset.
Fig. 4. Comparison of robustness on manipulated users
0
20
40
60
80
100
120
100 500 1000 2000
The Number of Injected Manipulated Users
Acc
esse
d M
alic
ious
Use
rs
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Abs
oluy
e P
redi
ctio
n S
hift
#MR of TCFMA + cosine #MR of Random
APS of TCFMA + cosine APS of Random
PERFORMANCE EVALUATIONPERFORMANCE EVALUATION Efficiency of similarity model building
The time required for model building The number of neighbors required for model building
The proposed method is far superior with respect to the effectiveness of similarity model building.
Table 4. Comparison of required time and accessed users (neighbor user size = 50)
Model Owner User 1 User 2 User 3 User 4 User 5 Average
TCFMA + cosine
Time(ms) 5786.81 11576.54 9776.97 12676.54 9425.59 9848.49
# User 292.64 861.94 680.08 953.54 636.64 684.968
RandomTime(ms) 31590.24 30129.18 31966.27 23209.48 20977.24 27574.48
# User 4379.48 4209.75 4505.89 3315.13 2962.29 3874.51
CONCLUSIONCONCLUSION
We proposed a novel TCFMA architecture to solve the problems that can occur in online CF recommender systems related to an improper use of personal information and a profile injection attack.
We obtained very good robustness from malicious attacks without any degradation of prediction quality, compared to general peer-to-peer CF recommender systems.
We also achieved efficient distributed computing for building item-item similarity models by adding useful functionalities of mobile agents.
FUTURE WORKFUTURE WORK Trust Decay
The trust relationship becomes weaker as it forwards to its successors.
It is essential to take this phenomenon into consideration for applying trust propagation algorithms to real-world applications.
Attack Detection Automated attack detection algorithms based on
diverse types of attack models can lead to more robust recommendation algorithms.
!!!!THANK!!!!THANK YOU!!!!YOU!!!!
TRUST GRAPH CONVERSION - TRUST GRAPH CONVERSION - ADVOGATOADVOGATOAdvogato graph transform
function transform ( G = (V, E, CV)) {
set E′ 0, V′ 0;for all x ∈ V do
add node x+ to V′ ;
add node x- to V′ ;
if CV (x) >= 1 then add edge (x-, x+) to E′;
set CE′ (x-, x+) CV (x) -1;for all edge (x, y) E ∈ do
add edge (x+, y-) to E′;
set CE′ (x+, y-) ∞;end doadd edge (x, supersink) to E′;
set CE′ (x-, supersink) 1;end if
end doreturn G′ =(V′, E′, CE′ );
}
CAPACITY ASSIGNMENT CAPACITY ASSIGNMENT
a
c
b
d
f
e
TrustSource
20
7
7
3
3
1
g
0
CONVERTED GRAPHCONVERTED GRAPH
c-b+
d+
f-
e+
19
6 6
2 2
0
s
SuperSink
a-
TrustSource
a+
b-
c+
f+
d- e-
1
1
11
1
1
TRUST PROPAGATION & FINDING TRUST PROPAGATION & FINDING MIGRATION PATHMIGRATION PATH
Ford-fulkerson maxflow algorithmfunction maxflow (G′, seed, supersink) {
for each edge (x, y) E∈ ′ in G′ do F (x, y) 0;F (y, x) 0;
end do
while there exists a path P from seed to supersink in the residual Network G′F do
CF(P) min {CF (x, y) : (x, y) in P};for each edge (x, y) in P do
F (x, y) F (x, y) + CF (P);F (y, x) -F (x, y)
end doend while
}
EXAMPLESEXAMPLES
items ratings
A Matrix 3
AI 5
Space Odyssey 4
Dark City 4
items ratings
B Matrix 4
Star Wars 3
Dark City 4
Ghost Busters 5
items ratings
CSpace
Odyssey 3
Star Wars 1
Dark City 3
AI 4
ResidentEvil 2
items ratings
D AI 4
Resident Evil 5
Minority Report 3
Star Wars 2
Dark City 1
EXAMPLESEXAMPLES22222222 32134134
34211334
items ratings
A Matrix 3
AI 5
Model
OwnerSpace
Odyssey 4
Dark City 4
items ratings
BNeighbor
Matrix 4
Star Wars 3
Dark City 4
Ghost Busters 5
Mi: Model owner’s itemsNi: Neighbor’s items
Star WarsGhost Busters
Resident Evil
Matrix ∨ ∨
Dark City 0.8910 ∨ 0.6459
AI 0.9487 0.9191
Mi
Ni
Star WarsGhost Busters
Resident Evil
Matrix 0.9995 0.9856
Dark City 0.9134 0.8957 0.6459
AI 0.9487 0.9191
Mi
Ni
0.913442+32+12+42
32+12+22+324*3+3*1+1*2+4*3
2222222222 1321334134
1334211334
items ratings
CSpace
Odyssey 3
Neighbor Star Wars 1
Dark City 3
AI 4
ResidentEvil 2
Mi: Model owner’s itemsNi: Neighbor’s items
Star WarsGhost Busters
Resident Evil
Matrix 0.9995 0.9856
Dark City 0.9134 0.8957 0.6459
AI 0.9487 0.9191
Mi
Niitems ratings
A Matrix 3
AI 5
Model
OwnerSpace
Odyssey 4
Dark City 4
Star WarsGhost Busters
Resident Evil
Matrix 0.9995 0.9856
Dark City 0.9146 0.8957 0.6459
Space Odyssey
∨ ∨
AI 0.9487 0.9191
Mi
Ni
0.914642+32+12+42+32
32+12+22+32+124*3+3*1+1*2+4*3+3*1
EXAMPLESEXAMPLES
EXAMPLESEXAMPLES222222222222 513213134134
511334211334
Star WarsGhost Busters
Resident Evil
Minority Report
Matrix 0.9995 0.9856
Dark City 0.7329 0.8957 0.5658 ∨
Space Odyssey
∨ ∨
AI 0.9487 0.8967 ∨
items ratings
A Matrix 3
AI 5
Model
OwnerSpace
Odyssey 4
Dark City 4
items ratings
D AI 4
NeighborResident
Evil 5
Minority Report 3
Star Wars 5
Dark City 1
Mi
Ni
Star WarsGhost Busters
Resident Evil
Matrix 0.9995 0.9856
Dark City 0.9146 0.8957 0.6459
Space Odyssey
∨ ∨
AI 0.9487 0.9191
Mi
Ni
0.732942+32+12+42+32+12
32+12+22+32+12+524*3+3*1+1*2+4*3+3*1+1*5
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