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DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST RECOMMENDATION AGAINST SHILLING ATTACKS DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST RECOMMENDATION AGAINST.

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Presentation on theme: "DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST RECOMMENDATION AGAINST SHILLING ATTACKS DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST RECOMMENDATION AGAINST."— Presentation transcript:

1 DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST RECOMMENDATION AGAINST SHILLING ATTACKS DISTRIBUTED COLLABORATIVE FILTERING FOR ROBUST RECOMMENDATION AGAINST SHILLING ATTACKS AE-TTIE JI 1, CHEOL YEON 1, HEUNG-NAM KIM 1, AND GEUN- SIK JO 2 1 Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information Engineering, Inha University {aerry13, entireboy, 2 School of Computer Science & Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea

2 INTRODUCTION & 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

3 TCFMA ARCHITECTURE TRUST-BASED COLLABORATIVE FILTERING WITH 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

4 A RCHITECTURE Fig. 1. Overview of trust-based collaborative filtering with mobile agents

5 THE MEANING OF NOTATIONS PXPX Arbitrary user included in web of trust POPO Target user, i.e. similarity model owner PCPC Current user who P O s mobile agent is visiting at the moment {TRUST Px }List of users who are trusted by P X {BLOCK Px }List of users who are distrusted by P X {ITEMS Px } List of pairs, i.e. items which P X already has expressed his or her own opinion and these preference ratings. {PATH Px }Migration path which P X s mobile agent migrates along AGENT Px Personal agent of P X AGENT M Px Mobile agent of P X Table 1. The meaning of notations

6 TRUST-BASED USER SELECTION I. AGENT Po finds the migration path {PATH Po } that includes users trusted by P O for a mobile agent AGENT M Po. II. The neighbors of target user P O are chosen from the users included in {PATH Po }. III. P O s personal agent AGENT Po creates a mobile agent, AGENT M Po, to find neighbors and build a similarity model based on them incrementally. IV. AGENT M Po traces the path recursively until no users exist in {PATH Po }{TRUST Pc }. V. AGENT M Po is disposed of from the last node after visiting all users in {PATH Po }.

7 T RUST - BASED U SER S ELECTION 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.

8 I NCREMENTAL M ODEL B UILDING I. AGENT M PO identifies IO i and IP j that are {ITEMS PO }{ITEMS PC } and {ITEMS PC } - {ITEMS PO } respectively, by communicating with a neighbor agent AGENT PC. II. For each pair (IO i, IP j ), AGENT M PO calculates values and sends the values to its own user agent AGENT PO. (cosine and adjusted cosine similarity)

9 I NCREMENTAL M ODEL B UILDING III. AGENT PO adds up these values incrementally until AGENT M PO sends values of all users in {PATH PO } except for those which dont have IO i. IV. AGENT PO calculates the similarity of item pair (IO i, IP j ).

10 AGENTS TASKS IN EACH CASE Fig. 2. Agents tasks in each case

11 RECOMMENDATIONS & FEEDBACK Predictions Feedback Fig. 3. Recommendations and propagation users feedback

12 DATASETS & EVALUATION METRICS Datasets Crawling through epinions.com in May 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 userstrustsitemsrating 4,751216,4902,955121,862 Table 1. Dataset for Experiment

13 DATASETS & EVALUATION METRICS Evaluation Metrics Mean absolute error (MAE) Absolute Prediction Shift (APS)

14 P ERFORMANCE E VALUATION Prototype system implemented using IBM aglet Software with JDK Benchmark system to compare the performance Random 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)

15 P ERFORMANCE E VALUATION 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 Random TCFMA + cosine TCFMA + adjusted

16 P ERFORMANCE E VALUATION 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 xTrust 5Trust 10Trust 15Trust 25Trust 45 TCFMA + cosine TCFMA + adjusted Table 3. Sensitivity of trust on MAE (neighbor peer size = 50)

17 P ERFORMANCE 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

18 P ERFORMANCE 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 OwnerUser 1User 2User 3User 4User 5Average TCFMA + cosine Time(ms) # User Random Time(ms) # User

19 C ONCLUSION 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.

20 FUTURE 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.

21 !!!!THANKYOU!!!! !!!!THANK YOU!!!!

22 T RUST G RAPH C ONVERSION - A DVOGATO Advogato graph transform function transform ( G = (V, E, C V )) { set E 0, V 0; for all x V do add node x+ to V ; add node x- to V ; if C V (x) >= 1 then add edge (x-, x+) to E; set C E (x-, x+) C V (x) -1; for all edge (x, y) E do add edge (x+, y-) to E; set C E (x+, y-) ; end do add edge (x, supersink) to E; set C E (x-, supersink) 1; end if end do return G =(V, E, CE ); }

23 C APACITY ASSIGNMENT

24 C ONVERTED GRAPH

25 T RUST P ROPAGATION & F INDING M IGRATION P ATH Ford-fulkerson maxflow algorithm function 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 C F (P) min {C F (x, y) : (x, y) in P}; for each edge (x, y) in P do F (x, y) F (x, y) + C F (P); F (y, x) -F (x, y) end do end while }

26 E XAMPLES itemsratings A Matrix 3 AI 5 Space Odyssey 4 Dark City 4 itemsratings B Matrix 4 Star Wars 3 Dark City 4 Ghost Busters 5 itemsratings C Space Odyssey 3 Star Wars 1 Dark City 3 AI 4 Resident Evil 2 itemsratings D AI 4 Resident Evil 5 Minority Report 3 Star Wars 2 Dark City 1

27 E XAMPLES itemsratings A Matrix 3 AI 5 Model Owner Space Odyssey 4 Dark City 4 itemsratings B Neighbor Matrix 4 Star Wars 3 Dark City 4 Ghost Busters 5 Mi: Model owners items Ni: Neighbors items Star Wars Ghost Busters Resident Evil Matrix Dark City AI Mi Ni Star Wars Ghost Busters Resident Evil Matrix Dark City AI Mi Ni *3+3*1+1*2+4*3

28 itemsratings C Space Odyssey 3 NeighborStar Wars 1 Dark City 3 AI 4 Resident Evil 2 Mi: Model owners items Ni: Neighbors items Star Wars Ghost Busters Resident Evil Matrix Dark City AI Mi Ni itemsratings A Matrix 3 AI 5 Model Owner Space Odyssey 4 Dark City 4 Star Wars Ghost Busters Resident Evil Matrix Dark City Space Odyssey AI Mi Ni *3+3*1+1*2+4*3+3*1 E XAMPLES

29 Star Wars Ghost Busters Resident Evil Minority Report Matrix Dark City Space Odyssey AI itemsratings A Matrix 3 AI 5 Model Owner Space Odyssey 4 Dark City 4 itemsratings D AI 4 Neighbor Resident Evil 5 Minority Report 3 Star Wars 5 Dark City 1 Mi Ni Star Wars Ghost Busters Resident Evil Matrix Dark City Space Odyssey AI Mi Ni *3+3*1+1*2+4*3+3*1+1*5


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