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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許永真 | |
| dc.contributor.author | Chia-en Tai | en |
| dc.contributor.author | 戴佳恩 | zh_TW |
| dc.date.accessioned | 2021-06-13T06:49:36Z | - |
| dc.date.available | 2005-08-01 | |
| dc.date.copyright | 2005-08-01 | |
| dc.date.issued | 2005 | |
| dc.date.submitted | 2005-07-28 | |
| dc.identifier.citation | Brin, S., and Page, L. 1998. The anatomy of a large-scale hypertextual Web search
engine. Computer Networks and ISDN Systems 30(1–7):107–117. Chirita, P.; Nejdl, W.; Schlosser, M. T.; and Scurtu, O. 2004. Personalized reputation management in p2p networks. In ISWC Workshop on Trust, Security, and Reputation on the Semantic Web. Clausen, A. 2004. The cost of attack of pagerank. In IAWTIC’2004: Proceeding of the International Conference on Agents, Web Technologies and Internet Commerce. Dellarocas, C. 2000. Mechanisms for coping with unfair ratings and discriminatory behavior in online reputation reporting systems. In ICIS ’00: Proceedings of the twenty first international conference on Information systems, 520–525. Atlanta, GA, USA: Association for Information Systems. Jeh, G., and Widom, J. 2002. Scaling personalized web search. Technical report, Stanford University. Jurca, R., and Faltings, B. 2003. An incentive compatible reputation mechanism. In Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, 1026–1027. ACM Press. Kamvar, S. D.; Schlosser, M. T.; and Garcia-Molina, H. 2003. The eigentrust algorithm for reputation management in P2P networks. In Proceedings of the Twelfth International World Wide Web Conference. Lin, K.; Lu, H.; Yu, T.; and Tai, C. 2005. A reputation and trust management broker framework for web applications. In EEE05: Proceedings of the IEEE International Conference on e-Technology, e-Commerce, and e-Service. IEEE Computer Society. 80 REFERENCES Marsh, S. 1994. Formalising Trust as a Computational Concept. Ph.D. Dissertation, University of Stirling. Mitchell, T. 1997. Machine Learning. McGraw Hill. chapter 6, 191–196. Mui, L.; Mohtashemi, M.; and Halberstadt, A. 2002. Notions of reputation in multiagents systems: a review. In AAMAS ’02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems, 280–287. New York, NY, USA: ACM Press. Pavlov, E.; Rosenschein, J. S.; and Topol, Z. 2004. Supporting privacy in decentralized additive reputation systems. In The Second International Conference on Trust Management. Pujol, J. M.; Sanguesa, R.; and Delgado, J. 2002. Extracting reputation in multi agent systems by means of social network topology. In AAMAS ’02: Proceedings of the first international joint conference on Autonomous agents and multiagent systems, 467–474. New York, NY, USA: ACM Press. Resnick, P.; Iacovou, N.; Suchak, M.; Bergstrom, P.; and Riedl, J. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work, 175–186. New York, NY: ACM Press. Sabater, J., and Sierra, C. 2001. Regret: A reputation model for gregarious societies. In Fourth Workshop on Deception Fraud and Trust in Agent Societies, 61–70. Wang, Y., and Vassileva, J. 2003. Bayesian network trust model in peer-to-peer networks. In AAMAS ’03: Proceedingsof the Second International Workshop on Agents and Peer-to-Peer Computing at the Autonomous Agents and Multi Agent Systems 2003 Conference, 23–34. REFERENCES 81 Whitby, A.; Josang, A.; and Indulska, J. 2004. Filtering out unfair ratings in bayesian reputation systems. In Proceedings of the 7th Int Workshop on Trust in Agent Societies. Yamamoto, A.; Asahara, D.; Itao, T.; Tanaka, S.; and Suda, T. 2004. Distributed pagerank: A distributed reputation model for open peer-to-peer network. In SAINTWorkshops, 389–394. Yu, B., and Singh, M. P. 2002. An evidential model of distributed reputation management. In Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems, 294–301. ACM Press. Zacharia, G., and Maes, P. 2000. Trust management through reputation mechanisms. Applied Artificial Intelligence Journal 14(9):881–908. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/35361 | - |
| dc.description.abstract | A reputation system predicts a user’s reputation in a way similar to the word-ofmouth
in the real world. Each user sends feedbacks to the system, and the system learns a trust model predicting each user's reputation. The prediction builds up trust relationship between each pair of users and it can reduce a user's losses in a transaction. Our system learns user trust by using Expectation-Maximization algorithm (EM algorithm). EM algorithm can learn the unobservable trust of a user from observable feedbacks sent by users, with the probabilistic model describing the relationship between the known and unknown. The model assumes the existence of a buyer's rating bias which is reflected in a buyer's feedbacks in order to better predict a user's reputation, especially when there are few feedbacks available. Our reputation system predicts both user's reputation and rating bias in a broker-based architecture. EM learning is done inside each broker who only receives feedbacks from its own group of users. Inter-broker communication can reduce the errors brought by the seperation of user feedbacks, while the broker-based architecture keeps the system scalable and avoids drawbacks of a centralized system. EigenTrust is resilience to various attacks in a P2P environment, and we use it to manage our inter-broker communication where the inter-broker relation is in a P2P fashion. We implement a simulator to verify our model, and the experiment result shows that our system can predict better than the simple averaging method. Our system is also less sensitive to the change of feedback types and the increase of users. Therefore, our model can accurately learn a user’s trust in a broker-based system. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T06:49:36Z (GMT). No. of bitstreams: 1 ntu-94-R92922109-1.pdf: 440741 bytes, checksum: 67cc6e189a1e3e81b018cf2619ed6e8f (MD5) Previous issue date: 2005 | en |
| dc.description.tableofcontents | Abstract i
List of Figures vi List of Tables ix Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Broker-based Trust Management . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Chapter 2 RelatedWork 7 2.1 Centralized Trust Management . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.1 Recommender System . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2 Reputation System . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Distributed Trust Management . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 P2P Environment . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 Referral Network . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 Broker System . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 iii 2.3 Dealing User Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.1 Web of Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.2 Learning Trust from Feedback Records . . . . . . . . . . . . . . 12 2.4 EM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Chapter 3 System Architecture 15 3.1 Broker-based Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2 Inter-broker communication . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3 Personalized Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Chapter 4 Probabilistic Trust Model 21 4.1 Likelihood Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 4.2 Bayesian Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.3 EM Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.4 EM Derivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.4.1 Log Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4.2 E-step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.4.3 M-step . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.5 Derived Formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Chapter 5 Inter-broker Communication 31 5.1 EigenTrust Reputation Model . . . . . . . . . . . . . . . . . . . . . . . . 31 5.2 Usage of EigenTrust . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2.1 Broker Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2.2 Usage of Broker Trust . . . . . . . . . . . . . . . . . . . . . . . 34 Chapter 6 Simulator 37 6.1 Simulator Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 iv 6.1.1 Sellers and Buyers . . . . . . . . . . . . . . . . . . . . . . . . . 39 6.1.2 Brokers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6.1.3 EigenTrust Manager . . . . . . . . . . . . . . . . . . . . . . . . 43 6.2 Simulation Process for Single-Broker System . . . . . . . . . . . . . . . 46 6.2.1 Transaction Generation . . . . . . . . . . . . . . . . . . . . . . . 46 6.2.2 Output Timing Set . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.2.3 Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.2.4 Feedbacks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.2.5 Error Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.3 Simulation Process for Multi-Broker System . . . . . . . . . . . . . . . . 50 6.3.1 Transactions and Feedbacks . . . . . . . . . . . . . . . . . . . . 50 6.3.2 Model Training . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.3.3 Error Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Chapter 7 Experiment 53 7.1 Experiment Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 7.2 EM Learning Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.3 Feedback Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 7.4 Standard Deviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 7.5 EM Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 7.6 Numbers of Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 7.7 Multi-Broker Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Chapter 8 Conclusion 77 8.1 Summary of Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . 77 8.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 | |
| dc.language.iso | en | |
| dc.subject | 期望值最大化 | zh_TW |
| dc.subject | 信任 | zh_TW |
| dc.subject | 口碑 | zh_TW |
| dc.subject | 仲介 | zh_TW |
| dc.subject | 學習 | zh_TW |
| dc.subject | Expectation-Maximization | en |
| dc.subject | EM | en |
| dc.subject | Trust | en |
| dc.subject | Reputation | en |
| dc.subject | broker | en |
| dc.subject | learning | en |
| dc.title | 以期望值最大化於仲介式口碑系統之信任學習 | zh_TW |
| dc.title | EM Learning of Trust in A Broker-based Reputation System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 93-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林桂傑,項潔,林智仁 | |
| dc.subject.keyword | 信任,口碑,仲介,學習,期望值最大化, | zh_TW |
| dc.subject.keyword | Trust,Reputation,broker,learning,EM,Expectation-Maximization, | en |
| dc.relation.page | 81 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2005-07-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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