EM Learning of Trust in A Broker-based Reputation System
Date Issued
2005
Date
2005
Author(s)
Tai, Chia-en
DOI
en-US
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.
Subjects
信任
口碑
仲介
學習
期望值最大化
Trust
Reputation
broker
learning
EM
Expectation-Maximization
Type
thesis
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