Ph.D. Dissertation Defense
A Trust and Reputation Mechanism Through
Behavioral Modeling of Reviewers
Yasaman Haghpanah
11:00am Tuesday 21 August 2012, ITE 325b, UMBC
Trust and reputation have become important topics in various domains, such as online markets, supply chain management, auctions, social networks, and e-commerce applications. With the significant increase in transactions with people and organizations especially in online markets, people need to interact with strangers with whom they have little or no previous interactions. Reputation information as a form of world of mouth in auctions and supply chain management and as a form of provided reviews and ratings on online websites are two different sources for modeling trust and reputation in order to mitigate the risk of not knowing a stranger before actually start interacting with that stranger.
In providing reputation information, people can have different behavior, such as being biased based on incentives or they can have different preferences and viewpoints. In this dissertation, I introduce a novel trust and reputation mechanism that models and learns a reputation provider’s behavior based on probability theory. This learned behavior is then used to re-interpret the reputation information, thus making use of the entire reputation data effectively, even if they are biased or based on personal viewpoints and preferences.I show the importance of learning the behavior of reputation providers using different patterns of being biased or having different preferences and satisfaction thresholds in three different settings of game-theory, an online rating website, and an online marketplace. My results show that learning the behavior of reputation providers in all three above settings helps individuals to more effectively aggregate and adjust reputation information in order to make decisions, thereby increasing their satisfaction and overall payoffs in their interactions.
Committee: Drs. Marie desJardins (Chair), Tim Oates, Tim Finin, Wolfgang Ketter and David Aha
