Thomas T. Tran

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In this paper, we propose a market model and learning algorithms for buying and selling agents in electronic marketplaces. We take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possible existence of dishonest selling(More)
In this paper, we propose a reputation oriented reinforcement learning algorithm for buying and selling agents in electronic market environments. We take into account the fact that multiple selling agents may offer the same good with different qualities. In our approach, buying agents learn to avoid the risk of purchasing low quality goods and to maximize(More)
In this paper, we demonstrate the value of decentralized models of sellers in electronic marketplaces, as the basis for purchasing decisions from buyers. We discuss how buying agents can model the reputation of sellers in order to make effective purchases and how these agents can also take advantage of reputation ratings provided by other buying agents in(More)
In this paper, we describe a framework for modelling the trustworthiness of sellers in the context of an electronic marketplace where multiple selling agents may offer the same good with different qualities and selling agents may alter the quality of their goods. We consider that there may be dishonest sellers in the market (for example, agents who offer(More)
In this paper we introduce a multi-faceted trust model of use for the application of ad-hoc vehicular networks (VANETs) – scenarios where agents representing drivers exchange information with other drivers regarding road and traffic conditions. We argue that there is a need to model trust in various dimensions and that combining these elements(More)
This paper identifies a widely existing phenomenon in social media content, which we call the “words of few mouths” phenomenon. This phenomenon challenges the development of recommender systems based on users’ online opinions by presenting additional sources of uncertainty. In the context of predicting the “helpfulness” of a review document based on users’(More)
E-commerce web sites, such as Amazon.com, provide platforms for consumers to review products and share their opinions. However, it is impossible for consumers to read throughout the huge amount of available reviews. In addition, the quality and helpfulness of reviews are unavailable unless consumers have to read through them.This paper proposes an(More)