Improving Trust and Reputation Modeling in E-Commerce Using Agent Lifetime and Transaction Count

Effective and reliable trust and reputation modeling systems are central to the success of decentralized e-commerce systems where autonomous agents are relied upon to conduct commercial transactions. However, the subjective and social-based qualities that are inherent to trust and reputation introduce many complexities into the development of a reliable model. Existing research has successfully demonstrated how trust systems can be decentralized and has illustrated the importance of sharing trust information, or rather, modeling reputation. Still, few models have provided a solution for developing an initial set of advisors from whom to solicit reputation rankings, or have taken into account all of the social criteria used to determine trustworthiness. To meet these objectives, we propose the use of two new parameters in trust and reputation modeling: agent lifetime and total transaction count. We describe a model that employs these parameters to calculate an agent’s seniority, then apply this information when selecting agents for soliciting and ranking reputation information. Experiments using this model are described. The results are then presented and discussed to evaluate the effect of using these parameters in reputation modeling. We also discuss the value of our particular model in contrast with related work and conclude with directions for future research.