A POMDP Based Approach to Optimally Select Sellers in Electronic Marketplaces

Selecting a seller in e-markets is a tedious task that we might want to delegate to an agent. Many approaches to constructing such agents have been proposed, building upon different foundations (decision theory, trust modeling) and making use of different information (direct experience with sellers, reputation of sellers, trustworthiness of other buyers called advisors, etc.). In this paper, we propose the SALE POMDP, a new approach based on the decision-theoretic framework of POMDPs. It enables optimal trade-offs of information gaining and exploiting actions, with the ultimate goal of maximizing buyer satisfaction. A unique feature of the model is that it allows querying advisors about the trustworthiness of other advisors. We represent the model as a factored POMDP, thereby enabling the use of computationally more efficient solution methods. Evaluation on the ART testbed demonstrates that SALE POMDP balances the cost of obtaining and benefit of more information more effectively, leading to more earnings, than traditional trust models. Experiments also show that it is more robust to deceptive advisors than a previous POMDP based approach, and that the factored formulation allows the solution of reasonably large instances of seller selection problems.