Consumers can choose from many different products and base their decisions on the tens of thousands of online evidence about each of their options. However, to synthesize this information into confident decisions can incur high interaction and cognitive costs. Online information is scattered across different sources, and evidence such as reviews can be subjective and conflicting, requiring users to interpret them under their personal context. We introduce Mesh, which scaffolds users in iteratively building up a better understanding of both their choices by evaluating evidence gathered across sources. Lab and field deployment studies found that Mesh significantly reduces the costs of gathering and evaluating evidence and scaffolds decision-making through personalized criteria enabling users to gain deeper insights from data to make confident purchase decisions.
ACM UIST 2020 (r=21.6% N=450)