Information seeking in an online shopping environment is different from classical relevance-based information retrieval. In this paper, we focus on understanding how humans seek information and make economic decisions, when interacting with an array of choices in an online shopping environment. Our study is instantiated on a unique dataset of US hotel reservations from Travelocity.com. Current travel search engines display results with only rudimentary interfaces by using a single ranking criterion. This largely ignores consumers’ multi-dimensional preferences and is constrained by human cognitive limitations towards multiple and diverse online data sources. This paper proposes to improve the search interface using an inter-disciplinary approach. It combines text mining, image classification, social geo-tagging and field experiments with structural modeling techniques, and designs an interface whereby each hotel of interest is ranked according to its average consumer surplus on the travel search site. This system would display the hotels with the “best value for money” for a given consumer at the top of the computer screen during the travel search process in response to a query. The final outcome based on several user studies shows that our proposed ranking system outperforms the existing baselines. This suggests that our inter-disciplinary approach has the potential to enhance user search experience for information seeking in the context of economic decision-making.