Decision support systems for electronic catalogs assist users in making the right decision from a set of possible choices. Common examples of decision making include shopping, deciding where to go for holidays, or deciding your vote in an election. Current research in the field is mainly focused on improving such systems in terms of decision accuracy, i.e. the ratio of correct decisions out of the total number of decisions taken. However, it has been widely recognized recently that another important dimension to consider is how to improve decision confidence, i.e. the certainty of the decision maker that she has made the best decision. We first review multi-attribute decision theory –the underlying framework for electronic catalogs– and present the state-of-the-art research in e-catalogs. We then describe objective and subjective measures to evaluate such systems, and propose a system baseline for achieving more accurate and meaningful comparative evaluations. We propose a framework to study the building of decision confidence within the query-feedback search interaction model, and use it to compare different types of system feedback proposed in the literature. We argue that different types of system feedback based on constraints (e.g. conflict and corrective feedback), even if not novel as such, can be combined in order to improve decision confidence. This claim is further validated by simulations and experimental evaluation comparing constraint-based feedback to ranked list feedback.