The k-regret query aims to return a size-k subset S of a database D such that, for any query user that selects a data object from this size-k subset S rather than from database D, her regret ratio is minimized. The regret ratio here is modeled by the relative difference in the optimality between the locally optimal object in S and the globally optimal object in D. The optimality of a data object in turn is modeled by a utility function of the query user. Unlike traditional top-k queries, the k-regret query does not minimize the regret ratio for a specific utility function. Instead, it considers a family of infinite utility functions F, and aims to find a size-k subset that minimizes the maximum regret ratio of any utility function in F. Studies on k-regret queries have focused on the family of additive utility functions, which have limitations in modeling individuals' preferences and decision making processes, especially for a common observation called the diminishing marginal rate of substitution (DMRS). We introduce k-regret queries with multiplicative utility functions, which are more expressive in modeling the DMRS, to overcome those limitations. We propose a query algorithm with bounded regret ratios. To showcase the applicability of the algorithm, we apply it to a special family of multiplicative utility functions, the Cobb-Douglas family of utility functions, and a closely related family of utility functions, the Constant Elasticity of Substitution family of utility functions, both of which are frequently used utility functions in microeconomics. After a further study of the query properties, we propose a heuristic algorithm that produces even smaller regret ratios in practice. Extensive experiments on the proposed algorithms confirm that they consistently achieve small maximum regret ratios.