Basic economic relations such as substitutability and com- plementarity between products are crucial for recommenda- tion tasks, since the utility of one product may depend on whether or not other products are purchased. For example, the utility of a camera lens could be high if the user pos- sesses the right camera (complementarity), while the utility of another camera could be low because the user has already purchased one (substitutability). We propose multi-product utility maximization (MPUM) as a general approach to rec- ommendation driven by economic principles. MPUM inte- grates the economic theory of consumer choice with person- alized recommendation, and focuses on the utility of sets of product sets for individual users. MPUM considers what the users already have when recommending additional products. We evaluate MPUM against several popular recommenda- tion algorithms on two real-world E-commerce datasets. Re- sults confirm the underlying economic intuition, and show that MPUM significantly outperforms the comparison algo- rithms under top-K evaluation metrics.