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We study how to design product recommendations when consumers’ attention and utility are influenced by time pressure—a prominent example of the context effect—and menu characteristics, such as the number of recommended products in the assortment. Using unique data on consumer purchases from vending machines on train platforms in Tokyo, we develop and estimate a structural consideration set model in which time pressure and the recommendation menu influence attention and utility. We find that time pressure reduces consumer attention but increases utility in general. Time pressure moderates the effect of recommendations for attention of both recommended and non-recommended products, and utility for recommended products. Moreover, the number of total recommendations increases consumer attention in general, but in a diminishing way. In our counterfactual simulation, we find that the revenue-maximizing number of recommendations increases with time pressure. Optimizing the number of recommendations for each vending machine and for each time of day increases the total sales volume by 4.5% relative to the actual policy, 1.9% points more than traditional consumer-segment-based targeting.