Recently, the potential for dietary personalization based on genetic/phenotypic characteristics to improve health has been studied. While promising, inputs into this biology-focused personalization process are intensive and may not align with an individual's own health priorities, which drive health behaviors. Here, we examine how personalizing food suggestions based on individuals' health priorities affects the healthiness of their food choices. We conducted a pre-registered experiment examining hypothetical food choices from three food categories in six conditions: (1) control, (2) health priority prime, (3) healthy product subset, (4) health priority prime + healthy product subset, (5) health priority prime + priority subset, and (6) health priority-based personalized product suggestions. Participants in conditions 2, 4, 5, and 6 first encountered a question asking them to select their top health priority from a list of options. In conditions 5, the subset of healthy items was described as foods beneficial for the selected health priority, while in condition 6, participants immediately saw the set of foods beneficial for the selected health priority, but had the option to see all foods instead. After making food choices, participants completed a survey with questions about the choice process, health priorities, and demographic variables. We used logistic regression to analyze the impact of condition on healthiness of food choices, and ordered logistic regression to examine the impact of condition on satisfaction with choices made. The experiment and survey were completed by 4171 adults (≥ 19 years) in the US, with the sample closely matching US distribution of age, sex, education, and income characteristics. There were no significant differences in the distribution of demographic characteristics among conditions. All intervention conditions significantly increased the likelihood that an individual chose a healthy food. However, interventions that combined priming with healthy subsets were significantly more effective than single interventions. Conditions that connected the healthy subsets to individuals' health priorities were particularly effective. The adjusted odds ratio (aOR) of selecting a healthy food was 4.77 (95% CI 4.12, 5.52) relative to the control condition when participants could view a subset described as helpful for their health priority. When people immediately viewed the personalized product set, the aOR increased to 11.67 (95% CI 0.1, 13,5). Likewise, analysis of nutrient content from food choices revealed that personalization decreased saturated fat, added sugar, and sodium and increased dietary fiber, potassium, iron, and calcium. However, product choice satisfaction was significantly lower in the personalized product set, which appears to be partially due to a tendency in this condition to forego choosing a product rather than selecting an unhealthy product. Personalization of product options based on individual health priorities should be tested in real-choice environments.