<p>In recent years, the number of people using online recipe services for cooking has increased with the spread of the smartphone. In a recent survey, more than 60% of housewives answered that they use the opportunity to use online recipe sites more often. It is difficult to match food taste to user 's preference as each online recipe page shows a recipe to realize just one taste even though there are countless numbers of recipes in an online recipe service. As a preliminary experiment, we investigated the degree of individual difference in taste preference. As a result, one-third of the subjects preferred to adjust the amount of hot water used for a Miso Soup by -20% or + 20% of the specied amount of the original recipe. The results of the two conducted experiments lead to the decision, that there is a need for a system that can help to adjust the optimal amount of seasoning to personal taste and to support the addition of the exact amount of seasoning. The purpose of this research is, therefore, to increase daily meal satisfaction through the realization of a system, that improves the taste of food based on an online recipe closer to the user's preferable taste without burdening the user. To realize this system, the following three problems are approached: (1) building an individual preference model for food taste, (2) determining the seasoning quantity based on the individual preference model. Existing Research focuses on recommending recipes according to personal taste. However, there is no research on the usage of a system which supports the adjustment of the recipe based on personal preference. Therefore, we develop a system that extracts the user's preference by assessing the individual taste through a questionnaire in a learning model and adjusting the amount of seasoning in the base recipe accordingly, which solves the problem (1) and (2). As a validating experiment, we analyzed the user preference models based on feedback before and after adjusting the seasonings. The results showed that the preference model estimation using a two-week timeframe whose results show that the longer timeframe also increased the accuracy of the preference model, to a point, that the individual preference model can be built correctly.</p>