In a world of diverse ingredients and dietary considerations, individuals often face challenges in selecting food pairings that cater to their preferences and restrictions. The abundance of options and complexities make this process overwhelming and time-consuming. Our project addresses this issue by implementing a solution that utilizes computational gastronomy techniques to offer personalized food pairing suggestions. By combining content-based filtering and natural lang...