(untitled) (Journal of Information Technology and Digital World, 2023)
Food is essential for living and is the foremost important energy source, making us do all the work. Nowadays, the variability in these food items is increasing. To find out about any new dish or recipe, we mainly depend upon people around us or by trial-and-error method, but neither method tells us about its nutritional content. Since the web has begun to grow, the advent of food recommender systems has made food suggestions easier but these systems work only on the feedback provided by the customer. Hence, here comes a requirement for a nutritional-based recommender system that considers ratings and nutrition and provides the user with an absolute best recommendation so that the users’ taste preferences and well-being are given equal priority. This study intends to use graph embedding approaches to develop a food recipe recommender system, which uses the ingredients’ nutritional value alongside the recipe’s taste and customer feedback. These food recommender systems can impact people’s dietary practices, as their suggestions are both healthy and relevant. People can now eat healthily without being compromised on taste.