figure the rise of graph databases the user ragnar likes several dishes for the simple recommendation engine we like to buildall that' left for us to do is ask the graph database to give us the nearest dishes in terms of ingredients againthis is basic approach to recommender systems because it doesn' take into account factors such as dislike of an ingredient or dish the amount of like or dislike score out of instead of binary like or don' like could make difference the amount of the ingredient that is present in the dish the threshold for certain ingredient to become apparent in its taste certain ingredientssuch as spicy pepperwill represent bigger impact for smaller dose than other ingredients would food allergies while this will be implicitly modeled in the like or dislike of dishes with certain ingredientsa food allergy can be so important that single mistake can be fatal avoidance of allergens should overwrite the entire recommendation system many more things for you to ponder about