Allergen-Free Food Dataset and Comparative Analysis of Recommendation Algorithms

Food sensitivities brought about by allergens, for example, gluten, shellfish, nuts, soy and dairy, are developing at an outstanding rate affecting at least 3% of the world at any given time. In developed nations, allergen-free food is widely available because of better clinical and patient mindfulness, significant access to substitutes and financial stability to keep a sans allergen diet. This isn't the situation in the rest of the world, especially in countries like India where unavailability is a huge issue. This paper proposes a dataset consisting of allergen-free food and a comparative analysis of algorithms like Normalized Levenshtein, Damerau Levenshtein, Cosine Similarity, Jaro Winkler and Metric LCS in the context of recommending hypoallergenic variants of products from this dataset to customers.