Semantic-based Thai Recipe Recommendation

Nowadays, people are constantly affected by epidemics such as COVID-19. To reduce the risk of acquiring germs in the community, people's lifestyles have been changed, and they are more inclined to cook for themselves. Typically, people can usually quickly and easily find recipe information via websites and applications. The resulting recipes consist of ingredients as specified by the user. Unfortunately, users often have ingredients that disappear in available cooking recipes. This makes the system is unable to recommend all relevant recipes to users, although the users can use the existing ingredients instead of the ingredients specified in the recipes. Based on this limitation, this research proposes a semantic-based Thai cooking recipe recommendation system which can recommend recipes based on the ingredient substitutes. This research uses existing Thai food ontology to retrieve substitute ingredients based on three different ingredient properties, such as smell, taste, and texture. To recommend cooking recipes, the system expands the given user queries with substitute ingredients and then calculates similarities between all queries and each cooking recipe. Recipes with high similarities are presented and ranked to users. To evaluate the performances, precision, recall and f-measure are applied. The experiments demonstrate that the proposed method performs well with 0.96, 0.72, and 0.82 in precision, recall, and f-measure respectively.