Question-answering and recommendation system on cooking recipes

Question answering (QA), one of the important applications of Natural Language Processing (NLP) aims to take the user questions and returned to the user with the answers. An open domain QA system deals with a set of questions that can be of any domain. The other type of QA is close-domain where it deals with the questions under a specific domain e.g., agriculture, medicine, education, tourism, etc. Our cooking question answering system is an example of a closed domain QA system. Here, users can ask the cooking related questions and the system returns the actual answer to the user. In this paper, we present different modules of a cooking QA system. In addition to dataset preparation, the development of a cooking ontology, the classification of questions as well as the extraction of candidate answers are also treated as other important aspects, which are discussed in this paper in details. In the cooking QA system, automatic evaluation metrics such as precision, recall, F-score, and C@1 were used for the evaluation of precise answers. In addition, human evaluation is used based on a rating scale. Moreover, the recommendation of recipes has also been attempted and the evaluation metrics show satisfactory performances of the systems.