Graph Database for Recipe Recommendations

Graph databases represent a paradigm shift from relational databases with a strong support for “ relationships”. As compared to relational databases which compute relationships at runtime, graph databases persist relationships for fast querying and data retrieval. This work presents a recipe recommender as a graph database, Neo4j application. Given any set of ingredients, this application recommends a variety of recipes with the help of a data set containing thousands of ingredients. Further based on availability of ingredients with a user, this application helps discover the list of possible dishes with these ingredients. In order to implement this application, ingredients and recipes have been crawled from cookery based websites using Python scripts. The crawled data has been inserted into the Neo4j database and subsequently inter-relationships between ingredients and recipes nodes have been analyzed. Execution of self designed queries has verified the time-efficiency of the proposed approach.