Distributed Representation of Dish Names in Food Related Web Services for Associative Search and Nutrition Estimation
Food related web services, such as recipe websites and food journaling apps, are rapidly gaining popularity. Data from service providers and that generated by users often coexist in these services. Compared to the former, the latter, due to its randomness and lack of organization, is often difficult to incorporate into common service features like recommendation making and associative searching. This work addresses this problem by investigating the effectiveness of multiple text embedding methods in generating distributed representations of dish titles that accurately capture the concepts of the dishes which these titles are referring to. We evaluated the practical potential of these "food vectors" with a retrieval task and a nutrition estimation task, and concluded that a method based on word embedding and LSTM is most suited for these purposes.