Improved Alternative Average Support Value for Automatic Ingredient Substitute Recommendation in Cooking Recipes

Abstract

Nowadays, any individual interested in learning how to prepare a new dish or complete meal can consult specialized cooking recipe websites and video sharing platforms. Such online repositories are able to keep hundreds of thousands of entries in their databases, being rich sources of information for users with the most vast degrees of expertise. But sometimes it is hard for a user to find an adequate recipe that fits, simultaneously, his nutritional needs, tastes, dietary restrictions and the set of ingredients at hand, and, for non-expert users, it may be too much complicated, or even impossible, to adapt the recipes returned by such systems into his current state of needs. In this work, we propose a new cooking recipe recommendation and generation system, based on improved alternative Average Support Value (ASV) filters and a data-driven text mining approach, for single ingredient substitution. Three new ASV variants are proposed as mechanisms to aggregate recipe context and ingredient relevance into standard ASV, in an attempt to promote better ingredient substitute recommendations to adapt recipes into new culinary domains. The proposed ASV-based recipe generation systems are tested and evaluated by means of qualitative analysis, and the proposed filters performances are compared with standard ASV, when adapting recipes with no restrictions into different dietary restriction domains, showing promising results.