Market2Dish: Health-aware Food Recommendation

TL;DR

Market2Dish is a personalized health-aware food recommendation system that maps market ingredients to healthy dishes by integrating recipe retrieval, user health profiling, and a novel hierarchical memory network for improved recommendations.

Contribution

The paper introduces a new scheme combining recipe retrieval, social media-based health profiling, and a hierarchical memory network for personalized health-aware food recommendations.

Findings

Effective health-aware recommendations demonstrated through extensive experiments.

The word-class interaction mechanism improves health condition understanding.

Hierarchical memory network enhances user-recipe interaction modeling.

Abstract

With the rising incidence of some diseases, such as obesity and diabetes, a healthy diet is arousing increasing attention. However, most existing food-related research efforts focus on recipe retrieval, user preference-based food recommendation, cooking assistance, or the nutrition and calorie estimation of dishes, ignoring the personalized health-aware food recommendation. Therefore, in this work, we present a personalized health-aware food recommendation scheme, namely Market2Dish, mapping the ingredients displayed in the market to the healthy dishes eaten at home. The proposed scheme comprises three components, namely recipe retrieval, user-health profiling, and health-aware food recommendation. In particular, recipe retrieval aims to acquire the ingredients available to the users, and then retrieve recipe candidates from a large-scale recipe dataset. User health profiling is to characterize the health conditions of users by capturing the textual health-related information crawled from social networks. Specifically, to solve the issue that the health-related information is extremely sparse, we incorporate a word-class interaction mechanism into the proposed deep model to learn the fine-grained correlations between the textual tweets and pre-defined health concepts. For the health-aware food recommendation, we present a novel category-aware hierarchical memory network-based recommender to learn the health-aware user-recipe interactions for better food recommendation. Moreover, extensive experiments demonstrate the effectiveness of the health-aware food recommendation scheme.