INTERPRETABLE RELATIONAL REPRESENTATIONS FOR FOOD INGREDIENT RECOMMENDATION SYSTEMS Anonymous authors Paper under double-blind review

Abstract

Supporting chefs with ingredient recommender systems to create new recipes is challenging, as good ingredient combinations depend on many factors like taste, smell, cuisine style, texture among others. There have been few attempts to address these issues using machine learning. Useful Machine Learning models do obviously need to be accurate but importantly -especially for food professionals -interpretable. In order to address these issues, we propose the Interpretable Relational Representation Model (IRRM). The main component of the model is a key-value memory network to represent relationships of ingredients. We propose and test two variants of the model. One can learn latent relational representations over a trainable memory network (Implicit model), and the other can learn explainable relational representations over a pre-trained memory network that integrates an external knowledge base (Explicit model). The relational representations resulting from the model are interpretable -they allow to inspect why certain ingredient pairings have been suggested. The Explicit model additionally allows to integrate any number of manually specified constraints. We conduct experiments on two recipe datasets, including CulinaryDB with 45,772 recipes and Flavornet with 55,001 recipes, respectively. The experimental results show that our models are both predictive and informative.

1. INTRODUCTION

Data mining and machine learning methods play an increasingly prominent role in food preference modeling, food ingredient pairing discovery and new recipe generation. Solving these tasks is nontrivial, since the goodness of ingredient combinations depends on many factors like taste, smell, cuisine, texture, and culture. Ahn et al. (2011) detected that the number of shared flavor molecules between ingredients is one of important factors for food pairing. They found Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, while East Asian cuisines tend to avoid compound sharing ingredients. Using this idea, Garg et al. ( 2017) developed a rule-based food pairing system which ranks ingredients based on the number of shares of flavor molecules. Recently, Park et al. ( 2019) suggested a neural network approach based on flavor molecules and co-occurrence of ingredients in recipes. These approaches focus on one-to-one food pairing. There is also research related to many-to-one pairing. De Clercq et al. (2016) proposed the Recipe Completion Task which tries to identify matching ingredients for a partial list of ingredients (the recipe) using a Matrix Factorization based recommender system. Although efforts have been made to detect good ingredient combinations, there is no current Machine Learning method in this field that allows to interpret why suggested pairs are good. Our work is targeted at interpretable recommendation systems for food pairing and recipe completion. Given a set of pre-selected ingredients (cardinality 1 or more) by a user, the recommender suggests top-N ingredients from a set of candidates. For example, suppose a user selects apple and chocolate as the pre-selected ingredients, our recommender suggests some good paired ingredients (e.g. cinnamon) and also identifies reasons (e.g. cinnamon is good for apple and chocolate in terms of their flavor affinity). For this, we propose the Interpretable Relational Representations Model (IRRM) in two variants to address food pairing and recipe completion tasks. The model features a key-value memory network (Sukhbaatar et al. (2015 ), Miller et al. (2016) ) to represent relationships of ingredients. One variant 1

