RecipeBowl: A Cooking Recommender for Ingredients and Recipes using Set Transformer

Michael Spranger

Kana Maruyama

IEEE Access



Countless possibilities of recipe combinations challenge us to determine which additional ingredient goes well with others. In this work, we propose RecipeBowl which is a cooking recommendation system that takes a set of ingredients and cooking tags as input and suggests possible ingredient and recipe choices. We formulate a recipe completion task to train RecipeBowl on our constructed dataset where the model predicts a target ingredient previously eliminated from the original recipe. The RecipeBowl consists of a set encoder and a 2-way decoder for prediction. For the set encoder, we utilize the Set Transformer that builds meaningful set representations. Overall, our model builds a set representation of an leave-one-out recipe and maps it to the ingredient and recipe embedding space. Experimental results demonstrate the effectiveness of our approach. Furthermore, analysis on model predictions and interpretations show interesting insights related to cooking knowledge

Related Publications

Improving Artificial Intelligence with Games

Science, 2023
Peter R. Wurman, Peter Stone, Michael Spranger

Games continue to drive progress in the development of artificial intelligence.

MocoSFL: enabling cross-client collaborative self-supervised learning

ICLR, 2023
Jingtao Li, Lingjuan Lyu, Daisuke Iso, Chaitali Chakrabarti*, Michael Spranger

Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…

MECTA: Memory-Economic Continual Test-Time Model Adaptation

ICLR, 2023
Junyuan Hong, Lingjuan Lyu, Jiayu Zhou*, Michael Spranger

Continual Test-time Adaptation (CTA) is a promising art to secure accuracy gains in continually-changing environments. The state-of-the-art adaptations improve out-of-distribution model accuracy via computation-efficient online test-time gradient descents but meanwhile cost …

  • HOME
  • Publications
  • RecipeBowl: A Cooking Recommender for Ingredients and Recipes using Set Transformer


Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.