# RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

arXiv: https://arxiv.org/abs/2210.10628 · HTML: https://mlsanity.com/paper/2210.10628/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [Inverse Cooking: Recipe Generation from Food Images](https://mlsanity.com/paper/1812.06164/) (2018)
- [Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings](https://mlsanity.com/paper/1804.11146/) (2018)
- [RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System](https://mlsanity.com/paper/2003.02498/) (2020)
- [Rep the Set: Neural Networks for Learning Set Representations](https://mlsanity.com/paper/1904.01962/) (2019)
- [Structure-Aware Generation Network for Recipe Generation from Images](https://mlsanity.com/paper/2009.00944/) (2020)
- [Improving Pointwise Mutual Information (PMI) by Incorporating Significant Co-occurrence](https://mlsanity.com/paper/1307.0596/) (2013)
- [KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks](https://mlsanity.com/paper/1905.07261/) (2019)
- [Eating Healthier: Exploring Nutrition Information for Healthier Recipe Recommendation](https://mlsanity.com/paper/2003.07027/) (2020)
- [Learning Joint Embedding with Modality Alignments for Cross-Modal Retrieval of Recipes and Food Images](https://mlsanity.com/paper/2108.03788/) (2021)
- [PICASO: Permutation-Invariant Cascaded Attentional Set Operator](https://mlsanity.com/paper/2107.08305/) (2021)
- [Completing partial recipes using item-based collaborative filtering to recommend ingredients](https://mlsanity.com/paper/1907.12380/) (2019)
- [Deep Cooking: Predicting Relative Food Ingredient Amounts from Images](https://mlsanity.com/paper/1910.00100/) (2019)

## Built on by (most cited first, 4 of 4)

- [KitchenScale: Learning to predict ingredient quantities from recipe contexts](https://mlsanity.com/paper/2304.10739/) (2023)
- [CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions](https://mlsanity.com/paper/2405.00523/) (2024)
- [S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe](https://mlsanity.com/paper/2507.09101/) (2025)
- [Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task](https://mlsanity.com/paper/2411.01996/) (2024)

## Abstract (arXiv)

We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients. We constructed a large-scale dataset containing ingredient co-occurrence based scores to train and evaluate RecipeMind on food affinity score prediction. Deployed in recipe ideation, RecipeMind helps the user expand an initial set of ingredients by suggesting additional ingredients. Experiments and qualitative analysis show RecipeMind's potential in fulfilling its assistive role in cuisine domain.