# S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe

arXiv: https://arxiv.org/abs/2507.09101 · HTML: https://mlsanity.com/paper/2507.09101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation](https://mlsanity.com/paper/2205.14005/) (2022)
- [Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks](https://mlsanity.com/paper/2205.12396/) (2022)
- [Market2Dish: Health-aware Food Recommendation](https://mlsanity.com/paper/2012.06416/) (2020)
- [KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks](https://mlsanity.com/paper/1905.07261/) (2019)
- [RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer](https://mlsanity.com/paper/2210.10628/) (2022)
- [Completing partial recipes using item-based collaborative filtering to recommend ingredients](https://mlsanity.com/paper/1907.12380/) (2019)
- [Enhancing Personalized Recipe Recommendation Through Multi-Class Classification](https://mlsanity.com/paper/2409.10267/) (2024)

## Built on by (most cited first, 1 of 1)

- [Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction](https://mlsanity.com/paper/2601.10132/) (2026)

## Abstract (arXiv)

In grocery e-commerce, customers often build ingredient baskets guided by dietary preferences but lack the expertise to create complete meals. Leveraging recipe knowledge to recommend complementary ingredients based on a partial basket is essential for improving the culinary experience. Traditional recipe completion methods typically predict a single missing ingredient using a leave-one-out strategy. However, they fall short in two key aspects: (i) they do not reflect real-world scenarios where multiple ingredients are often needed, and (ii) they overlook relationships among the missing ingredients themselves. To address these limitations, we reformulate basket completion as a set-to-set (S2S) recommendation problem, where an incomplete basket is input into a system that predicts a set of complementary ingredients. We introduce S2SRec2, a set-to-set ingredient recommendation framework based on a Set Transformer and trained in a multitask learning paradigm. S2SRec2 jointly learns to (i) retrieve missing ingredients from the representation of existing ones and (ii) assess basket completeness after prediction. These tasks are optimized together, enforcing accurate retrieval and coherent basket completion. Experiments on large-scale recipe datasets and qualitative analyses show that S2SRec2 significantly outperforms single-target baselines, offering a promising approach to enhance grocery shopping and inspire culinary creativity.