S2SRec2: Set-to-Set Recommendation for Basket Completion with Recipe
Yanan Cao, Omid Memarrast, Shiqin Cai, Sinduja Subramaniam, Evren Korpeoglu, Kannan Achan

TL;DR
This paper introduces S2SRec2, a novel set-to-set recommendation framework that predicts multiple complementary ingredients for incomplete baskets, leveraging recipe knowledge and a multitask learning approach to improve grocery shopping and meal creation.
Contribution
It reformulates basket completion as a set-to-set problem and develops a Set Transformer-based model trained with multitask learning, addressing limitations of traditional single-ingredient prediction methods.
Findings
Outperforms baseline methods on large-scale recipe datasets
Effectively predicts multiple complementary ingredients
Enhances culinary creativity and grocery shopping experience
Abstract
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…
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Taxonomy
TopicsVideo Analysis and Summarization · Recommender Systems and Techniques · Data Mining Algorithms and Applications
