Work

RecipeBuild: Learning to Build Recipe Ideas Using Language Model-Based Recipe Contextual Modeling

Year: 2025

Type: article

Abstract: We introduce RecipeBuild, a fine-tuned Pre-trained Language Model (PLM) specifically trained for enhanced contextual understanding in cooking recipes, including human-written recipe titles and user preferences such as cuisine style and cooking method. Our model excels in two key tasks: the Recipe Completion Task, where it recommends proper ingredients, and the Recipe Ideation Task, where it suggests innovative and diverse ingredient combinations. Experimental results demonstrate that RecipeBuild outperforms the baseline models in the Recipe Completion task, achieving an MRR of 0.0798 and a recall of 0.0794, as well as in the Recipe Ideation task, with a Macro MRR of 0.3606 and a Micro MRR of 0.0641, making it a valuable tool for generating personalized and realistic cooking ideas that cater to various dietary needs and culinary cultures. By addressing the generalized task of ingredient recommendation, RecipeBuild demonstrates the potential for improved recipe representation learning.

Source: IEEE Access

DOI: 10.1109/access.2025.3610410

Authors: Donghee Choi, Jaesung Hwang, Mogan Gim, Donghyeon Park

Institutions: Imperial College London, Pusan National University, Sejong University, Hankuk University of Foreign Studies

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: Institute for Information and Communications Technology Promotion, National Institute of Information and Communications Technology