RecipeBuild: Learning to Build Recipe Ideas using Language model-based Recipe Contextual Modeling

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.