RecipeADQA: A User-aware Question-Answering Dataset for Multi-Constraint Recipe Recommendation

Question-answering recipe recommendation tasks lack datasets with ground-truth labels. Existing methods often generate datasets from simple combinations of ingredient or tag constraints, leading to unnatural expressions and unreasonable constraints that are hard to reflect users’ real preferences. This paper proposes a construction method for a benchmark-style QA dataset for natural-language recipe recommendation. Driven by real user behavior, our method integrates external user query and review data to extract fine-grained preference signals by LLMs and maps them onto a knowledge-graphbased tag schema, thereby constructing QA data consisting of preference and ingredient constraints. Besides, a greedy strategy for constraint selection is also used to control the answer size, while tagbased re-ranking is applied to improve data quality. The generated dataset RecipeADQA contains natural-language questions, structured constraints, and answer sets, resulting in 128,465 instances. Experiments compare RecipeADQA with baseline datasets from two perspectives: data quality and retrieval-recommendation performance. The results show that ours outperforms existing methods on the same recipe knowledge graph in coverage, higher question quality in naturalness and constraint rationality, and better queryability. Moreover, we design a noise-augmented retrieve-and-rerank model, in which a lightweight parser first retrieves candidate recipes, and a small language model then selects and ranks relevant recipes from a noisy candidate pool. The model trained on RecipeADQA outperforms the same model trained on the baseline datasets. The relevant construction code and data have been open-sourced at a GitHub repository https://github.com/MorimoLab/RecipeADQA.