QD-Rec: Open-Ended Evolution of Recommendations via Quality–Diversity

Open-ended search based on foundation models has produced faster algorithms, new results on open mathematical problems, and creative discoveries. We extend this paradigm to decision-making, where the set of possible choices is fixed but the reasons for choosing them are not. Examples are choosing which company to acquire, which paper to read, or which customer to target. In high-stakes decisions, comparing diverse, well-reasoned options can be more useful than a single ranked list. We formalize open-ended recommendation as building a diverse, high-quality set of recommendations, each an item together with a hypothesis, a natural-language reason why the item fits the needs of a decision maker. We propose QD-Rec, a quality-diversity method that selects items with a contextual bandit and generates hypotheses with a foundation model from earlier ones in an archive. A new recommendation is kept in the archive if it is novel or better than its nearest neighbor. Novel recommendations can thus serve as stepping stones for later hypotheses. Across M&A, customer targeting, and paper and book recommendation, QD-Rec gives the highest mean quality and builds the largest archive in most settings.