QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents. We extend this paradigm to recommendation for decision-making, focusing on decisions where the set of possible choices is fixed but the reasons for choosing them are not. In high-stakes decisions, e.g., which company to acquire or which customer to target, 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 ...
| Authors | Takashi Ishida, Taro Makino, Issa Sugiura |
|---|---|
| Primary category | cs.IR |
| Published | 16 October 2026 |
| Last updated at source | 19 October 2026 |
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