| title | QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity |
|---|---|
| authors | Takashi Ishida, Taro Makino, Issa Sugiura |
| date | 2026-10-16 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.23470 |
| affiliation | Sakana AI |
| upvotes | 4 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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 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 kept in an archive as stepping stones. A new recommendation enters the archive if it is novel or better than its nearest neighbor. 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. |
cite
@misc{ishida2026qd,
title={QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity},
author={Takashi Ishida and Taro Makino and Issa Sugiura},
year={2026},
eprint={2610.23470},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.23470}
}