arXiv AI October 19, 2026 By Takashi Ishida, Taro Makino, Issa Sugiura

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

QD-Rec is a quality–diversity approach that expands recommendation systems by generating diverse, high‑quality suggestions paired with natural‑language hypotheses. It uses a contextual bandit to select items and a foundation model to craft reasons, storing new recommendations in an archive when they are novel or superior to existing ones. Experiments on mergers & acquisitions, customer targeting, and content recommendation show that QD‑Rec achieves higher mean quality and larger archives than baseline methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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