arXiv cs.AI· Takashi Ishida, Taro Makino, Issa Sugiura·· 12 小时前AI 评分29
QD-Rec：用质量-多样性方法实现推荐的开放式进化
QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
AI 导读
QD-Rec 是一种质量-多样性方法，将开放式搜索与基础模型结合用于决策推荐，通过上下文 bandit 选择条目、由基础模型基于存档中的早期假设生成新假设，新推荐若新颖或优于最近邻才进入存档。在 M&A、客户定向以及论文与书籍推荐任务中，QD-Rec 在多数设置下取得最高平均质量并构建最大存档。
正文 · 原文
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.
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2610.23470 [cs.IR] |
| (or arXiv:2610.23470v1 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.23470 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Takashi Ishida [view email]
[v1]
Fri, 16 Oct 2026 13:49:22 UTC (3,896 KB)
来源：arXiv cs.AI · arxiv.org