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

Key points

• Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents.1
• 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.1
• 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.1
• 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.1

Sources (1)

• [1]QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 01:49 PM

  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.

  Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents.

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