Provably Efficient Personalized Multi-Objective Bandits with Proactive Conversational Queries
arXiv:2606.08410v1 Announce Type: new Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depends on both unknown rewards and unknown preferences, existing methods infer preferences only from utility feedback, entangling preference learning with reward exploration. In practice, however, users often reveal their priorities through proactive conversational queries (e.g., "cheap and clean hotel"), yet this structured signal is not leveraged. We formalize a proactive query-based framework in whic
The paper highlights a current limitation in existing multi-objective bandit methods that fail to leverage direct user preferences, indicating a gap in current AI decision-making systems.
This research could lead to significantly more efficient and personalized AI decision-making systems by integrating user feedback more directly, improving user satisfaction and operational efficacy.
AI systems could move beyond inferring preferences solely from utility feedback, incorporating conversational queries to streamline preference learning and improve decision quality.
- · AI product developers
- · Customer service platforms
- · Personalized recommendation engines
- · Conversational AI
- · AI systems relying solely on implicit preference learning
- · Companies with poor user feedback mechanisms
More accurate and user-aligned AI systems emerge across various applications.
Increased user trust and adoption of AI-driven personalized services due to better responsiveness to explicit needs.
New business models centered on advanced preference elicitation and continuous user interaction become viable.
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Read at arXiv cs.LG