Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
The paper discusses the interaction between users and AI-driven recommendation systems. It models how users convey preferences and how AI interprets these messages to optimize recommendations. The study focuses on balancing communication and search costs to maximize user satisfaction.
- ▪The user communicates preference information through a costly and noisy message.
- ▪The AI assistant interprets this message to form a posterior belief about the user's true preferences.
- ▪The study identifies optimal message precision and recommendation set size based on cost parameters.
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Computer Science > Artificial Intelligence arXiv:2605.23944 (cs) [Submitted on 2 May 2026] Title:Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search Authors:Jing Dong, Prakirt Raj Jhunjhunwala, Yash Kanoria View a PDF of the paper titled Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search, by Jing Dong and 2 other authors View PDF HTML (experimental) Abstract:We model the interaction between a user and an AI driven recommendation system. The user initiates the process by conveying preference information through a costly and noisy message. The AI assistant, acting as a Bayesian agent, interprets the user's message to form a posterior belief about their true preferences and make product recommendations.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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