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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23944 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | toxMHAuybyHe |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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