Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search

• URL: http://arxiv.org/abs/2605.23944v1
• Date: Sat, 02 May 2026 04:23:09 GMT
• Title: Right-Sizing Communication and Recommendation Set Size in AI-Assisted Search
• Abstract summary: We model the interaction between a user and an AI driven recommendation system.<n>The user initiates the process by conveying preference information through a costly and noisy message.<n>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.
• Score: 4.522246974662248
• License: http://creativecommons.org/licenses/by/4.0/
• 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. In particular, it determines how many recommendations to present so as to maximize the user's expected utility from their final choice, while accounting for the search cost induced by the size of the recommendation set. We use mutual information based cost functions to model the two distinct costs incurred by the user during the interaction: (i) a communication cost, which increases with the precision of their preference message, and (ii) a search cost, which increases with the size of the recommendation set provided by the AI assistant. We study products and preferences which live in d dimensional space, and ask how the user's expected payoff can be maximized. For large d, we characterize how optimal message precision and recommendation set size depend on the cost parameters, under two distinct distributions from which recommendations can be sampled from the product universe: (i) Bayes' posterior belief, and (ii) an optimized tilted distribution. Under the posterior sampling scheme (i), we identify a hybrid regime, in which an efficient interaction policy requires jointly optimizing the amount of information (in bits) conveyed by the user and the number of recommendations provided by the AI assistant. In the tilted sampling scheme (ii), our results show that the optimal interaction policy uses only one of communication and search, favoring whichever of them is less costly.

Related papers

• Tree of Preferences for Diversified Recommendation [54.183647833064136]
  We study diversified recommendation from a data-bias perspective.<n>Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach.
  arXiv  Detail & Related papers  (2025-12-24T04:13:17Z)
• Consecutive Preferential Bayesian Optimization [5.048216954459151]
  We generalize preference-based optimization to account for production and evaluation costs.<n>We empirically demonstrate a notable increase in accuracy in setups with high production costs or with indifference feedback.
  arXiv  Detail & Related papers  (2025-11-07T11:30:36Z)
• Churn-Aware Recommendation Planning under Aggregated Preference Feedback [6.261444979025644]
  We study a sequential decision-making problem motivated by recent regulatory and technological shifts.<n>We introduce the Rec-APC model, in which an anonymous user is drawn from a known prior over latent user types.<n>We prove that optimal policies converge to pure exploitation in finite time and propose a branch-and-bound algorithm to efficiently compute them.
  arXiv  Detail & Related papers  (2025-07-06T19:22:47Z)
• Multi-agents based User Values Mining for Recommendation [52.26100802380767]
  We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
  arXiv  Detail & Related papers  (2025-05-02T04:01:31Z)
• Preference Discerning with LLM-Enhanced Generative Retrieval [23.559962422900238]
  In sequential recommendation, models recommend items based on user's interaction history.<n>We propose a new paradigm, namely preference discerning, which explicitly conditions a generative recommendation model on user preferences.<n>Our results show that Mender effectively adapts its recommendation guided by human preferences, even if not observed during training.
  arXiv  Detail & Related papers  (2024-12-11T18:26:55Z)
• Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis [69.37718774071793]
  This paper introduces novel information-theoretic measures for understanding recommender systems. We evaluate 7 recommendation algorithms across 9 datasets, revealing the relationships between our measures and standard performance metrics.
  arXiv  Detail & Related papers  (2024-10-03T13:02:07Z)
• Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations [15.143224593682012]
  We propose a novel recommendation strategy that combines relevance and diversity by a copula function. We use diversity as a surrogate of the amount of knowledge obtained by the user while interacting with the system. Our strategy outperforms several state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-07T13:48:24Z)
• Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback [76.7007545844273]
  We propose a multi-objective decision making framework that accommodates different user preferences over objectives. Our model consists of a Markov decision process with a vector-valued reward function, with each user having an unknown preference vector. We suggest an algorithm that finds a nearly optimal policy for the user using a small number of comparison queries.
  arXiv  Detail & Related papers  (2023-02-07T23:58:19Z)
• Personalized Algorithmic Recourse with Preference Elicitation [20.78332455864586]
  We introduce PEAR, the first human-in-the-loop approach capable of providing personalized algorithmic recourse tailored to the needs of any end-user. PEAR builds on insights from Bayesian Preference Elicitation to iteratively refine an estimate of the costs of actions by asking choice set queries to the target user. Our empirical evaluation on real-world datasets highlights how PEAR produces high-quality personalized recourse in only a handful of iterations.
  arXiv  Detail & Related papers  (2022-05-27T03:12:18Z)
• Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation [55.0391061198924]
  Session-based recommendation aims to predict items that an anonymous user would like to purchase based on her short behavior sequence. It is nontrivial to incorporate price preferences for session-based recommendation. We propose a novel method Co-guided Heterogeneous Hypergraph Network (CoHHN) for session-based recommendation.
  arXiv  Detail & Related papers  (2022-05-09T10:47:15Z)
• Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions [74.00030431081751]
  We formalize the notion of user-specific cost functions and introduce a new method for identifying actionable recourses for users. Our method satisfies up to 25.89 percentage points more users compared to strong baseline methods.
  arXiv  Detail & Related papers  (2021-11-01T19:49:35Z)
• Using Stable Matching to Optimize the Balance between Accuracy and Diversity in Recommendation [3.0938904602244355]
  Increasing aggregate diversity (or catalog coverage) is an important system-level objective in many recommendation domains. Attempts to increase aggregate diversity often result in lower recommendation accuracy for end users. We propose a two-sided post-processing approach in which both user and item utilities are considered.
  arXiv  Detail & Related papers  (2020-06-05T22:12:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.