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Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems

Dat Tran, Yongce Li, Hannah Clay, Negin Golrezaei, Sajjad Beygi, Amin Saberi

TL;DR

Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent.

Abstract

Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions, and act on the user's behalf, which makes handling such ambiguity increasingly important. In existing platforms, ambiguity led to excessive interactions and question fatigue or overconfident recommendations prematurely collapsing the search space. We present an Interactive Decision Support System (IDSS) that addresses ambiguous user queries using entropy as a unifying signal. IDSS maintains a dynamically filtered candidate product set and quantifies uncertainty over item attributes using entropy. This uncertainty guides adaptive preference elicitation by selecting follow-up questions that maximize expected information gain. When preferences remain incomplete, IDSS explicitly incorporates residual uncertainty into downstream recommendations through uncertainty-aware ranking and entropy-based diversification, rather than forcing premature resolution. We evaluate IDSS using review-driven simulated users grounded in real user reviews, enabling a controlled study of diverse shopping behaviors. Our evaluation measures both interaction efficiency and recommendation quality. Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent. These findings demonstrate that entropy-guided reasoning provides an effective foundation for agentic recommendation systems operating under uncertainty.

Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems

TL;DR

Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent.

Abstract

Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions, and act on the user's behalf, which makes handling such ambiguity increasingly important. In existing platforms, ambiguity led to excessive interactions and question fatigue or overconfident recommendations prematurely collapsing the search space. We present an Interactive Decision Support System (IDSS) that addresses ambiguous user queries using entropy as a unifying signal. IDSS maintains a dynamically filtered candidate product set and quantifies uncertainty over item attributes using entropy. This uncertainty guides adaptive preference elicitation by selecting follow-up questions that maximize expected information gain. When preferences remain incomplete, IDSS explicitly incorporates residual uncertainty into downstream recommendations through uncertainty-aware ranking and entropy-based diversification, rather than forcing premature resolution. We evaluate IDSS using review-driven simulated users grounded in real user reviews, enabling a controlled study of diverse shopping behaviors. Our evaluation measures both interaction efficiency and recommendation quality. Results show that entropy-guided elicitation reduces unnecessary follow-up questions, while uncertainty-aware ranking and presentation yield more informative, diverse, and transparent recommendation sets under ambiguous intent. These findings demonstrate that entropy-guided reasoning provides an effective foundation for agentic recommendation systems operating under uncertainty.
Paper Structure (45 sections, 12 equations, 3 figures, 2 tables, 2 algorithms)

This paper contains 45 sections, 12 equations, 3 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. System
  5. Turn-level loop.
  6. Semantic Parsing
  7. Entropy-Guided Question Selection
  8. Entropy Computation
  9. Dynamic Question Dimension Selection
  10. Hybrid Question Generation
  11. Candidate Ranking
  12. Embedding Similarity with MMR
  13. Coverage-Risk Optimization
  14. Entropy-Based Result Diversification
  15. Diversification Dimension Selection
  16. ...and 30 more sections

Figures (3)

  • Figure 1: IDSS system overview. The conversational loop uses LLM parsing and entropy-guided question selection, followed by candidate ranking and entropy-based grid diversification.
  • Figure 2: Example of LLM-based semantic parsing that maps a free-form user query into structured hard constraints ($\mathcal{F}$), soft preference cues ($\mathcal{P}^+$), user disliked features ($\mathcal{P}^-$), and user's patience level ($s$) which are subsequently used for downstream retrieval and ranking.
  • Figure 3: Example of review-driven simulation. A single seed review is rewritten into multiple stylistically consistent variants targeting different vehicles while preserving constraints and priorities. Each review is enriched into a structured record, which yields a persona and an initial query for the interactive evaluation.