Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems

TL;DR

This paper introduces an entropy-guided interactive recommendation system that improves handling ambiguous user queries by reducing unnecessary questions and providing diverse, informative recommendations, enhancing user experience under uncertainty.

Contribution

It presents a novel entropy-based framework for preference elicitation and diversification in agentic recommendation systems, addressing ambiguity more effectively than prior methods.

Findings

Entropy-guided elicitation reduces unnecessary follow-up questions.

Uncertainty-aware ranking yields more diverse, informative recommendations.

System improves interaction efficiency and recommendation quality under ambiguity.

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.