Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems

2026-03-11

AI Summary

This work addresses the challenge of ambiguous user preferences during early-stage interactions on e-commerce platforms, which often leads to query ambiguity, redundant interactions, or premature convergence in recommender systems. To mitigate this, the authors propose an entropy-based Interactive Decision Support System (IDSS) that leverages entropy as a unified signal to dynamically maintain candidate sets and quantify attribute-level uncertainty. The system actively elicits user feedback through questions selected to maximize information gain, thereby clarifying preferences, and subsequently incorporates residual uncertainty into the recommendation phase for ranking and diversified presentation. Experimental results on a simulation environment built from real user reviews demonstrate that the approach significantly reduces ineffective interactions while enhancing recommendation diversity, informativeness, and overall quality, thereby improving system transparency and users’ sense of control.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• ambiguous queries
• preference uncertainty
• agentic recommendation
• user intent ambiguity
• interactive recommendation

Innovation

• entropy-guided elicitation
• uncertainty-aware ranking
• preference elicitation
• agentic recommendation
• interactive decision support