Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2025

Abstract

Recent advancements in dialogue policy planning have focused on optimizing system agent policies to achieve predefined goals, emphasizing strategy design, trajectory acquisition, and training efficiency. However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals. To address this gap, we conduct a comprehensive study using task-specific user personas to evaluate dialogue policy planning under diverse user behaviors. Our analysis, based on these user profiles, reveals significant shortcomings in existing approaches, underscoring the necessity for user-tailored dialogue policies. Building on these insights, we propose the User-Tailored Dialogue Policy Planning (UDP) framework, which integrates an Intrinsic User World Model to capture user traits and feedback. UDP operates in three stages: (1) User Persona Portraying, employing a diffusion model to dynamically infer user profiles; (2) User Feedback Anticipating, using a Brownian Bridge-inspired mechanism to predict user reactions; and (3) User-Tailored Policy Planning, synthesizing these elements to optimize response strategies. To enhance robustness, we introduce an active learning approach that prioritizes challenging user personas during training. Extensive experiments across benchmarks, including both collaborative and non-collaborative settings, demonstrate UDP's effectiveness in learning user-specific dialogue strategies. Results confirm the framework's utility, highlighting its robustness, adaptability, and potential to advance user-centric dialogue systems.

Keywords

User-tailored Dialogue Policy Planning, LLM-based Dialogue Agents

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

SIGIR '25: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, Padua, Italy, July 13-18

First Page

645

Last Page

655

Identifier

10.1145/3726302.3730084

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3726302.3730084

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