Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent

• URL: http://arxiv.org/abs/2506.14302v1
• Date: Tue, 17 Jun 2025 08:29:04 GMT
• Title: Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
• Authors: Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong,
• Abstract summary: We introduce a novel multi-turn preference optimization paradigm ECPO.<n>We show that ECPO significantly enhances CRA's interaction capabilities, delivering notable improvements in both efficiency and effectiveness over existing MTPO methods.
• Score: 24.134616865308985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs). However, these agents often generate short-sighted responses that fail to sustain user guidance and meet expectations. Although preference optimization has proven effective in aligning LLMs with user expectations, it remains costly and performs poorly in multi-turn dialogue. To address this challenge, we introduce a novel multi-turn preference optimization (MTPO) paradigm ECPO, which leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turn dialogues, uncovering the underlying causes of dissatisfaction. These causes can be utilized to support targeted optimization of unsatisfactory responses, thereby achieving turn-level preference optimization. ECPO ingeniously eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. To support ECPO, we introduce an LLM-based user simulator, AILO, to simulate user feedback and perform expectation confirmation during conversational recommendations. Experimental results show that ECPO significantly enhances CRA's interaction capabilities, delivering notable improvements in both efficiency and effectiveness over existing MTPO methods.

Related papers

• RecGPT Technical Report [57.84251629878726]
  We propose RecGPT, a next-generation framework that places user intent at the center of the recommendation pipeline.<n> RecGPT integrates large language models into key stages of user interest mining, item retrieval, and explanation generation.<n>Online experiments demonstrate that RecGPT achieves consistent performance gains across stakeholders.
  arXiv  Detail & Related papers  (2025-07-30T17:55:06Z)
• Rethinking Prompt Optimization: Reinforcement, Diversification, and Migration in Blackbox LLMs [10.434732630519377]
  We propose a novel Automatic Prompt Optimization (APO) framework centered on enhancing the feedback mechanism.<n>To mitigate the noise inherent in LLM-generated feedback, we introduce a technique called feedback diversification.<n>Our approach consistently outperforms strong baselines, achieving significant accuracy improvements, faster convergence, and lower computational costs.
  arXiv  Detail & Related papers  (2025-07-14T00:20:14Z)
• Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models? [62.579951798437115]
  This work investigates iterative approximate evaluation for arbitrary prompts.<n>It introduces Model Predictive Prompt Selection (MoPPS), a Bayesian risk-predictive framework.<n>MoPPS reliably predicts prompt difficulty and accelerates training with significantly reduced rollouts.
  arXiv  Detail & Related papers  (2025-07-07T03:20:52Z)
• ASPO: Adaptive Sentence-Level Preference Optimization for Fine-Grained Multimodal Reasoning [14.034412856423529]
  Direct Preference Optimization (DPO) has gained attention for its simplicity and computational efficiency in aligning large language models (LLMs)<n>Recent advancements have extended DPO to multimodal scenarios, achieving strong performance.<n>Traditional DPO relies on binary preference optimization, rewarding or penalizing entire responses without considering fine-grained segment correctness.<n>We propose Adaptive Sentence-level Preference Optimization (ASPO), which evaluates individual sentences for more precise preference optimization.
  arXiv  Detail & Related papers  (2025-05-25T11:33:08Z)
• Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining [66.54211199959298]
  We propose a novel preference learning framework, Modality-Balancing Preference Optimization (MBPO), to address the modality imbalance in LMMs.<n>MBPO constructs a more effective offline preference dataset by generating hard negatives, i.e., rejected responses misled by LLM biases.<n>It can enhance LMM performance on challenging vision-language tasks and effectively reduce hallucinations.
  arXiv  Detail & Related papers  (2025-05-20T03:59:05Z)
• HF4Rec: Human-Like Feedback-Driven Optimization Framework for Explainable Recommendation [8.532115411106068]
  We propose a novel human-like feedback-driven optimization framework for explainable recommendations.<n>This framework employs a dynamic interactive optimization mechanism for achieving human-centered explainable requirements without incurring high labor costs.<n>In particular, we propose to utilize large language models (LLMs) as human simulators to predict human-like feedback for guiding the learning process.
  arXiv  Detail & Related papers  (2025-04-19T02:46:10Z)
• Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment [69.11529841118671]
  We propose a new Deliberative Recommendation task, which incorporates explicit reasoning about user preferences as an additional alignment goal.<n>We then introduce the Reasoning-powered Recommender framework for deliberative user preference alignment.
  arXiv  Detail & Related papers  (2025-02-04T07:17:54Z)
• RosePO: Aligning LLM-based Recommenders with Human Values [38.029251417802044]
  We propose a general framework -- Recommendation with smoothing personalized Preference Optimization (RosePO) RosePO better aligns with customized human values during the post-training stage. Evaluation on three real-world datasets demonstrates the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-10-16T12:54:34Z)
• Overcoming Reward Overoptimization via Adversarial Policy Optimization with Lightweight Uncertainty Estimation [46.61909578101735]
  Adversarial Policy Optimization (AdvPO) is a novel solution to the pervasive issue of reward over-optimization in Reinforcement Learning from Human Feedback. In this paper, we introduce a lightweight way to quantify uncertainties in rewards, relying solely on the last layer embeddings of the reward model.
  arXiv  Detail & Related papers  (2024-03-08T09:20:12Z)
• Relative Preference Optimization: Enhancing LLM Alignment through Contrasting Responses across Identical and Diverse Prompts [95.09994361995389]
  Relative Preference Optimization (RPO) is designed to discern between more and less preferred responses derived from both identical and related prompts. RPO has demonstrated a superior ability to align large language models with user preferences and to improve their adaptability during the training process.
  arXiv  Detail & Related papers  (2024-02-12T22:47:57Z)
• Adversarial Preference Optimization: Enhancing Your Alignment via RM-LLM Game [31.66896160733569]
  We propose an Adversarial Preference Optimization (APO) framework to target more efficient human preference optimization. We find the proposed adversarial training framework further enhances existing alignment baselines in terms of LLM helpfulness and harmlessness.
  arXiv  Detail & Related papers  (2023-11-14T10:10:31Z)
• Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System [65.93577256431125]
  We propose an alternative approach called User-Guided Response Optimization (UGRO) to combine it with a smaller task-oriented dialogue model. This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models. Our approach outperforms previous state-of-the-art (SOTA) results.
  arXiv  Detail & Related papers  (2023-06-16T13:04:56Z)

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.