# Towards Effective Structured Context Modeling for Conversational Recommender Systems via Dual-node Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/2609.00618 · HTML: https://mlsanity.com/paper/2609.00618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 28 of 28)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System](https://mlsanity.com/paper/2303.14524/) (2023)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems](https://mlsanity.com/paper/2002.09102/) (2020)
- [Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning](https://mlsanity.com/paper/2206.09363/) (2022)
- [Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning](https://mlsanity.com/paper/2305.13660/) (2023)
- [Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning](https://mlsanity.com/paper/2105.09710/) (2021)
- [A Multi-Agent Conversational Recommender System](https://mlsanity.com/paper/2402.01135/) (2024)
- [A Large Language Model Enhanced Conversational Recommender System](https://mlsanity.com/paper/2308.06212/) (2023)
- [BARCOR: Towards A Unified Framework for Conversational Recommendation Systems](https://mlsanity.com/paper/2203.14257/) (2022)
- [Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation](https://mlsanity.com/paper/2403.06769/) (2024)
- [Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking](https://mlsanity.com/paper/2406.00033/) (2024)
- [GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table Question Answering](https://mlsanity.com/paper/2412.01230/) (2024)
- [Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue](https://mlsanity.com/paper/2310.14626/) (2023)
- [Improving Conversational Recommendation Systems via Counterfactual Data Simulation](https://mlsanity.com/paper/2306.02842/) (2023)
- [Toward Structured Knowledge Reasoning: Contrastive Retrieval-Augmented Generation on Experience](https://mlsanity.com/paper/2506.00842/) (2025)
- [SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search](https://mlsanity.com/paper/2410.09580/) (2024)
- [Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User](https://mlsanity.com/paper/2504.20458/) (2025)
- [Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation](https://mlsanity.com/paper/2508.05657/) (2025)
- [Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation](https://mlsanity.com/paper/2504.17427/) (2025)
- [SIGMA: Refining Large Language Model Reasoning via Sibling-Guided Monte Carlo Augmentation](https://mlsanity.com/paper/2506.06470/) (2025)
- [All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era](https://mlsanity.com/paper/2407.10081/) (2024)
- [Compress the Context, Keep the Commitments: A Formal Framework for Verifiable LLM Context Compression](https://mlsanity.com/paper/2605.17304/) (2026)
- [Vague Preference Policy Learning for Conversational Recommendation](https://mlsanity.com/paper/2306.04487/) (2023)

## Abstract (arXiv)

We investigate the role of conversational context modeling in user preference tracking for Conversational Recommendation Systems (CRSs). In this regard, we propose DREAMS, a novel tree-structured context modeling framework that explicitly captures user preference evolution throughout multi-turn interactions. DREAMS introduces two specialized node types to support the two fundamental objectives of CRSs: preference elicitation and preference exploitation. Specifically, elicitation nodes leverage Monte Carlo Tree Search (MCTS) to strategically explore conversational actions and infer latent user preferences, while exploitation nodes employ LLM-based refinement to transform the tracked preference state into structured retrieval queries for recommendation. Extensive experiments on benchmark datasets demonstrate the effectiveness of DREAMS and its design.