用蒙特卡洛树搜索优化多轮对话推荐，让推荐更懂用户意图。
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
- 引入蒙特卡洛树搜索构建对话规划树，寻找最优对话策略。
- 在四个数据集上超越现有最佳方法，提升推荐准确率。
- 适合研究对话系统与个性化推荐的学者和工程师。
对话式推荐系统（CRS）通过交互对话主动获取用户偏好并提供个性化推荐。现有方法采用贪婪或采样策略训练基于强化学习的智能体，可能导致对话规划不佳。为此，我们提出基于蒙特卡洛树搜索（MCTS）的新型框架SAPIENT。SAPIENT包含对话智能体（S-agent）和对话规划器（S-planner）。S-planner基于S-agent提出的初始动作构建对话搜索树，利用MCTS寻找最优对话方案。这些优质对话计划用于指导S-agent的训练，形成自训练循环，使智能体持续提升对话规划能力。此外，我们还提出高效变体SAPIENT，平衡训练效率与性能。在四个基准数据集上的大量实验验证了该方法的有效性，SAPIENT显著优于当前最先进的基线模型。代码与数据可通过https://github.com/ninglab/SAPIENT 获取。
原文摘要 · Abstract (English)
Conversational Recommender Systems (CRS) proactively engage users in interactive dialogues to elicit user preferences and provide personalized recommendations. Existing methods train Reinforcement Learning (RL)-based agent with greedy action selection or sampling strategy, and may suffer from suboptimal conversational planning. To address this, we present a novel Monte Carlo Tree Search (MCTS)-based CRS framework SAPIENT. SAPIENT consists of a conversational agent (S-agent) and a conversational planner (S-planner). S-planner builds a conversational search tree with MCTS based on the initial actions proposed by S-agent to find conversation plans. The best conversation plans from S-planner are used to guide the training of S-agent, creating a self-training loop where S-agent can iteratively improve its capability for conversational planning. Furthermore, we propose an efficient variant SAPIENT for trade-off between training efficiency and performance. Extensive experiments on four benchmark datasets validate the effectiveness of our approach, showing that SAPIENT outperforms the state-of-the-art baselines. Our code and data are accessible through https://github.com/ninglab/SAPIENT.
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