SAPIENT：通过策略规划与蒙特卡洛树搜索掌握多轮对话推荐
SAPIENT: Mastering Multi-turn Conversational Recommendation with Strategic Planning and Monte Carlo Tree Search
AI总结：
SAPIENT框架通过蒙特卡洛树搜索规划对话并自训练对话智能体，在多轮对话推荐中超越现有最先进方法。
摘要：
对话推荐系统（CRS）主动与用户进行交互式对话，以获取用户偏好并提供个性化推荐。现有方法训练基于强化学习（RL）的智能体时采用贪心动作选择或采样策略，可能面临次优的对话规划问题。为解决这一问题，我们提出了一种新颖的基于蒙特卡洛树搜索（MCTS）的CRS框架SAPIENT。SAPIENT由一个对话智能体（S-agent）和一个对话规划器（S-planner）组成。S-planner基于S-agent提出的初始动作，利用MCTS构建对话搜索树以寻找对话计划。来自S-planner的最佳对话计划被用于指导S-agent的训练，形成一个自训练循环，使S-agent能够迭代地提升其对话规划能力。此外，我们提出了一个高效的变体SAPIENT，用于在训练效率和性能之间进行权衡。在四个基准数据集上的大量实验验证了我们方法的有效性，表明SAPIENT优于最先进的基线方法。我们的代码和数据可通过https://github.com/ninglab/SAPIENT获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
