Recommender systems have been attracting much attention from both academia and industry because of their ability to capture user interests and generate personalized item recommendations. As the life pace in contemporary society speeds up, traditional recommender systems are inevitably limited by their disconnected interaction styles and low adaptivity to users’ evolving demands. Consequently, conversational recommender systems emerge as a prospective research area, where an intelligent dialogue agent is integrated with a recommender system. Conversational recommender systems possess the ability to accurately understand end-users’ intent or request and generate human-like dialogue responses when performing recommendations. However, existing conversational recommender systems only allow the systems to ask users for more preference information, while users’ further questions and concerns about the recommended items (e.g., enquiring the location of a recommended restaurant) can hardly be addressed. Though the recent task-oriented dialogue systems allow for two-way communications, they are not easy to train because of their high dependence on human guidance in terms of user intent recognition and system response generation. Hence, to enable two-way human-machine communications and tackle the challenges brought by manually crafted rules, we propose Conversational Recommender System with Adversarial Learning (CRSAL), a novel end-to-end system to tackle the task of conversational recommendation. In CRSAL, we innovatively design a fully statistical dialogue state tracker coupled with a neural policy agent to precisely capture each user’s intent from limited dialogue data and generate conversational recommendation actions. We further develop an adversarial Actor-Critic reinforcement learning approach to adaptively refine the quality of generated system actions, thus ensuring coherent human-like dialogue responses. Extensive experiments on two benchmark datasets fully demonstrate the superiority of CRSAL on conversational recommendation tasks.

中文翻译：

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CRSAL

推荐系统因其捕捉用户兴趣和生成个性化项目推荐的能力而受到学术界和工业界的广泛关注。随着当代社会生活节奏的加快，传统的推荐系统不可避免地受限于其不连贯的交互方式和对用户不断变化的需求的低适应性。因此，对话式推荐系统成为一个前瞻性研究领域，其中智能对话代理与推荐系统集成。会话推荐系统能够准确理解最终用户的意图或请求，并在执行推荐时生成类似人类的对话响应。然而，现有的会话推荐系统只允许系统向用户询问更多偏好信息，而用户对推荐项目的进一步问题和顾虑（例如，查询推荐餐厅的位置）则难以解决。尽管最近的面向任务的对话系统允许双向通信，但由于在用户意图识别和系统响应生成方面高度依赖人类指导，它们并不容易训练。因此，为了实现双向人机通信并应对人工规则带来的挑战，我们提出了具有对抗性学习的会话推荐系统 (CRSAL)，这是一种用于解决会话推荐任务的新型端到端系统。在 CRSAL 中，我们创新地设计了一个完全统计的对话状态跟踪器和一个神经策略代理，以从有限的对话数据中精确捕捉每个用户的意图并生成对话推荐动作。我们进一步开发了一种对抗性的 Actor-Critic 强化学习方法，以自适应地改进生成的系统动作的质量，从而确保连贯的类人对话响应。在两个基准数据集上进行的大量实验充分证明了 CRSAL 在会话推荐任务上的优越性。