Conversational Recommender Systems (CoRSs) implement a paradigm that allows users to interact in natural language with the system for defining their preferences and discovering items that best fit their needs. CoRSs can be straightforwardly implemented as chatbots that, nowadays, are becoming more and more popular for several applications, such as customer care, health care, and medical diagnoses. Chatbots implement an interaction based on natural language, buttons, or both. The implementation of a chatbot is a challenging task since it requires knowledge about natural language processing and human–computer interaction. A CoRS might be particularly useful in the music domain since music is generally enjoyed in contexts when a standard interface cannot be exploited (driving, doing homeworks, running). However, there is no work in the literature that analytically compares different interaction modes for a conversational music recommender system. In this paper, we focus on the design and implementation of a CoRS for the music domain. Our CoRS consists of different components. The system implements content-based recommendation, critiquing and adaptive strategies, as well as explanation facilities. The main innovative contribution is that the user can interact through different interaction modes: natural language, buttons, and mixed. Due to the lack of available datasets for testing CoRSs, we carried out an in vivo experimental evaluation with the goal of investigating the impact of the different interaction modes on the recommendation accuracy and on the cost of interaction for the final user. The experiment involved 110 people, and 54 completed the whole process. The analysis of the results shows that the best interaction mode is based on a mixed strategy that combines buttons and natural language. In addition, the results allow to clearly understand which are the steps in the dialog that are particularly strenuous for the user.

中文翻译：

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音乐领域对话式推荐系统用户交互模式研究

对话式推荐系统 (CoRS) 实现了一种范式，允许用户以自然语言与系统交互，以定义他们的偏好并发现最适合他们需求的项目。CoRS 可以直接实现为聊天机器人，如今，聊天机器人在客户服务、医疗保健和医疗诊断等多种应用中变得越来越流行。聊天机器人基于自然语言、按钮或两者实现交互。聊天机器人的实现是一项具有挑战性的任务，因为它需要有关自然语言处理和人机交互的知识。CoRS 在音乐领域可能特别有用，因为通常在无法利用标准界面（开车、做作业、跑步）的环境中欣赏音乐。然而，文献中没有分析比较对话式音乐推荐系统的不同交互模式的工作。在本文中，我们专注于音乐领域 CoRS 的设计和实现。我们的 CoRS 由不同的组件组成。该系统实现了基于内容的推荐、批评和自适应策略，以及解释工具。主要的创新贡献是用户可以通过不同的交互方式进行交互：自然语言、按钮和混合。由于缺乏用于测试 CoRS 的可用数据集，我们进行了体内实验评估，目的是调查不同交互模式对推荐准确性和最终用户交互成本的影响。实验涉及 110 人，54人完成了整个过程。结果分析表明，最佳交互模式是基于按钮和自然语言相结合的混合策略。此外，结果允许清楚地了解对话中的哪些步骤对用户来说特别费力。