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An investigation on the user interaction modes of conversational recommender systems for the music domain

机译：音乐域对话推荐系统用户交互模式的研究

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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.
机译：会话推荐系统（CORS）实施一个范式，允许用户使用自然语言与系统进行交互，用于定义其偏好和发现最适合其需求的项目。 CORSS可以直截了当地实施，因为Thatbots，现在，对于几个应用，例如客户服务，医疗保健和医学诊断，越来越受欢迎。 Chatbots基于自然语言，按钮或两者实现互动。 Chatbot的实施是一个具有挑战性的任务，因为它需要关于自然语言处理和人机交互的知识。由于在无法利用标准接口（驾驶，正在进行家庭作业，运行）时，CORS可能在音乐域中特别有用。但是，文献中没有工作，分析对话音乐推荐系统的不同交互模式。在本文中，我们专注于音乐领域的CORS的设计和实现。我们的CORS包括不同的组件。该系统实现基于内容的建议，批评和自适应策略，以及解释设施。主要的创新贡献是用户可以通过不同的交互模式进行互动：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建议准确性和最终用户互动成本的影响。实验涉及110人，54人完成整个过程。结果的分析表明，最好的交互模式基于混合策略，这些策略结合了按钮和自然语言。此外，结果允许清楚地理解哪些对话中的对话中的步骤，这些步骤对于用户特别奋斗。

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