Digital Assistants (DA) such as Amazon Alexa, Siri, or Google Assistant are now gaining great diffusion, since they allow users to execute a wide range of actions through messages in natural language. Even though DAs are able to complete tasks such as sending texts, making phone calls, or playing songs, they do not yet implement recommendation facilities. In this paper, we investigate the combination of Digital Assistants and Conversational Recommender Systems (CoRSs) by designing and implementing a framework named ConveRSE (Conversational Recommender System framEwork), for building chatbots that can recommend items from different domains and interact with the user through natural language. Since a CoRS architecture is generally composed of different elements, we performed an in-vitro experiment with two synthetic datasets, to investigate the impact that each component has on the CoRS in terms of recommendation accuracy. Additionally, an in-vivo experiment was carried out to understand how natural language influences both the cost of interaction and recommendation accuracy of a CoRS. Experimental results have revealed the most critical components in a CoRS architecture, especially in cold-start situations, and the main issues of the natural-language-based interaction. All the dialogues have been collected in a public available dataset.

诸如Amazon Alexa，Siri或Google Assistant之类的数字助理（DA）现在正在迅速普及，因为它们允许用户通过自然语言的消息来执行多种动作。即使DA能够完成诸如发送文本，拨打电话或播放歌曲之类的任务，但它们仍未实现推荐功能。在本文中，我们通过设计和实现一个名为ConveRSE（会话推荐系统框架）的框架来研究数字助理和会话推荐系统（CoRS）的组合，以构建可以推荐不同领域的项目并通过自然交互与用户交互的聊天机器人。语言。由于CoRS架构通常由不同的元素组成，因此我们使用两个综合数据集进行了体外实验，在建议准确性方面调查每个组件对CoRS的影响。此外，还进行了一项体内实验，以了解自然语言如何影响CoRS的交互成本和推荐准确性。实验结果揭示了CoRS架构中最关键的组件，尤其是在冷启动情况下，以及基于自然语言的交互的主要问题。所有对话均已收集到公共可用数据集中。特别是在冷启动情况下，以及基于自然语言的交互的主要问题。所有对话均已收集到公共可用数据集中。特别是在冷启动情况下，以及基于自然语言的交互的主要问题。所有对话均已收集到公共可用数据集中。