Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues Analysing and improving chatbot dialogues xe2x80x93 socalled chatbot xe2x80x98trainingxe2x80x99 xe2x80x93 is key to the successful implementation and maintenance of chatbots for customer service. Nevertheless, the details of this practice and what service providers may learn from the analysis of such dialogues is not investigated in current research on chatbots. As a first step towards bridging this gap in existing knowledge, we present a study of the qualitative analysis of chatbots dialogues in the context of the customer service department of a large telecom provider. In total 406 dialogues, randomly sampled from all chatbot dialogues during a fourweek period, were included in the analysis. The analysis concerned the chatbotxe2x80x99s ability to resolve customersxe2x80x99 requests, the quality in the chatbot dialogues, and suggestions for improvements of the chatbot knowledge base generated through the analysis. The findings shed light on characteristics of successful and unsuccessful chatbot dialogues and the kind of improvements that may be derived from such analysis. On the basis of the findings we summarize implications for theory and practice, and suggest future research.

Conversational Recommender Systems and Natural Language: A study through the ConveRSE Framework Abstract   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 invitro experiment with two synthetic datasets, to investigate the impact that each component has on the CoRS in terms of recommendation accuracy. Additionally, an invivo 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 coldstart situations, and the main issues of the naturallanguagebased interaction. All the dialogues have been collected in a public available dataset.