An investigation on the user interaction modes of conversational recommender systems for the music domain
TLDR
An in vivo experimental evaluation of the design and implementation of a conversational music recommender system for the music domain finds that the best interaction mode is based on a mixed strategy that combines buttons and natural language.Abstract:
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.read more
Citations
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Journal ArticleDOI
A Survey on Conversational Recommender Systems
TL;DR: A detailed survey of existing approaches to conversational recommendation is provided, categorizing these approaches in various dimensions, e.g., in terms of the supported user intents or the knowledge they use in the background.
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A Survey on Conversational Recommender Systems
TL;DR: Conversational recommender systems (CRS) as mentioned in this paper are software applications that help users to find items of interest in situations of information overload, where the user can ask questions about the recommendations and to give feedback.
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Conversational Recommender Systems and natural language:: A study through the ConveRSE framework
TL;DR: This paper designs and implements a framework for building chatbots that can recommend items from different domains and interact with the user through natural language, and reveals the most critical components in a CoRS architecture, especially in cold-start situations, and the main issues of the natural-language-based interaction.
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Towards Emotion-aware Recommender Systems: an Affective Coherence Model based on Emotion-driven Behaviors
TL;DR: A general emotion-aware computational model based on affective user profiles in which each preference is associated with the affective state felt by the user at the time when that preference was collected is proposed.
Proceedings ArticleDOI
A Socially-Aware Conversational Recommender System for Personalized Recipe Recommendations
TL;DR: The results show that a conversational recommendation system that engages its users through a rapport-building dialogue improves users' perception of the interaction as well as their perception ofThe system.
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