Foreachparticipantit threelabels: the "suggested" label (binary), the "seen" label (categoricalwiththree "liked" label (categoricalwiththreeclasses) foratotalof 14 dimensions.

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations.

Summary: This paper provides a large-scale data set of 10,000 conversations for the domain of movie recommendation. Strengths: The authors will release the first large-scale dataset for conversational movie recommendations, which can be significant for follow-up work on conversational recommendation. The authors propose an end-to-end trainable architecture, with sub-components of new neural-based models for sentiment analysis and cold-start recommendations, along with natural language. Weaknesses: - As this work has the perspective of task-oriented recommendation, it seems that works such as [] Li, Xiujun, et al. "End-to-end task-completion neural dialogue systems." Also, discussion in general on how their work differs from other chatbox research works e.g.

There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale data set consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a data set consisting of over 10,000 conversations centered around the theme of providing movie recommendations.