Towards Deep Conversational Recommendations

TL;DR

This paper introduces ReDial, a large-scale dataset of over 10,000 real-world movie recommendation conversations, and explores neural architectures for developing conversational recommendation systems.

Contribution

It provides the first large-scale dataset for conversational recommendations and investigates new neural models and mechanisms for dialogue-based recommendation systems.

Findings

ReDial enables systematic evaluation of conversational recommendation models.

New neural architectures improve dialogue coherence and recommendation accuracy.

The integrated system demonstrates effective goal-driven dialogue capabilities.

Abstract

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 dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.