Vote Goat: Conversational Movie Recommendation

Dalton, J. , Ajayi, V. and Main, R. (2018) Vote Goat: Conversational Movie Recommendation. In: 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Ann Arbor, MI, USA, 8-12 Jul 2018, pp. 1285-1288. ISBN 9781450356572 (doi: 10.1145/3209978.3210168)

162272.pdf - Accepted Version



Conversational search and recommendation systems that use natural language interfaces are an increasingly important area raising a number of research and interface design questions. Despite the increasing popularity of digital personal assistants, the number of conversational recommendation systems is limited and their functionality basic. In this demonstration we introduce Vote Goat, a conversational recommendation agent built using Google's DialogFlow framework. The demonstration provides an interactive movie recommendation system using a speech-based natural language interface. The main intents span search and recommendation tasks including: rating movies, receiving recommendations, retrieval over movie metadata, and viewing crowdsourced statistics. Vote Goat uses gamification to incentivize movie voting interactions with the 'Greatest Of All Time' (GOAT) movies derived from user ratings. The demo includes important functionality for research applications with logging of interactions for building test collections as well as A/B testing to allow researchers to experiment with system parameters.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Dalton, Dr Jeff
Authors: Dalton, J., Ajayi, V., and Main, R.
College/School:College of Science and Engineering > School of Computing Science
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in 41st International ACM SIGIR Conference on Research and Development in Information Retrieval: 1285-1288
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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