Research Area: Machine Learning
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 Googles 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.
Keywords:
Conversational search
Recommendation systems
Conversational recommendation systems
DialogFlow framework
Author(s) Name: Jeffrey Dalton , Victor Ajayi , Richard Main
Journal name:
Conferrence name: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval
Publisher name: ACM Library
DOI: 10.1145/3209978.3210168
Volume Information:
