Understanding how people use natural language to ask for recommendations

Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren Terveen, F. Maxwell Harper

Research output: Chapter in Book/Report/Conference proceedingConference contribution

50 Scopus citations


The technical barriers for conversing with recommender systems using natural language are vanishing. Already, there are commercial systems that facilitate interactions with an AI agent. For instance, it is possible to say "what should I watch" to an Apple TV remote to get recommendations. In this research, we investigate how users initially interact with a new natural language recommender to deepen our understanding of the range of inputs that these technologies can expect. We deploy a natural language interface to a recommender system, we observe users' first interactions and follow-up queries, and we measure the differences between speaking- and typing-based interfaces.We employ qualitative methods to derive a categorization of users' first queries (objective, subjective, and navigation) and follow-up queries (refine, reformulate, start over). We employ quantitative methods to determine the differences between speech and text, finding that speech inputs are typically longer and more conversational.

Original languageEnglish (US)
Title of host publicationRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450346528
StatePublished - Aug 27 2017
Event11th ACM Conference on Recommender Systems, RecSys 2017 - Como, Italy
Duration: Aug 27 2017Aug 31 2017

Publication series

NameRecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems


Conference11th ACM Conference on Recommender Systems, RecSys 2017

Bibliographical note

Funding Information:
This material is based on work supported by the National Science Foundation under grants IIS-0964695, IIS-1017697, IIS-1111201, IIS-1210863, and IIS-1218826, and by a grant from Google.

Publisher Copyright:
© 2017 Association for Computing Machinery.


  • Critiquing
  • Natural language recommenders
  • Qualitative methods
  • Recommender systems
  • Speech
  • User study
  • Virtual assistants
  • Voice


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