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 language||English (US)|
|Title of host publication||RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||9|
|State||Published - Aug 27 2017|
|Event||11th ACM Conference on Recommender Systems, RecSys 2017 - Como, Italy|
Duration: Aug 27 2017 → Aug 31 2017
|Name||RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems|
|Conference||11th ACM Conference on Recommender Systems, RecSys 2017|
|Period||8/27/17 → 8/31/17|
Bibliographical noteFunding 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.
© 2017 Association for Computing Machinery.
- Natural language recommenders
- Qualitative methods
- Recommender systems
- User study
- Virtual assistants