3 research outputs found
Exploring Interactions with Voice-Controlled TV
Intelligent agents such as Alexa, Siri, and Google Assistant are now built
into streaming TV systems, allowing people to use voice input to navigate the
increasingly complex set of apps available on a TV. However, these systems
typically support a narrow range of control- and search-oriented commands, and
do not support deeper recommendation or exploration queries. To learn about how
people interact with a recommendation-oriented voice-controlled TV, we use
research through design methods to explore an early prototype movie
recommendation system where the only input modality is voice. We describe
in-depth qualitative research sessions with 11 participants. We contribute
implications for designers of voice-controlled TV: mitigating the drawbacks of
voice-only interactions, navigating the tension between expressiveness and
efficiency, and building voice-driven recommendation interfaces that facilitate
exploration.Comment: 11 pages, pre-prin
INSPIRED: Toward Sociable Recommendation Dialog Systems
In recommendation dialogs, humans commonly disclose their preference and make
recommendations in a friendly manner. However, this is a challenge when
developing a sociable recommendation dialog system, due to the lack of dialog
dataset annotated with such sociable strategies. Therefore, we present
INSPIRED, a new dataset of 1,001 human-human dialogs for movie recommendation
with measures for successful recommendations. To better understand how humans
make recommendations in communication, we design an annotation scheme related
to recommendation strategies based on social science theories and annotate
these dialogs. Our analysis shows that sociable recommendation strategies, such
as sharing personal opinions or communicating with encouragement, more
frequently lead to successful recommendations. Based on our dataset, we train
end-to-end recommendation dialog systems with and without our strategy labels.
In both automatic and human evaluation, our model with strategy incorporation
outperforms the baseline model. This work is a first step for building sociable
recommendation dialog systems with a basis of social science theories.Comment: Accepted as a long paper at EMNLP 2020, corrected typo
Generating Recommendation Dialogs by Extracting Information from User Reviews
Recommendation dialog systems help users navigate e-commerce listings by asking questions about users ’ preferences toward relevant domain attributes. We present a framework for generating and ranking fine-grained, highly relevant questions from user-generated reviews. We demonstrate our approach on a new dataset just released by Yelp, and release a new sentiment lexicon with 1329 adjectives for the restaurant domain.