6,856 research outputs found
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Building a Tailored Text Messaging System for Smoking Cessation in Native American Populations
When starting new and healthy habits or encouraging vigilance against returning to poor habits, a simple text message can be beneficial. Text messages also have the advantage of being easily accessible for lower-income populations spread over a rural area, who may not be able to afford smartphones with apps or data plans. Users benefit the most from text messages that are customized for them, but personalization requires time and effort on part of the user and the counselor. However, personalization that focuses on the cultural background of a pool of recipients, in addition to general personal preferences, can be a low-cost method of ensuring the best experience for patients interested in taking up new habits. In this paper, we discuss the development of a system for motivating users to quit smoking designed for Native American users in South Dakota, using text messaging as a daily intervention method for patients. Our results show that focusing on modular message customization options and messages with a conversational tone best helps our goal of providing users with customization options that help motivate them to live happy and healthy lifestyles
A Virtual Conversational Agent for Teens with Autism: Experimental Results and Design Lessons
We present the design of an online social skills development interface for
teenagers with autism spectrum disorder (ASD). The interface is intended to
enable private conversation practice anywhere, anytime using a web-browser.
Users converse informally with a virtual agent, receiving feedback on nonverbal
cues in real-time, and summary feedback. The prototype was developed in
consultation with an expert UX designer, two psychologists, and a pediatrician.
Using the data from 47 individuals, feedback and dialogue generation were
automated using a hidden Markov model and a schema-driven dialogue manager
capable of handling multi-topic conversations. We conducted a study with nine
high-functioning ASD teenagers. Through a thematic analysis of post-experiment
interviews, identified several key design considerations, notably: 1) Users
should be fully briefed at the outset about the purpose and limitations of the
system, to avoid unrealistic expectations. 2) An interface should incorporate
positive acknowledgment of behavior change. 3) Realistic appearance of a
virtual agent and responsiveness are important in engaging users. 4)
Conversation personalization, for instance in prompting laconic users for more
input and reciprocal questions, would help the teenagers engage for longer
terms and increase the system's utility
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
Personalized Dialogue Generation with Diversified Traits
Endowing a dialogue system with particular personality traits is essential to
deliver more human-like conversations. However, due to the challenge of
embodying personality via language expression and the lack of large-scale
persona-labeled dialogue data, this research problem is still far from
well-studied. In this paper, we investigate the problem of incorporating
explicit personality traits in dialogue generation to deliver personalized
dialogues.
To this end, firstly, we construct PersonalDialog, a large-scale multi-turn
dialogue dataset containing various traits from a large number of speakers. The
dataset consists of 20.83M sessions and 56.25M utterances from 8.47M speakers.
Each utterance is associated with a speaker who is marked with traits like Age,
Gender, Location, Interest Tags, etc. Several anonymization schemes are
designed to protect the privacy of each speaker. This large-scale dataset will
facilitate not only the study of personalized dialogue generation, but also
other researches on sociolinguistics or social science.
Secondly, to study how personality traits can be captured and addressed in
dialogue generation, we propose persona-aware dialogue generation models within
the sequence to sequence learning framework. Explicit personality traits
(structured by key-value pairs) are embedded using a trait fusion module.
During the decoding process, two techniques, namely persona-aware attention and
persona-aware bias, are devised to capture and address trait-related
information. Experiments demonstrate that our model is able to address proper
traits in different contexts. Case studies also show interesting results for
this challenging research problem.Comment: Please contact [zhengyinhe1 at 163 dot com] for the PersonalDialog
datase
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