In this article, we present
MyrrorBot
, a
personal digital assistant
implementing a natural language interface that allows the users to: (i) access online services, such as
music, video, news,
and
food recommendation
s, in a
personalized
way, by exploiting a strategy for implicit user modeling called
holistic user profiling
; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process.
Basically, the system allows the users to formulate
natural language
requests related to their information needs. Such needs are roughly classified in two groups:
quantified self-related
needs (e.g.,
Did I sleep enough? Am I extrovert?
) and
personalized access
to online services (e.g.,
Play a song I like
). The
intent recognition
strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query.
In the experimental evaluation, we evaluated both
qualitative
(users’ acceptance of the system, usability) as well as
quantitative
(time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that
MyrrorBot
can improve the way people access online services and applications. This leads to a more effective interaction and paves the way for further development of our system.
User Models, Metrics and Measures of Search
