  Recent years have witnessed the increasing popularity of mobile devices (such
as iphone) due to the convenience that it brings to human lives. On one hand,
rich user profiling and behavior data (including per-app level, app-interaction
level and system-interaction level) from heterogeneous information sources make
it possible to provide much better services (such as recommendation,
advertisement targeting) to customers, which further drives revenue from
understanding users' behaviors and improving user' engagement. In order to
delight the customers, intelligent personal assistants (such as Amazon Alexa,
Google Home and Google Now) are highly desirable to provide real-time audio,
video and image recognition, natural language understanding, comfortable user
interaction interface, satisfactory recommendation and effective advertisement
targeting.
  This paper presents the research efforts we have conducted on mobile devices
which aim to provide much smarter and more convenient services by leveraging
statistics and big data science, machine learning and deep learning, user
modeling and marketing techniques to bring in significant user growth and user
engagement and satisfactions (and happiness) on mobile devices. The developed
new features are built at either cloud side or device side, harmonically
working together to enhance the current service with the purpose of increasing
users' happiness. We illustrate how we design these new features from system
and algorithm perspective using different case studies, through which one can
easily understand how science driven innovations help to provide much better
service in technology and bring more revenue liftup in business. In the
meantime, these research efforts have clear scientific contributions and
published in top venues, which are playing more and more important roles for
mobile AI products.

Computers and Society