  Virtual assistants, also known as intelligent conversational systems such as
Google's Virtual Assistant and Apple's Siri, interact with human-like responses
to users' queries and finish specific tasks. Meanwhile, existing recommendation
technologies model users' evolving, diverse and multi-aspect preferences to
generate recommendations in various domains/applications, aiming to improve the
citizens' daily life by making suggestions. The repertoire of actions is no
longer limited to the one-shot presentation of recommendation lists, which can
be insufficient when the goal is to offer decision support for the user, by
quickly adapting to his/her preferences through conversations. Such an
interactive mechanism is currently missing from recommendation systems. This
article sheds light on the gap between virtual assistants and recommendation
systems in terms of different technological aspects. In particular, we try to
answer the most fundamental research question, which are the missing
technological factors to implement a personalized intelligent conversational
agent for producing accurate recommendations while taking into account how
users behave under different conditions. The goal is, instead of adapting
humans to machines, to actually provide users with better recommendation
services so that machines will be adapted to humans in daily life.
