  People have information needs of varying complexity, which can be solved by
an intelligent agent able to answer questions formulated in a proper way,
eventually considering user context and preferences. In a scenario in which the
user profile can be considered as a question, intelligent agents able to answer
questions can be used to find the most relevant answers for a given user. In
this work we propose a novel model based on Artificial Neural Networks to
answer questions with multiple answers by exploiting multiple facts retrieved
from a knowledge base. The model is evaluated on the factoid Question Answering
and top-n recommendation tasks of the bAbI Movie Dialog dataset. After
assessing the performance of the model on both tasks, we try to define the
long-term goal of a conversational recommender system able to interact using
natural language and to support users in their information seeking processes in
a personalized way.
