  The suggestion of Points of Interest to people with Autism Spectrum Disorder
(ASD) challenges recommender systems research because these users' perception
of places is influenced by idiosyncratic sensory aversions which can mine their
experience by causing stress and anxiety. Therefore, managing individual
preferences is not enough to provide these people with suitable
recommendations. In order to address this issue, we propose a Top-N
recommendation model that combines the user's idiosyncratic aversions with
her/his preferences in a personalized way to suggest the most compatible and
likable Points of Interest for her/him. We are interested in finding a
user-specific balance of compatibility and interest within a recommendation
model that integrates heterogeneous evaluation criteria to appropriately take
these aspects into account. We tested our model on both ASD and "neurotypical"
people. The evaluation results show that, on both groups, our model outperforms
in accuracy and ranking capability the recommender systems based on item
compatibility, on user preferences, or which integrate these two aspects by
means of a uniform evaluation model.
