  Today's recommender systems are criticized for recommending items that are
too obvious to arouse users' interest. That's why the recommender systems
research community has advocated some "beyond accuracy" evaluation metrics such
as novelty, diversity, coverage, and serendipity with the hope of promoting
information discovery and sustain users' interest over a long period of time.
While bringing in new perspectives, most of these evaluation metrics have not
considered individual users' difference: an open-minded user may favor highly
novel or diversified recommendations whereas a conservative user's appetite for
novelty or diversity may not be that large. In this paper, we developed a model
to approximate an individual's curiosity distribution over different levels of
stimuli guided by the well-known Wundt curve in Psychology. We measured an
item's surprise level to assess the stimulation level and whether it is in the
range of the user's appetite for stimulus. We then proposed a recommendation
system framework that considers both user preference and appetite for stimulus
where the curiosity is maximally aroused. Our framework differs from a typical
recommender system in that it leverages human's curiosity to promote intrinsic
interest with the system. A series of evaluation experiments have been
conducted to show that our framework is able to rank higher the items with not
only high ratings but also high response likelihood. The recommendation list
generated by our algorithm has higher potential of inspiring user curiosity
compared to traditional approaches. The personalization factor for assessing
the stimulus (surprise) strength further helps the recommender achieve smaller
(better) inter-user similarity.
