8 research outputs found
Justification of Recommender Systems Results: A Service-based Approach
With the increasing demand for predictable and accountable Artificial
Intelligence, the ability to explain or justify recommender systems results by
specifying how items are suggested, or why they are relevant, has become a
primary goal. However, current models do not explicitly represent the services
and actors that the user might encounter during the overall interaction with an
item, from its selection to its usage. Thus, they cannot assess their impact on
the user's experience. To address this issue, we propose a novel justification
approach that uses service models to (i) extract experience data from reviews
concerning all the stages of interaction with items, at different granularity
levels, and (ii) organize the justification of recommendations around those
stages. In a user study, we compared our approach with baselines reflecting the
state of the art in the justification of recommender systems results. The
participants evaluated the Perceived User Awareness Support provided by our
service-based justification models higher than the one offered by the
baselines. Moreover, our models received higher Interface Adequacy and
Satisfaction evaluations by users having different levels of Curiosity or low
Need for Cognition (NfC). Differently, high NfC participants preferred a direct
inspection of item reviews. These findings encourage the adoption of service
models to justify recommender systems results but suggest the investigation of
personalization strategies to suit diverse interaction needs