  The primary objective of this paper is to present an approach for recommender
systems that can assimilate ranking to the voters or rankers so that
recommendation can be made by giving priority to experts suggestion over usual
recommendation. To accomplish this, we have incorporated the concept of
human-centric aggregation via Ordered Weighted Aggregation (OWA). Here, we are
advocating ranked recommendation where rankers are assigned weights according
to their place in the ranking. Further, the recommendation process which is
presented here for the recommendation of books to university students exploits
linguistic data summaries and Ordered Weighted Aggregation (OWA) technique. In
the suggested approach, the weights are assigned in a way that it associates
higher weights to best ranked university. The approach has been evaluated over
eight different parameters. The superiority of the proposed approach is evident
from the evaluation results. We claim that proposed scheme saves storage spaces
required in traditional recommender systems as well as it does not need users
prior preferences and hence produce a solution for cold start problem. This
envisaged that the proposed scheme can be very useful in decision making
problems, especially for recommender systems. In addition, it emphasizes on how
human-centric aggregation can be useful in recommendation researches, and also
it gives a new direction about how various human specific tasks can be
numerically aggregated.
