  Design of recommender systems aimed at achieving high prediction accuracy is
a widely researched area. However, several studies have suggested the need for
diversified recommendations, with acceptable level of accuracy, to avoid
monotony and improve customers experience. However, increasing diversity comes
with an associated reduction in recommendation accuracy; thereby necessitating
an optimum tradeoff between the two. In this work, we attempt to achieve
accuracy vs diversity balance, by exploiting available ratings and item
metadata, through a single, joint optimization model built over the matrix
completion framework. Most existing works, unlike our formulation, propose a 2
stage model, a heuristic item ranking scheme on top of an existing
collaborative filtering technique. Experimental evaluation on a movie
recommender system indicates that our model achieves higher diversity for a
given drop in accuracy as compared to existing state of the art techniques.
