  With the rapid development of recommender systems, accuracy is no longer the
only golden criterion for evaluating whether the recommendation results are
satisfying or not. In recent years, diversity has gained tremendous attention
in recommender systems research, which has been recognized to be an important
factor for improving user satisfaction. On the one hand, diversified
recommendation helps increase the chance of answering ephemeral user needs. On
the other hand, diversifying recommendation results can help the business
improve product visibility and explore potential user interests. In this paper,
we are going to review the recent advances in diversified recommendation.
Specifically, we first review the various definitions of diversity and generate
a taxonomy to shed light on how diversity have been modeled or measured in
recommender systems. After that, we summarize the major optimization approaches
to diversified recommendation from a taxonomic view. Last but not the least, we
project into the future and point out trending research directions on this
topic.
