  Many recommender systems suffer from popularity bias: popular items are
recommended frequently while less popular, niche products, are recommended
rarely or not at all. However, recommending the ignored products in the `long
tail' is critical for businesses as they are less likely to be discovered. In
this paper, we introduce a personalized diversification re-ranking approach to
increase the representation of less popular items in recommendations while
maintaining acceptable recommendation accuracy. Our approach is a
post-processing step that can be applied to the output of any recommender
system. We show that our approach is capable of managing popularity bias more
effectively, compared with an existing method based on regularization. We also
examine both new and existing metrics to measure the coverage of long-tail
items in the recommendation.
