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.

本文介绍了一种基于个性化多样化重新排序的方法，以增加不受欢迎的项目在推荐中的表现，同时保持良好的推荐准确性，并展示了其对比现有基于规范化的方法对处理受欢迎程度偏差更有效，同时检验了各种新的和现有的指标来衡量推荐中长尾商品的覆盖率。