  Diversity is an important factor in providing high-quality personalized news
recommendations. However, most existing news recommendation methods only aim to
optimize recommendation accuracy while ignoring diversity. Reranking is a
widely used post-processing technique to promote the diversity of top
recommendation results. However, the recommendation model is not perfect and
errors may be propagated and amplified in a cascaded recommendation algorithm.
In addition, the recommendation model itself is not diversity-aware, making it
difficult to achieve a good tradeoff between recommendation accuracy and
diversity. In this paper, we propose a news recommendation approach named
LeaDivRec, which is a fully learnable model that can generate diversity-aware
news recommendations in an end-to-end manner. Different from existing news
recommendation methods that are usually based on point- or pair-wise ranking,
in LeaDivRec we propose a more effective list-wise news recommendation model.
More specifically, we propose a permutation Transformer to consider the
relatedness between candidate news and meanwhile can learn different
representations for similar candidate news to help improve recommendation
diversity. We also propose an effective list-wise training method to learn
accurate ranking models. In addition, we propose a diversity-aware
regularization method to further encourage the model to make controllable
diversity-aware recommendations. Extensive experiments on two real-world
datasets validate the effectiveness of our approach in balancing recommendation
accuracy and diversity.
